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Mycobacterium tuberculosis and Clostridium difficille interactomes: demonstration of rapid development of computational system for bacterial interactome prediction
Background Protein-protein interaction (PPI) networks (interactomes) of most organisms, except for some model organisms, are largely unknown. Experimental methods including high-throughput techniques are highly resource intensive. Therefore, computational discovery of PPIs can accelerate biological discovery by presenting "most-promising" pairs of proteins that are likely to interact. For many bacteria, genome sequence, and thereby genomic context of proteomes, is readily available; additionally, for some of these proteomes, localization and functional annotations are also available, but interactomes are not available. We present here a method for rapid development of computational system to predict interactome of bacterial proteomes. While other studies have presented methods to transfer interologs across species, here, we propose transfer of computational models to benefit from cross-species annotations, thereby predicting many more novel interactions even in the absence of interologs. Mycobacterium tuberculosis (Mtb) and Clostridium difficile (CD) have been used to demonstrate the work. Results We developed a random forest classifier over features derived from Gene Ontology annotations and genetic context scores provided by STRING database for predicting Mtb and CD interactions independently. The Mtb classifier gave a precision of 94% and a recall of 23% on a held out test set. The Mtb model was then run on all the 8 million protein pairs of the Mtb proteome, resulting in 708 new interactions (at 94% expected precision) or 1,595 new interactions at 80% expected precision. The CD classifier gave a precision of 90% and a recall of 16% on a held out test set. The CD model was run on all the 8 million protein pairs of the CD proteome, resulting in 143 new interactions (at 90% expected precision) or 580 new interactions (at 80% expected precision). We also compared the overlap of predictions of our method with STRING database interactions for CD and Mtb and also with interactions identified recently by a bacterial 2-hybrid system for Mtb. To demonstrate the utility of transfer of computational models, we made use of the developed Mtb model and used it to predict CD protein-pairs. The cross species model thus developed yielded a precision of 88% at a recall of 8%. To demonstrate transfer of features from other organisms in the absence of feature-based and interaction-based information, we transferred missing feature values from Mtb orthologs into the CD data. In transferring this data from orthologs (not interologs), we showed that a large number of interactions can be predicted. Conclusions Rapid discovery of (partial) bacterial interactome can be made by using existing set of GO and STRING features associated with the organisms. We can make use of cross-species interactome development, when there are not even sufficient known interactions to develop a computational prediction system. Computational model of well-studied organism(s) can be employed to make the initial interactome prediction for the target organism. We have also demonstrated successfully, that annotations can be transferred from orthologs in well-studied organisms enabling accurate predictions for organisms with no annotations. These approaches can serve as building blocks to address the challenges associated with feature coverage, missing interactions towards rapid interactome discovery for bacterial organisms. Availability The predictions for all Mtb and CD proteins are made available at: http://severus.dbmi.pitt.edu/TB and http://severus.dbmi.pitt.edu/CD respectively for browsing as well as for download.
The Predicted Arabidopsis Interactome Resource and Network Topology-Based Systems Biology Analyses[W][OA
Predicted interactions are a valuable complement to experimentally reported interactions in molecular mechanism studies, particularly for higher organisms, for which reported experimental interactions represent only a small fraction of their total interactomes. With careful engineering consideration of the lessons from previous efforts, the Predicted Arabidopsis Interactome Resource (PAIR; ) presents 149,900 potential molecular interactions, which are expected to cover ~24% of the entire interactome with ~40% precision. This study demonstrates that, although PAIR still has limited coverage, it is rich enough to capture many significant functional linkages within and between higher-order biological systems, such as pathways and biological processes. These inferred interactions can nicely power several network topology-based systems biology analyses, such as gene set linkage analysis, protein function prediction, and identification of regulatory genes demonstrating insignificant expression changes. The drastically expanded molecular network in PAIR has considerably improved the capability of these analyses to integrate existing knowledge and suggest novel insights into the function and coordination of genes and gene networks.
Lin, Mingzhi; Zhou, Xi; Shen, Xueling; Mao, Chuanzao; Chen, Xin
Prediction and comparison of Salmonella-human and Salmonella-Arabidopsis interactomes
Salmonellosis caused by Salmonella bacteria is a food-borne disease and worldwide health threat causing millions of infections and thousands of deaths every year. This pathogen infects an usually broad range of host organisms including human and plants. A better understanding of the mechanisms of communication between Salmonella and its hosts requires identifying the interactions between Salmonella and host proteins. Protein-protein interactions (PPIs) are the fundamental building blocks of communication. Here we utilize the prediction platform BIANA to obtain the putative Salmonella-human and Salmonella-Arabidopsis interactomes based on sequence and domain similarity to known PPIs. A gold standard list of Salmonella-host PPIs served to validate the quality of the human model. 24,726 and 10,926 PPIs comprising interactions between 38 and 33 Salmonella effectors and virulence factors with 9,740 human and 4,676 Arabidopsis proteins, respectively, were predicted. Putative hub proteins could be identified and parallels between the two interactomes were discovered. This approach can provide insight into possible biological functions of so far uncharacterized proteins. The predicted interactions are available via a web interface which allows filtering of the database according to parameters provided by the user to narrow down the list of suspected interactions. The interactions are available via a webinterface at http://sbi.imim.es/web/SHIPREC.php
Schleker, Sylvia; Garcia-Garcia, Javier
Interactome-wide prediction of protein-protein binding sites reveals effects of protein sequence variation in Arabidopsis thaliana.
The specificity of protein-protein interactions is encoded in those parts of the sequence that compose the binding interface. Therefore, understanding how changes in protein sequence influence interaction specificity, and possibly the phenotype, requires knowing the location of binding sites in those sequences. However, large-scale detection of protein interfaces remains a challenge. Here, we present a sequence- and interactome-based approach to mine interaction motifs from the recently published Arabidopsis thaliana interactome. The resultant proteome-wide predictions are available via www.ab.wur.nl/sliderbio and set the stage for further investigations of protein-protein binding sites. To assess our method, we first show that, by using a priori information calculated from protein sequences, such as evolutionary conservation and residue surface accessibility, we improve the performance of interface prediction compared to using only interactome data. Next, we present evidence for the functional importance of the predicted sites, which are under stronger selective pressure than the rest of protein sequence. We also observe a tendency for compensatory mutations in the binding sites of interacting proteins. Subsequently, we interrogated the interactome data to formulate testable hypotheses for the molecular mechanisms underlying effects of protein sequence mutations. Examples include proteins relevant for various developmental processes. Finally, we observed, by analysing pairs of paralogs, a correlation between functional divergence and sequence divergence in interaction sites. This analysis suggests that large-scale prediction of binding sites can cast light on evolutionary processes that shape protein-protein interaction networks. PMID:23077539
Leal Valentim, Felipe; Neven, Frank; Boyen, Peter; van Dijk, Aalt D J
Interactome of Radiation-Induced microRNA-Predicted Target Genes
The microRNAs (miRNAs) function as global negative regulators of gene expression and have been associated with a multitude of biological processes. The dysfunction of the microRNAome has been linked to various diseases including cancer. Our laboratory recently reported modulation in the expression of miRNA in a variety of cell types exposed to ionizing radiation (IR). To further understand miRNA role in IR-induced stress pathways, we catalogued a set of common miRNAs modulated in various irradiated cell lines and generated a list of predicted target genes. Using advanced bioinformatics tools we identified cellular pathways where miRNA predicted target genes function. The miRNA-targeted genes were found to play key roles in previously identified IR stress pathways such as cell cycle, p53 pathway, TGF-beta pathway, ubiquitin-mediated proteolysis, focal adhesion pathway, MAPK signaling, thyroid cancer pathway, adherens junction, insulin signaling pathway, oocyte meiosis, regulation of actin cytoskeleton, and renal cell carcinoma pathway. Interestingly, we were able to identify novel targeted pathways that have not been identified in cellular radiation response, such as aldosterone-regulated sodium reabsorption, long-term potentiation, and neutrotrophin signaling pathways. Our analysis indicates that the miRNA interactome in irradiated cells provides a platform for comprehensive modeling of the cellular stress response to IR exposure.
Lhakhang, Tenzin W.; Chaudhry, M. Ahmad
DDI-CPI, a server that predicts drug-drug interactions through implementing the chemical-protein interactome.
Drug-drug interactions (DDIs) may cause serious side-effects that draw great attention from both academia and industry. Since some DDIs are mediated by unexpected drug-human protein interactions, it is reasonable to analyze the chemical-protein interactome (CPI) profiles of the drugs to predict their DDIs. Here we introduce the DDI-CPI server, which can make real-time DDI predictions based only on molecular structure. When the user submits a molecule, the server will dock user's molecule across 611 human proteins, generating a CPI profile that can be used as a feature vector for the pre-constructed prediction model. It can suggest potential DDIs between the user's molecule and our library of 2515 drug molecules. In cross-validation and independent validation, the server achieved an AUC greater than 0.85. Additionally, by investigating the CPI profiles of predicted DDI, users can explore the PK/PD proteins that might be involved in a particular DDI. A 3D visualization of the drug-protein interaction will be provided as well. The DDI-CPI is freely accessible at http://cpi.bio-x.cn/ddi/. PMID:24875476
Luo, Heng; Zhang, Ping; Huang, Hui; Huang, Jialiang; Kao, Emily; Shi, Leming; He, Lin; Yang, Lun
Prediction and analysis of the protein interactome in Pseudomonas aeruginosa to enable network-based drug target selection.
Pseudomonas aeruginosa (PA) is a ubiquitous opportunistic pathogen that is capable of causing highly problematic, chronic infections in cystic fibrosis and chronic obstructive pulmonary disease patients. With the increased prevalence of multi-drug resistant PA, the conventional "one gene, one drug, one disease" paradigm is losing effectiveness. Network pharmacology, on the other hand, may hold the promise of discovering new drug targets to treat a variety of PA infections. However, given the urgent need for novel drug target discovery, a PA protein-protein interaction (PPI) network of high accuracy and coverage, has not yet been constructed. In this study, we predicted a genome-scale PPI network of PA by integrating various genomic features of PA proteins/genes by a machine learning-based approach. A total of 54,107 interactions covering 4,181 proteins in PA were predicted. A high-confidence network combining predicted high-confidence interactions, a reference set and verified interactions that consist of 3,343 proteins and 19,416 potential interactions was further assembled and analyzed. The predicted interactome network from this study is the first large-scale PPI network in PA with significant coverage and high accuracy. Subsequent analysis, including validations based on existing small-scale PPI data and the network structure comparison with other model organisms, shows the validity of the predicted PPI network. Potential drug targets were identified and prioritized based on their essentiality and topological importance in the high-confidence network. Host-pathogen protein interactions between human and PA were further extracted and analyzed. In addition, case studies were performed on protein interactions regarding anti-sigma factor MucA, negative periplasmic alginate regulator MucB, and the transcriptional regulator RhlR. A web server to access the predicted PPI dataset is available at http://research.cchmc.org/PPIdatabase/. PMID:22848443
Zhang, Minlu; Su, Shengchang; Bhatnagar, Raj K; Hassett, Daniel J; Lu, Long J
Prediction of Spatiotemporal Patterns of Neural Activity from Pairwise Correlations
We designed a model-based analysis to predict the occurrence of population patterns in distributed spiking activity. Using a maximum entropy principle with a Markovian assumption, we obtain a model that accounts for both spatial and temporal pairwise correlations among neurons. This model is tested on data generated with a Glauber spin-glass system and is shown to correctly predict the occurrence probabilities of spatiotemporal patterns significantly better than Ising models only based on spatial correlations. This increase of predictability was also observed on experimental data recorded in parietal cortex during slow-wave sleep. This approach can also be used to generate surrogates that reproduce the spatial and temporal correlations of a given data set.
Marre, O.; El Boustani, S.; Fregnac, Y.; Destexhe, A. [Unite de Neurosciences Integratives et Computationnelles (UNIC), UPR CNRS 2191, Gif-sur-Yvette (France)
Prediction of Spatiotemporal Patterns of Neural Activity from Pairwise Correlations
NASA Astrophysics Data System (ADS)
We designed a model-based analysis to predict the occurrence of population patterns in distributed spiking activity. Using a maximum entropy principle with a Markovian assumption, we obtain a model that accounts for both spatial and temporal pairwise correlations among neurons. This model is tested on data generated with a Glauber spin-glass system and is shown to correctly predict the occurrence probabilities of spatiotemporal patterns significantly better than Ising models only based on spatial correlations. This increase of predictability was also observed on experimental data recorded in parietal cortex during slow-wave sleep. This approach can also be used to generate surrogates that reproduce the spatial and temporal correlations of a given data set.
Marre, O.; El Boustani, S.; Fr�gnac, Y.; Destexhe, A.
Functional Integrative Levels in the Human Interactome Recapitulate Organ Organization
Interactome networks represent sets of possible physical interactions between proteins. They lack spatio-temporal information by construction. However, the specialized functions of the differentiated cell types which are assembled into tissues or organs depend on the combinatorial arrangements of proteins and their physical interactions. Is tissue-specificity, therefore, encoded within the interactome? In order to address this question, we combined protein-protein interactions, expression data, functional annotations and interactome topology. We first identified a subnetwork formed exclusively of proteins whose interactions were observed in all tested tissues. These are mainly involved in housekeeping functions and are located at the topological center of the interactome. This �Largest Common Interactome Network� represents a �functional interactome core�. Interestingly, two types of tissue-specific interactions are distinguished when considering function and network topology: tissue-specific interactions involved in regulatory and developmental functions are central whereas tissue-specific interactions involved in organ physiological functions are peripheral. Overall, the functional organization of the human interactome reflects several integrative levels of functions with housekeeping and regulatory tissue-specific functions at the center and physiological tissue-specific functions at the periphery. This gradient of functions recapitulates the organization of organs, from cells to organs. Given that several gradients have already been identified across interactomes, we propose that gradients may represent a general principle of protein-protein interaction network organization.
Prieto, Carlos; Benkahla, Alia; De Las Rivas, Javier; Brun, Christine
Virtual Interactomics of Proteins from Biochemical Standpoint
Virtual interactomics represents a rapidly developing scientific area on the boundary line of bioinformatics and interactomics. Protein-related virtual interactomics then comprises instrumental tools for prediction, simulation, and networking of the majority of interactions important for structural and individual reproduction, differentiation, recognition, signaling, regulation, and metabolic pathways of cells and organisms. Here, we describe the main areas of virtual protein interactomics, that is, structurally based comparative analysis and prediction of functionally important interacting sites, mimotope-assisted and combined epitope prediction, molecular (protein) docking studies, and investigation of protein interaction networks. Detailed information about some interesting methodological approaches and online accessible programs or databases is displayed in our tables. Considerable part of the text deals with the searches for common conserved or functionally convergent protein regions and subgraphs of conserved interaction networks, new outstanding trends and clinically interesting results. In agreement with the presented data and relationships, virtual interactomic tools improve our scientific knowledge, help us to formulate working hypotheses, and they frequently also mediate variously important in silico simulations.
Kubrycht, Jaroslav; Sigler, Karel; Soucek, Pavel
Ozone Concentration Prediction via Spatiotemporal Autoregressive Model With Exogenous Variables
NASA Astrophysics Data System (ADS)
Forecast of environmental variables are nowadays of main concern for public health or agricultural management. In this context a large literature is devoted to spatio-temporal modelling of these variables using different statistical approaches. However, most of studies ignored the potential contribution of local (e.g. meteorological and/or geographical) covariables as well as the dynamical characteristics of observations. In this study, we present a spatiotemporal short term forecasting model for ozone concentration based on regularly observed covariables in predefined geographical sites. Our driving system simply combines a multidimensional second order autoregressive structured process with a linear regression model over influent exogenous factors and reads as follows: ?2 ?q j Z (t) = A (�&,cedil;D )�- [ ?iZ(t- i)]+ B (�&,cedil;D )�- [ ?jX (t)]+ �?(t) i=1 j=1 Z(t)=(Z1(t),�?�,Zn(t)) represents the vector of ozone concentration at time t of the n geographical sites, whereas Xj(t)=(X1j(t),�?�,Xnj(t)) denotes the jth exogenous variable observed over these sites. The nxn matrix functions A and B account for the spatial relationships between sites through the inter site distance matrix D and a vector parameter �&.cedil; Multidimensional white noise �? is assumed to be Gaussian and spatially correlated but temporally independent. A covariance structure of Z that takes account of noise spatial dependences is deduced under a stationary hypothesis and then included in the likelihood function. Statistical model and estimation procedure: Contrarily to the widely used choice of a {0,1}-valued neighbour matrix A, we put forward two more natural choices of exponential or power decay. Moreover, the model revealed enough stable to readily accommodate the crude observations without the usual tedious and somewhat arbitrarily variable transformations. Data set and preliminary analysis: In our case, ozone variable represents here the daily maximum ozone concentration recorded in n=42 stations during the year 2005 within a south region in France, covering an area of approximately 10565 km2. Meteorological covariates are the daily maxima of temperature, wind speed, daily maxima of humidity and atmospheric pressure. Actually, the meteorological factors are not recorded in ozone monitoring sites and thus preliminary interpolation techniques were used and compared subsequently (Gaussian conditional simulation, ordinary kriging or kriging with external drift). Concluding remarks: From the statistical point of view, both simulation study and data analysis showed a fairly robust behaviour of estimation procedures. In both cases, the analysis of residuals proved a significant improvement of error prediction within this framework. From the environmental point of view, the ability of accounting for pertinent local and dynamical meteorological covariates clearly provided a useful tool in prediction methods. Bib [1]: Pfeifer.P.E; Deutsh.S.J. (1980) "A Three-Stage Iterative Procedure for Space-Time Modelling." Technometrics 22: 35-47. Bib [2]: Raffaella Giacomini and Cliff W.J.Granger 2002 - 07 "Aggregation of Space-Time Process" Departement of Economics, University of California, San Diego.
Kamoun, W.; Senoussi, R.
Ozone Concentration Prediction via Spatiotemporal Autoregressive Model With Exogenous Variables
Forecast of environmental variables are nowadays of main concern for public health or agricultural management. In this context a large literature is devoted to spatio-temporal modelling of these variables using different statistical approaches. However, most of studies ignored the potential contribution of local (e.g. meteorological and\\/or geographical) covariables as well as the dynamical characteristics of observations. In this study, we
W. Kamoun; R. Senoussi
SePreSA: a server for the prediction of populations susceptible to serious adverse drug reactions implementing the methodology of a chemical-protein interactome.
Serious adverse drug reactions (SADRs) are caused by unexpected drug-human protein interactions, and some polymorphisms within binding pockets make the population carrying these polymorphisms susceptible to SADR. Predicting which populations are likely to be susceptible to SADR will not only strengthen drug safety, but will also assist enterprises to adjust R&D and marketing strategies. Making such predictions has recently been facilitated by the introduction of a web server named SePreSA. The server has a comprehensive collection of the structural models of nearly all the well known SADR targets. Once a drug molecule is submitted, the scale of its potential interaction with multi-SADR targets is calculated using the DOCK program. The server utilizes a 2-directional Z-transformation scoring algorithm, which computes the relative drug-protein interaction strength based on the docking-score matrix of a chemical-protein interactome, thus achieve greater accuracy in prioritizing SADR targets than simply using dock scoring functions. The server also suggests the binding pattern of the lowest docking score through 3D visualization, by highlighting and visualizing amino acid residues involved in the binding on the customer's browser. Polymorphism information for different populations for each of the interactive residues will be displayed, helping users to deduce the population-specific susceptibility of their drug molecule. The server is freely available at http://SePreSA.Bio-X.cn/. PMID:19417066
Yang, Lun; Luo, Heng; Chen, Jian; Xing, Qinghe; He, Lin
Interactome Networks and Human Disease
Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here we review basic concepts in network biology, discussing different types of interactome networks and the insights that can come from analyzing them. We elaborate on why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease.
Vidal, Marc; Cusick, Michael E.; Barabasi, Albert-Laszlo
Retrospective prediction of intraurban spatiotemporal distribution of PM2.5 in Taipei
NASA Astrophysics Data System (ADS)
Numerous studies have shown that fine airborne particulate matter particles (PM2.5) are more dangerous to human health than coarse particles, e.g. PM10. The assessment of the impacts to human health or ecological effects by long-term PM2.5 exposure is often limited by lack of PM2.5 measurements. In Taipei, PM2.5 was not systematically observed until August, 2005. Taipei is the largest metropolitan area in Taiwan, where a variety of industrial and traffic emissions are continuously generated and distributed across space and time. PM-related data, i.e., PM10 and Total Suspended Particles (TSP) are independently systematically collected by different central and local government institutes. In this study, the retrospective prediction of spatiotemporal distribution of monthly PM2.5 over Taipei will be performed by using Bayesian Maximum Entropy method (BME) to integrate (a) the spatiotemporal dependence among PM measurements (i.e. PM10, TSP, and PM2.5), (b) the site-specific information of PM measurements which can be certain or uncertain information, and (c) empirical evidence about the PM2.5/PM10 and PM10/TSP ratios. The performance assessment of the retrospective prediction for the spatiotemporal distribution of PM2.5 was performed over space and time during 2003-2004 by comparing the posterior pdf of PM2.5 with the observations. Results show that the incorporation of PM10 and TSP observations by BME method can effectively improve the spatiotemporal PM2.5 estimation in the sense of lower mean and standard deviation of estimation errors. Moreover, the spatiotemporal retrospective prediction with PM2.5/PM10 and PM2.5/TSP ratios can provide good estimations of the range of PM2.5 levels over space and time during 2003-2004 in Taipei.
Hwa-Lung, Yu; Chih-Hsin, Wang
A data mining based approach to predict spatiotemporal changes in satellite images
NASA Astrophysics Data System (ADS)
The interpretation of remotely sensed images in a spatiotemporal context is becoming a valuable research topic. However, the constant growth of data volume in remote sensing imaging makes reaching conclusions based on collected data a challenging task. Recently, data mining appears to be a promising research field leading to several interesting discoveries in various areas such as marketing, surveillance, fraud detection and scientific discovery. By integrating data mining and image interpretation techniques, accurate and relevant information (i.e. functional relation between observed parcels and a set of informational contents) can be automatically elicited. This study presents a new approach to predict spatiotemporal changes in satellite image databases. The proposed method exploits fuzzy sets and data mining concepts to build predictions and decisions for several remote sensing fields. It takes into account imperfections related to the spatiotemporal mining process in order to provide more accurate and reliable information about land cover changes in satellite images. The proposed approach is validated using SPOT images representing the Saint-Denis region, capital of Reunion Island. Results show good performances of the proposed framework in predicting change for the urban zone.
Boulila, W.; Farah, I. R.; Ettabaa, K. Saheb; Solaiman, B.; Gh�zala, H. Ben
From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks
Growth and remodelling impact the network topology of complex systems, yet a general theory explaining how new links arise between existing nodes has been lacking, and little is known about the topological properties that facilitate link-prediction. Here we investigate the extent to which the connectivity evolution of a network might be predicted by mere topological features. We show how a link/community-based strategy triggers substantial prediction improvements because it accounts for the singular topology of several real networks organised in multiple local communities - a tendency here named local-community-paradigm (LCP). We observe that LCP networks are mainly formed by weak interactions and characterise heterogeneous and dynamic systems that use self-organisation as a major adaptation strategy. These systems seem designed for global delivery of information and processing via multiple local modules. Conversely, non-LCP networks have steady architectures formed by strong interactions, and seem designed for systems in which information/energy storage is crucial.
Cannistraci, Carlo Vittorio; Alanis-Lobato, Gregorio; Ravasi, Timothy
Learned spatiotemporal sequence recognition and prediction in primary visual cortex.
Learning to recognize and predict temporal sequences is fundamental to sensory perception and is impaired in several neuropsychiatric disorders, but little is known about where and how this occurs in the brain. We discovered that repeated presentations of a visual sequence over a course of days resulted in evoked response potentiation in mouse V1 that was highly specific for stimulus order and timing. Notably, after V1 was trained to recognize a sequence, cortical activity regenerated the full sequence even when individual stimulus elements were omitted. Our results advance the understanding of how the brain makes 'intelligent guesses' on the basis of limited information to form visual percepts and suggest that it is possible to study the mechanistic basis of this high-level cognitive ability by studying low-level sensory systems. PMID:24657967
Gavornik, Jeffrey P; Bear, Mark F
Assessing protein co-evolution in the context of the tree of life assists in the prediction of the interactome.
The identification of the whole set of protein interactions taking place in an organism is one of the main tasks in genomics, proteomics and systems biology. One of the computational techniques used by many investigators for studying and predicting protein interactions is the comparison of evolutionary histories (phylogenetic trees), under the hypothesis that interacting proteins would be subject to a similar evolutionary pressure resulting in a similar topology of the corresponding trees. Here, we present a new approach to predict protein interactions from phylogenetic trees, which incorporates information on the overall evolutionary histories of the species (i.e. the canonical "tree of life") in order to correct by the expected background similarity due to the underlying speciation events. We test the new approach in the largest set of annotated interacting proteins for Escherichia coli. This assessment of co-evolution in the context of the tree of life leads to a highly significant improvement (P(N) by sign test approximately 10E-6) in predicting interaction partners with respect to the previous technique, which does not incorporate information on the overall speciation tree. For half of the proteins we found a real interactor among the 6.4% top scores, compared with the 16.5% by the previous method. We applied the new method to the whole E.coli proteome and propose functions for some hypothetical proteins based on their predicted interactors. The new approach allows us also to detect non-canonical evolutionary events, in particular horizontal gene transfers. We also show that taking into account these non-canonical evolutionary events when assessing the similarity between evolutionary trees improves the performance of the method predicting interactions. PMID:16139301
Pazos, Florencio; Ranea, Juan A G; Juan, David; Sternberg, Michael J E
The current Salmonella-host interactome
Salmonella bacteria cause millions of infections and thousands of deaths every year. This pathogen has an unusually broad host range including humans, animals, and even plants. During infection, Salmonella expresses a variety of virulence factors and effectors that are delivered into the host cell triggering cellular responses through protein�protein interactions (PPIs) with host cell proteins which make the pathogen�s invasion and replication possible. To speed up proteomic efforts in elucidating Salmonella�host interactomes, we carried out a survey of the currently published Salmonella�host PPI. Such a list can serve as the gold standard for computational models aimed at predicting Salmonella�host interactomes through integration of large-scale biological data sources. Manual literature and database search of >2200 journal articles and >100 databases resulted in a gold standard list of currently 62 PPI, including primarily interactions of Salmonella proteins with human and mouse proteins. Only six of these interactions were directly retrievable from PPI databases and 16 were highlighted in databases featuring literature extracts. Thus, the literature survey resulted in the most complete interactome available to date for Salmonella. Pathway analysis using Ingenuity and Broad Gene Set Enrichment Analysis (GSEA) software revealed among general pathways such as MAPK signaling in particular those related to cell death as well as cell morphology, turnover, and interactions, in addition to response to not only Salmonella but also other pathogenic � viral and bacterial � infections. The list of interactions is available at http://www.shiprec.org/indicationslist.htm
Schleker, Sylvia; Sun, Jingchun; Raghavan, Balachandran; Srnec, Matthew; Muller, Nicole; Koepfinger, Mary; Murthy, Leelavati; Zhao, Zhongming; Klein-Seetharaman, Judith
Predictability of spatio-temporal patterns in a lattice of coupled FitzHugh-Nagumo oscillators
In many biological systems, variability of the components can be expected to outrank statistical fluctuations in the shaping of self-organized patterns. In pioneering work in the late 1990s, it was hypothesized that a drift of cellular parameters (along a �developmental path�), together with differences in cell properties (�desynchronization� of cells on the developmental path) can establish self-organized spatio-temporal patterns (in their example, spiral waves of cAMP in a colony of Dictyostelium discoideum cells) starting from a homogeneous state. Here, we embed a generic model of an excitable medium, a lattice of diffusively coupled FitzHugh�Nagumo oscillators, into a developmental-path framework. In this minimal model of spiral wave generation, we can now study the predictability of spatio-temporal patterns from cell properties as a function of desynchronization (or �spread�) of cells along the developmental path and the drift speed of cell properties on the path. As a function of drift speed and desynchronization, we observe systematically different routes towards fully established patterns, as well as strikingly different correlations between cell properties and pattern features. We show that the predictability of spatio-temporal patterns from cell properties contains important information on the pattern formation process as well as on the underlying dynamical system.
Grace, Miriam; Hutt, Marc-Thorsten
Meteorological Factors-Based Spatio-Temporal Mapping and Predicting Malaria in Central China
Despite significant reductions in the overall burden of malaria in the 20th century, this disease still represents a significant public health problem in China, especially in central areas. Understanding the spatio-temporal distribution of malaria is essential in the planning and implementing of effective control measures. In this study, normalized meteorological factors were incorporated in spatio-temporal models. Seven models were established in WinBUGS software by using Bayesian hierarchical models and Markov Chain Monte Carlo methods. M1, M2, and M3 modeled separate meteorological factors, and M3, which modeled rainfall performed better than M1 and M2, which modeled average temperature and relative humidity, respectively. M7 was the best fitting models on the basis of based on deviance information criterion and predicting errors. The results showed that the way rainfall influencing malaria incidence was different from other factors, which could be interpreted as rainfall having a greater influence than other factors.
Huang, Fang; Zhou, Shuisen; Zhang, Shaosen; Zhang, Hongwei; Li, Weidong
Spatio-temporal variation and prediction of ischemic heart disease hospitalizations in Shenzhen, China.
Ischemic heart disease (IHD) is a leading cause of death worldwide. Urban public health and medical management in Shenzhen, an international city in the developing country of China, is challenged by an increasing burden of IHD. This study analyzed the spatio-temporal variation of IHD hospital admissions from 2003 to 2012 utilizing spatial statistics, spatial analysis, and space-time scan statistics. The spatial statistics and spatial analysis measured the incidence rate (hospital admissions per 1,000 residents) and the standardized rate (the observed cases standardized by the expected cases) of IHD at the district level to determine the spatio-temporal distribution and identify patterns of change. The space-time scan statistics was used to identify spatio-temporal clusters of IHD hospital admissions at the district level. The other objective of this study was to forecast the IHD hospital admissions over the next three years (2013-2015) to predict the IHD incidence rates and the varying burdens of IHD-related medical services among the districts in Shenzhen. The results show that the highest hospital admissions, incidence rates, and standardized rates of IHD are in Futian. From 2003 to 2012, the IHD hospital admissions exhibited similar mean centers and directional distributions, with a slight increase in admissions toward the north in accordance with the movement of the total population. The incidence rates of IHD exhibited a gradual increase from 2003 to 2012 for all districts in Shenzhen, which may be the result of the rapid development of the economy and the increasing traffic pollution. In addition, some neighboring areas exhibited similar temporal change patterns, which were also detected by the spatio-temporal cluster analysis. Futian and Dapeng would have the highest and the lowest hospital admissions, respectively, although these districts have the highest incidence rates among all of the districts from 2013 to 2015 based on the prediction using the GM (1,1). In addition, the combined analysis of the prediction of IHD hospital admissions and the general hospital distributions shows that Pingshan and Longgang might experience the most serious burden of IHD hospital services in the near future, although Futian would still have the greatest number and the highest incidence rate of hospital admissions for IHD. PMID:24806191
Wang, Yanxia; Du, Qingyun; Ren, Fu; Liang, Shi; Lin, De-nan; Tian, Qin; Chen, Yan; Li, Jia-jia
Spatio-Temporal Variation and Prediction of Ischemic Heart Disease Hospitalizations in Shenzhen, China
Ischemic heart disease (IHD) is a leading cause of death worldwide. Urban public health and medical management in Shenzhen, an international city in the developing country of China, is challenged by an increasing burden of IHD. This study analyzed the spatio-temporal variation of IHD hospital admissions from 2003 to 2012 utilizing spatial statistics, spatial analysis, and space-time scan statistics. The spatial statistics and spatial analysis measured the incidence rate (hospital admissions per 1,000 residents) and the standardized rate (the observed cases standardized by the expected cases) of IHD at the district level to determine the spatio-temporal distribution and identify patterns of change. The space-time scan statistics was used to identify spatio-temporal clusters of IHD hospital admissions at the district level. The other objective of this study was to forecast the IHD hospital admissions over the next three years (2013�2015) to predict the IHD incidence rates and the varying burdens of IHD-related medical services among the districts in Shenzhen. The results show that the highest hospital admissions, incidence rates, and standardized rates of IHD are in Futian. From 2003 to 2012, the IHD hospital admissions exhibited similar mean centers and directional distributions, with a slight increase in admissions toward the north in accordance with the movement of the total population. The incidence rates of IHD exhibited a gradual increase from 2003 to 2012 for all districts in Shenzhen, which may be the result of the rapid development of the economy and the increasing traffic pollution. In addition, some neighboring areas exhibited similar temporal change patterns, which were also detected by the spatio-temporal cluster analysis. Futian and Dapeng would have the highest and the lowest hospital admissions, respectively, although these districts have the highest incidence rates among all of the districts from 2013 to 2015 based on the prediction using the GM (1,1). In addition, the combined analysis of the prediction of IHD hospital admissions and the general hospital distributions shows that Pingshan and Longgang might experience the most serious burden of IHD hospital services in the near future, although Futian would still have the greatest number and the highest incidence rate of hospital admissions for IHD.
Wang, Yanxia; Du, Qingyun; Ren, Fu; Liang, Shi; Lin, De-nan; Tian, Qin; Chen, Yan; Li, Jia-jia
Predicting Intra-Urban Variation in Air Pollution Concentrations with Complex Spatio-Temporal Dependencies
We describe a methodology for assigning individual estimates of long-term average air pollution concentrations that accounts for a complex spatio-temporal correlation structure and can accommodate spatio-temporally misaligned observations. This methodology has been developed as part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air), a prospective cohort study funded by the U.S. EPA to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. Our hierarchical model decomposes the space-time field into a �mean� that includes dependence on covariates and spatially varying seasonal and long-term trends and a �residual� that accounts for spatially correlated deviations from the mean model. The model accommodates complex spatio-temporal patterns by characterizing the temporal trend at each location as a linear combination of empirically derived temporal basis functions, and embedding the spatial fields of coefficients for the basis functions in separate linear regression models with spatially correlated residuals (universal kriging). This approach allows us to implement a scalable single-stage estimation procedure that easily accommodates a significant number of missing observations at some monitoring locations. We apply the model to predict long-term average concentrations of oxides of nitrogen (NOx) from 2005�2007 in the Los Angeles area, based on data from 18 EPA Air Quality System regulatory monitors. The cross-validated R2 is 0.67. The MESA Air study is also collecting additional concentration data as part of a supplementary monitoring campaign. We describe the sampling plan and demonstrate in a simulation study that the additional data will contribute to improved predictions of long-term average concentrations.
Szpiro, Adam A.; Sampson, Paul D.; Sheppard, Lianne; Lumley, Thomas; Adar, Sara D.; Kaufman, Joel
A climate-based spatiotemporal prediction for dengue fever epidemics: a case study in southern Taiwan
NASA Astrophysics Data System (ADS)
Dengue Fever (DF) has been identified by the World Health organization (WHO) as one of the most serious vector-borne infectious diseases in tropical and sub-tropical areas. DF has been one of the most important epidemics in Taiwan which occur annually especially in southern Taiwan during summer and autumn. Most DF studies have focused mainly on temporal DF patterns and its close association with climatic covariates, whereas few studies have investigated the spatial DF patterns (spatial dependence and clustering) and composite space-time effects of the DF epidemics. The present study proposes a spatio-temporal DF prediction approach based on stochastic Bayesian Maximum Entropy (BME) analysis. Core and site-specific knowledge bases are considered, including climate and health datasets under conditions of uncertainty, space-time dependence functions, and a Poisson regression model of climatic variables contributing to DF occurrences in southern Taiwan during 2007, when the highest number of DF cases was recorded in the history of Taiwan epidemics (over 2000). The obtained results show that the DF outbreaks in the study area are highly influenced by climatic conditions. Furthermore, the analysis can provide the required "one-week-ahead" outbreak warnings based on spatio-temporal predictions of DF distributions. Therefore, the proposed analysis can provide the Taiwan Disease Control Agency with a valuable tool to timely identify, control, and even efficiently prevent DF spreading across space-time.
Yu, H.-L.; Yang, S.-J.; Lin, Y.-C.
Manifold learning in protein interactomes.
Many studies and applications in the post-genomic era have been devoted to analyze complex biological systems by computational inference methods. We propose to apply manifold learning methods to protein-protein interaction networks (PPIN). Despite their popularity in data-intensive applications, these methods have received limited attention in the context of biological networks. We show that there is both utility and unexplored potential in adopting manifold learning for network inference purposes. In particular, the following advantages are highlighted: (a) fusion with diagnostic statistical tools designed to assign significance to protein interactions based on pre-selected topological features; (b) dissection into components of the interactome in order to elucidate global and local connectivity organization; (c) relevance of embedding the interactome in reduced dimensions for biological validation purposes. We have compared the performances of three well-known techniques--kernel-PCA, RADICAL ICA, and ISOMAP--relatively to their power of mapping the interactome onto new coordinate dimensions where important associations among proteins can be detected, and then back projected such that the corresponding sub-interactomes are reconstructed. This recovery has been done selectively, by using significant information according to a robust statistical procedure, and then standard biological annotation has been provided to validate the results. We expect that a byproduct of using subspace analysis by the proposed techniques is a possible calibration of interactome modularity studies. Supplementary Material is available online at www.libertonlinec.com. PMID:20666618
Marras, Elisabetta; Travaglione, Antonella; Capobianco, Enrico
Predicted spatio-temporal dynamics of radiocesium deposited onto forests following the Fukushima nuclear accident.
The majority of the area contaminated by the Fukushima Dai-ichi nuclear power plant accident is covered by forest. To facilitate effective countermeasure strategies to mitigate forest contamination, we simulated the spatio-temporal dynamics of radiocesium deposited into Japanese forest ecosystems in 2011 using a model that was developed after the Chernobyl accident in 1986. The simulation revealed that the radiocesium inventories in tree and soil surface organic layer components drop rapidly during the first two years after the fallout. Over a period of one to two years, the radiocesium is predicted to move from the tree and surface organic soil to the mineral soil, which eventually becomes the largest radiocesium reservoir within forest ecosystems. Although the uncertainty of our simulations should be considered, the results provide a basis for understanding and anticipating the future dynamics of radiocesium in Japanese forests following the Fukushima accident. PMID:23995073
Hashimoto, Shoji; Matsuura, Toshiya; Nanko, Kazuki; Linkov, Igor; Shaw, George; Kaneko, Shinji
Complementing the Eukaryotic Protein Interactome
Protein interaction networks are important for the understanding of regulatory mechanisms, for the explanation of experimental data and for the prediction of protein functions. Unfortunately, most interaction data is available only for model organisms. As a possible remedy, the transfer of interactions to organisms of interest is common practice, but it is not clear when interactions can be transferred from one organism to another and, thus, the confidence in the derived interactions is low. Here, we propose to use a rich set of features to train Random Forests in order to score transferred interactions. We evaluated the transfer from a range of eukaryotic organisms to S. cerevisiae using orthologs. Directly transferred interactions to S. cerevisiae are on average only 24% consistent with the current S. cerevisiae interaction network. By using commonly applied filter approaches the transfer precision can be improved, but at the cost of a large decrease in the number of transferred interactions. Our Random Forest approach uses various features derived from both the target and the source network as well as the ortholog annotations to assign confidence values to transferred interactions. Thereby, we could increase the average transfer consistency to 85%, while still transferring almost 70% of all correctly transferable interactions. We tested our approach for the transfer of interactions to other species and showed that our approach outperforms competing methods for the transfer of interactions to species where no experimental knowledge is available. Finally, we applied our predictor to score transferred interactions to 83 targets species and we were able to extend the available interactome of B. taurus, M. musculus and G. gallus with over 40,000 interactions each. Our transferred interaction networks are publicly available via our web interface, which allows to inspect and download transferred interaction sets of different sizes, for various species, and at specified expected precision levels. Availability: http://services.bio.ifi.lmu.de/coin-db/.
Pesch, Robert; Zimmer, Ralf
Viruses and Interactomes in Translation*
A decade of high-throughput screenings for intraviral and virus-host protein-protein interactions led to the accumulation of data and to the development of theories on laws governing interactome organization for many viruses. We present here a computational analysis of intraviral protein networks (EBV, FLUAV, HCV, HSV-1, KSHV, SARS-CoV, VACV, and VZV) and virus-host protein networks (DENV, EBV, FLUAV, HCV, and VACV) from up-to-date interaction data, using various mathematical approaches. If intraviral networks seem to behave similarly, they are clearly different from the human interactome. Viral proteins target highly central human proteins, which are precisely the Achilles' heel of the human interactome. The intrinsic structural disorder is a distinctive feature of viral hubs in virus-host interactomes. Overlaps between virus-host data sets identify a core of human proteins involved in the cellular response to viral infection and in the viral capacity to hijack the cell machinery for viral replication. Host proteins that are strongly targeted by a virus seem to be particularly attractive for other viruses. Such protein-protein interaction networks and their analysis represent a powerful resource from a therapeutic perspective.
Meyniel-Schicklin, Laurene; de Chassey, Benoit; Andre, Patrice; Lotteau, Vincent
An adaptive mode-driven spatiotemporal motion vector prediction for wavelet video coding
NASA Astrophysics Data System (ADS)
The three-dimensional subband/wavelet codecs use 5/3 filters rather than Haar filters for the motion compensation temporal filtering (MCTF) to improve the coding gain. In order to curb the increased motion vector rate, an adaptive motion mode driven spatiotemporal motion vector prediction (AMDST-MVP) scheme is proposed. First, by making use of the direction histograms of four motion vector fields resulting from the initial spatial motion vector prediction (SMVP), the motion mode of the current GOP is determined according to whether the fast or complex motion exists in the current GOP. Then the GOP-level MVP scheme is thereby determined by either the S-MVP or the AMDST-MVP, namely, AMDST-MVP is the combination of S-MVP and temporal-MVP (T-MVP). If the latter is adopted, the motion vector difference (MVD) between the neighboring MV fields and the S-MVP resulting MV of the current block is employed to decide whether or not the MV of co-located block in the previous frame is used for prediction the current block. Experimental results show that AMDST-MVP not only can improve the coding efficiency but also reduce the number of computation complexity.
Zhao, Fan; Liu, Guizhong; Qi, Yong
A Map of the Interactome Network of the Metazoan C. elegans
To initiate studies on how protein-protein interaction (or �interactome�) networks relate to multicellular functions, we have mapped a large fraction of the Caenorhabditis elegans interactome network. Starting with a subset of metazoan-specific proteins, more than 4000 interactions were identified from high-throughput, yeast two-hybrid (HT=Y2H) screens. Independent coaffinity purification assays experimentally validated the overall quality of this Y2H data set. Together with already described Y2H interactions and interologs predicted in silico, the current version of the Worm Interactome (WI5) map contains ?5500 interactions. Topological and biological features of this interactome network, as well as its integration with phenome and transcriptome data sets, lead to numerous biological hypotheses.
Li, Siming; Armstrong, Christopher M.; Bertin, Nicolas; Ge, Hui; Milstein, Stuart; Boxem, Mike; Vidalain, Pierre-Olivier; Han, Jing-Dong J.; Chesneau, Alban; Hao, Tong; Goldberg, Debra S.; Li, Ning; Martinez, Monica; Rual, Jean-Francois; Lamesch, Philippe; Xu, Lai; Tewari, Muneesh; Wong, Sharyl L.; Zhang, Lan V.; Berriz, Gabriel F.; Jacotot, Laurent; Vaglio, Philippe; Reboul, Jerome; Hirozane-Kishikawa, Tomoko; Li, Qianru; Gabel, Harrison W.; Elewa, Ahmed; Baumgartner, Bridget; Rose, Debra J.; Yu, Haiyuan; Bosak, Stephanie; Sequerra, Reynaldo; Fraser, Andrew; Mango, Susan E.; Saxton, William M.; Strome, Susan; van den Heuvel, Sander; Piano, Fabio; Vandenhaute, Jean; Sardet, Claude; Gerstein, Mark; Doucette-Stamm, Lynn; Gunsalus, Kristin C.; Wade Harper, J.; Cusick, Michael E.; Roth, Frederick P.; Hill, David E.; Vidal, Marc
An online spatio-temporal prediction model for dengue fever epidemic in Kaohsiung,Taiwan
NASA Astrophysics Data System (ADS)
Dengue Fever (DF) is one of the most serious vector-borne infectious diseases in tropical and subtropical areas. DF epidemics occur in Taiwan annually especially during summer and fall seasons. Kaohsiung city has been one of the major DF hotspots in decades. The emergence and re-emergence of the DF epidemic is complex and can be influenced by various factors including space-time dynamics of human and vector populations and virus serotypes as well as the associated uncertainties. This study integrates a stochastic space-time "Susceptible-Infected-Recovered" model under Bayesian maximum entropy framework (BME-SIR) to perform real-time prediction of disease diffusion across space-time. The proposed model is applied for spatiotemporal prediction of the DF epidemic at Kaohsiung city during 2002 when the historical series of high DF cases was recorded. The online prediction by BME-SIR model updates the parameters of SIR model and infected cases across districts over time. Results show that the proposed model is rigorous to initial guess of unknown model parameters, i.e. transmission and recovery rates, which can depend upon the virus serotypes and various human interventions. This study shows that spatial diffusion can be well characterized by BME-SIR model, especially at the district surrounding the disease outbreak locations. The prediction performance at DF hotspots, i.e. Cianjhen and Sanmin, can be degraded due to the implementation of various disease control strategies during the epidemics. The proposed online disease prediction BME-SIR model can provide the governmental agency with a valuable reference to timely identify, control, and efficiently prevent DF spread across space-time.
Cheng, Ming-Hung; Yu, Hwa-Lung; Angulo, Jose; Christakos, George
Spatiotemporal scale limits and roles of biogeochemical cycles in climate predictions
NASA Astrophysics Data System (ADS)
There is much confidence in the global temperature change and its attribution to human activities. Global climate models have attained unprecedented complexity in representing the climate system and its response to external forcings. However, climate prediction remains a serious challenge and carries large uncertainty, particularly when the scale of interest becomes small. With the increasing interest in regional impact studies for decision-making, one of the urgent tasks is to make a systematic, quantitative evaluation of the expected skill of climate models over a range of spatiotemporal scales. The first part of this dissertation was devoted to this task, with focus on the predictive skill in the linear trend of surface air temperature. By evaluating the hindcasts for the last 120 year period in the form of deterministic and probabilistic predictions, it was found that the hindcasts can reproduce broad-scale changes in the surface air temperature, showing reliable skill at spatial scales larger than or equal to a few thousand kilometers (30� x 30�) and at temporal scales of 30 years or longer. However, their skill remains limited at smaller spatiotemporal scales, where we saw no significant improvement over climatology or a random guess. Over longer temporal scales, the feedbacks from the carbon cycle to atmospheric CO2 concentration become important. Therefore the rest of the dissertation attempts to find key processes in the climate-carbon cycle feedback using one of the leading land-climate models, the National Center for Atmospheric Research Community Land Model. Evaluation of site-level simulations using field observations from the Amazon forest revealed that the current formulation for drought-related mortality, which lacks the ecophysiological link between short- and long-term drought stress, prevent the model from simulating realistic forest response. Global simulations showed that such dynamics of vegetation strongly influences the control of the nitrogen cycle on vegetation productivity, which then alters the sensitivity of the terrestrial biosphere to surface air temperature. This implies that if the state of the terrestrial biosphere is inconsistent with the simulated climate, either biased or prescribed, then its feedback to anthropogenic forcing could be also inconsistent.
Sakaguchi, Koichi
Geomagnetic imprinting predicts spatio-temporal variation in homing migration of pink and sockeye salmon.
Animals navigate using a variety of sensory cues, but how each is weighted during different phases of movement (e.g. dispersal, foraging, homing) is controversial. Here, we examine the geomagnetic and olfactory imprinting hypotheses of natal homing with datasets that recorded variation in the migratory routes of sockeye (Oncorhynchus nerka) and pink (Oncorhynchus gorbuscha) salmon returning from the Pacific Ocean to the Fraser River, British Columbia. Drift of the magnetic field (i.e. geomagnetic imprinting) uniquely accounted for 23.2% and 44.0% of the variation in migration routes for sockeye and pink salmon, respectively. Ocean circulation (i.e. olfactory imprinting) predicted 6.1% and 0.1% of the variation in sockeye and pink migration routes, respectively. Sea surface temperature (a variable influencing salmon distribution but not navigation, directly) accounted for 13.0% of the variation in sockeye migration but was unrelated to pink migration. These findings suggest that geomagnetic navigation plays an important role in long-distance homing in salmon and that consideration of navigation mechanisms can aid in the management of migratory fishes by better predicting movement patterns. Finally, given the diversity of animals that use the Earth's magnetic field for navigation, geomagnetic drift may provide a unifying explanation for spatio-temporal variation in the movement patterns of many species. PMID:25056214
Putman, Nathan F; Jenkins, Erica S; Michielsens, Catherine G J; Noakes, David L G
Predicting the spatiotemporal dynamics of hair follicle patterns in the developing mouse.
Reaction-diffusion models have been used as a paradigm for describing the de novo emergence of biological patterns such as stripes and spots. In many organisms, these initial patterns are typically refined and elaborated over the subsequent course of development. Here we study the formation of secondary hair follicle patterns in the skin of developing mouse embryos. We used the expression of sex-determining region Y box 2 to identify and distinguish the primary and secondary hair follicles and to infer the spatiotemporal dynamics of the follicle formation process. Quantitative analysis of the specific follicle patterns observed reveals a simple geometrical rule governing the formation of secondary follicles, and motivates an expansion-induction (EI) model in which new follicle formation is driven by the physical growth of the embryo. The EI model requires only one diffusible morphogen and provides quantitative, accurate predictions on the relative positions and timing of secondary follicle formation, using only the observed configuration of primary follicles as input. The same model accurately describes the positions of additional follicles that emerge from skin explants treated with an activator. Thus, the EI model provides a simple and robust mechanism for predicting secondary space-filling patterns in growing embryos. PMID:24550288
Cheng, Chi Wa; Niu, Ben; Warren, Mya; Pevny, Larysa Halyna; Lovell-Badge, Robin; Hwa, Terence; Cheah, Kathryn S E
Curating the innate immunity interactome
Background The innate immune response is the first line of defence against invading pathogens and is regulated by complex signalling and transcriptional networks. Systems biology approaches promise to shed new light on the regulation of innate immunity through the analysis and modelling of these networks. A key initial step in this process is the contextual cataloguing of the components of this system and the molecular interactions that comprise these networks. InnateDB (http://www.innatedb.com) is a molecular interaction and pathway database developed to facilitate systems-level analyses of innate immunity. Results Here, we describe the InnateDB curation project, which is manually annotating the human and mouse innate immunity interactome in rich contextual detail, and present our novel curation software system, which has been developed to ensure interactions are curated in a highly accurate and data-standards compliant manner. To date, over 13,000 interactions (protein, DNA and RNA) have been curated from the biomedical literature. Here, we present data, illustrating how InnateDB curation of the innate immunity interactome has greatly enhanced network and pathway annotation available for systems-level analysis and discuss the challenges that face such curation efforts. Significantly, we provide several lines of evidence that analysis of the innate immunity interactome has the potential to identify novel signalling, transcriptional and post-transcriptional regulators of innate immunity. Additionally, these analyses also provide insight into the cross-talk between innate immunity pathways and other biological processes, such as adaptive immunity, cancer and diabetes, and intriguingly, suggests links to other pathways, which as yet, have not been implicated in the innate immune response. Conclusions In summary, curation of the InnateDB interactome provides a wealth of information to enable systems-level analysis of innate immunity.
Spatio-temporal variability and predictability of summer monsoon onset over the Philippines
NASA Astrophysics Data System (ADS)
The spatio-temporal variability of boreal summer monsoon onset over the Philippines is studied through the analysis of daily rainfall data across a network of 76 gauges for the period 1977 to 2004 and the pentad Merged Analysis of Precipitation from the US Climate Prediction Center from 1979 to 2006. The onset date is defined using a local agronomic definition, namely the first wet day of a 5-day period receiving at least 40 mm without any 15-day dry spell receiving <5 mm in the 30 days following the start of that period. The onset is found to occur rather abruptly across the western Philippines around mid-May on average and is associated with the set-up of a �classical� monsoonal circulation with low-level easterlies subsequently veering to southerly, and then southwesterly. The onset manifests itself merely as a seasonal increase of rainfall over the eastern Philippines, where rainfall occurs throughout most of the year. Interannual variability of the onset date is shown to consist of a spatially coherent large-scale component, rather similar over the western and eastern Philippines, with a moderate to high amount of local-scale (i.e. station scale) noise. In consequence, the large-scale signal can be easily retrieved from any sample of at least 5-6 stations across the network although the local-scale coherence and fingerprint of the large-scale signal of the onset date are found to be stronger over the central Philippines, roughly from Southern Luzon to Northern Mindanao. The seasonal predictability of local onset is analyzed through a cross-validated canonical correlation analysis using tropical Pacific and Indian Ocean sea surface temperature in March and the 850 hPa May wind field from dynamical forecast models as predictors. The regional-scale onset, defined as the average of standardized local-scale anomalies in onset date, shows good predictive skill ( r ? 0.8). Moreover, most of the stations show weak to moderate skill (median skill = 0.28-0.43 depending on the scheme) with spatial averaging across stations typically increasing skill to >0.6.
Moron, V.; Lucero, A.; Hilario, F.; Lyon, B.; Robertson, A. W.; Dewitt, D.
Organization of Physical Interactomes as Uncovered by Network Schemas
Large-scale protein-protein interaction networks provide new opportunities for understanding cellular organization and functioning. We introduce network schemas to elucidate shared mechanisms within interactomes. Network schemas specify descriptions of proteins and the topology of interactions among them. We develop algorithms for systematically uncovering recurring, over-represented schemas in physical interaction networks. We apply our methods to the S. cerevisiae interactome, focusing on schemas consisting of proteins described via sequence motifs and molecular function annotations and interacting with one another in one of four basic network topologies. We identify hundreds of recurring and over-represented network schemas of various complexity, and demonstrate via graph-theoretic representations how more complex schemas are organized in terms of their lower-order constituents. The uncovered schemas span a wide range of cellular activities, with many signaling and transport related higher-order schemas. We establish the functional importance of the schemas by showing that they correspond to functionally cohesive sets of proteins, are enriched in the frequency with which they have instances in the H. sapiens interactome, and are useful for predicting protein function. Our findings suggest that network schemas are a powerful paradigm for organizing, interrogating, and annotating cellular networks.
Chazelle, Bernard; Singh, Mona
An empirical framework for binary interactome mapping.
Several attempts have been made to systematically map protein-protein interaction, or 'interactome', networks. However, it remains difficult to assess the quality and coverage of existing data sets. Here we describe a framework that uses an empirically-based approach to rigorously dissect quality parameters of currently available human interactome maps. Our results indicate that high-throughput yeast two-hybrid (HT-Y2H) interactions for human proteins are more precise than literature-curated interactions supported by a single publication, suggesting that HT-Y2H is suitable to map a significant portion of the human interactome. We estimate that the human interactome contains approximately 130,000 binary interactions, most of which remain to be mapped. Similar to estimates of DNA sequence data quality and genome size early in the Human Genome Project, estimates of protein interaction data quality and interactome size are crucial to establish the magnitude of the task of comprehensive human interactome mapping and to elucidate a path toward this goal. PMID:19060904
Venkatesan, Kavitha; Rual, Jean-Fran�ois; Vazquez, Alexei; Stelzl, Ulrich; Lemmens, Irma; Hirozane-Kishikawa, Tomoko; Hao, Tong; Zenkner, Martina; Xin, Xiaofeng; Goh, Kwang-Il; Yildirim, Muhammed A; Simonis, Nicolas; Heinzmann, Kathrin; Gebreab, Fana; Sahalie, Julie M; Cevik, Sebiha; Simon, Christophe; de Smet, Anne-Sophie; Dann, Elizabeth; Smolyar, Alex; Vinayagam, Arunachalam; Yu, Haiyuan; Szeto, David; Borick, Heather; Dricot, Am�lie; Klitgord, Niels; Murray, Ryan R; Lin, Chenwei; Lalowski, Maciej; Timm, Jan; Rau, Kirstin; Boone, Charles; Braun, Pascal; Cusick, Michael E; Roth, Frederick P; Hill, David E; Tavernier, Jan; Wanker, Erich E; Barab�si, Albert-L�szl�; Vidal, Marc
Modelling the yeast interactome.
The topology behind biological interaction networks has been studied for over a decade. Yet, there is no definite agreement on the theoretical models which best describe protein-protein interaction (PPI) networks. Such models are critical to quantifying the significance of any empirical observation regarding those networks. Here, we perform a comprehensive analysis of yeast PPI networks in order to gain insights into their topology and its dependency on interaction-screening technology. We find that: (1) interaction-detection technology has little effect on the topology of PPI networks; (2) topology of these interaction networks differs in organisms with different cellular complexity (human and yeast); (3) clear topological difference is present between PPI networks, their functional sub-modules, and their inter-functional "linkers"; (4) high confidence PPI networks have more "geometrical" topology compared to predicted, incomplete, or noisy PPI networks; and (5) inter-functional "linker" proteins serve as mediators in signal transduction, transport, regulation and organisational cellular processes. PMID:24589662
Janji?, Vuk; Sharan, Roded; Pr�ulj, Nata�a
DAP-kinase (DAPK) is a Ca(2+)/calmodulin regulated Ser/Thr kinase that activates a diverse range of cellular activities. It is subject to multiple layers of regulation involving both intramolecular signaling, and interactions with additional proteins, including other kinases and phosphatases. Its protein stability is modulated by at least three distinct ubiquitin-dependent systems. Like many kinases, DAPK participates in several signaling cascades, by phosphorylating additional kinases such as ZIP-kinase and protein kinase D (PKD), or Pin1, a phospho-directed peptidyl-prolyl isomerase that regulates the function of many phosphorylated proteins. Other substrate targets have more direct cellular effects; for example, phosphorylation of the myosin II regulatory chain and tropomyosin mediate some of DAPK's cytoskeletal functions, including membrane blebbing during cell death and cell motility. DAPK induces distinct death pathways of apoptosis, autophagy and programmed necrosis. Among the substrates implicated in these processes, phosphorylation of PKD, Beclin 1, and the NMDA receptor has been reported. Interestingly, not all cellular effects are mediated by DAPK's catalytic activity. For example, by virtue of protein-protein interactions alone, DAPK activates pyruvate kinase isoform M2, the microtubule affinity regulating kinases and inflammasome protein NLRP3, to promote glycolysis, influence microtubule dynamics, and enhance interleukin-1? production, respectively. In addition, a number of other substrates and interacting proteins have been identified, the physiological significance of which has not yet been established. All of these substrates, effectors and regulators together comprise the DAPK interactome. By presenting the components of the interactome network, this review will clarify both the mechanisms by which DAPK function is regulated, and by which it mediates its various cellular effects. PMID:24220855
Bialik, Shani; Kimchi, Adi
A network flow approach to predict drug targets from microarray data, disease genes and interactome network - case study on prostate cancer
Background Systematic approach for drug discovery is an emerging discipline in systems biology research area. It aims at integrating interaction data and experimental data to elucidate diseases and also raises new issues in drug discovery for cancer treatment. However, drug target discovery is still at a trial-and-error experimental stage and it is a challenging task to develop a prediction model that can systematically detect possible drug targets to deal with complex diseases. Methods We integrate gene expression, disease genes and interaction networks to identify the effective drug targets which have a strong influence on disease genes using network flow approach. In the experiments, we adopt the microarray dataset containing 62 prostate cancer samples and 41 normal samples, 108 known prostate cancer genes and 322 approved drug targets treated in human extracted from DrugBank database to be candidate proteins as our test data. Using our method, we prioritize the candidate proteins and validate them to the known prostate cancer drug targets. Results We successfully identify potential drug targets which are strongly related to the well known drugs for prostate cancer treatment and also discover more potential drug targets which raise the attention to biologists at present. We denote that it is hard to discover drug targets based only on differential expression changes due to the fact that those genes used to be drug targets may not always have significant expression changes. Comparing to previous methods that depend on the network topology attributes, they turn out that the genes having potential as drug targets are weakly correlated to critical points in a network. In comparison with previous methods, our results have highest mean average precision and also rank the position of the truly drug targets higher. It thereby verifies the effectiveness of our method. Conclusions Our method does not know the real ideal routes in the disease network but it tries to find the feasible flow to give a strong influence to the disease genes through possible paths. We successfully formulate the identification of drug target prediction as a maximum flow problem on biological networks and discover potential drug targets in an accurate manner.
Air temperature suitability for Plasmodium falciparum malaria transmission in Africa 2000-2012: a high-resolution spatiotemporal prediction
Background Temperature suitability for malaria transmission is a useful predictor variable for spatial models of malaria infection prevalence. Existing continental or global models, however, are synoptic in nature and so do not characterize inter-annual variability in seasonal patterns of temperature suitability, reducing their utility for predicting malaria risk. Methods A malaria Temperature Suitability Index (TSI) was created by first modeling minimum and maximum air temperature with an eight-day temporal resolution from gap-filled MODerate Resolution Imaging Spectroradiometer (MODIS) daytime and night-time Land Surface Temperature (LST) datasets. An improved version of an existing biological model for malaria temperature suitability was then applied to the resulting temperature information for a 13-year data series. The mechanism underlying this biological model is simulation of emergent mosquito cohorts on a two-hour time-step and tracking of each cohort throughout its life to quantify the impact air temperature has on both mosquito survival and sporozoite development. Results The results of this research consist of 154 monthly raster surfaces that characterize spatiotemporal patterns in TSI across Africa from April 2000 through December 2012 at a 1 km spatial resolution. Generalized TSI patterns were as expected, with consistently high values in equatorial rain forests, seasonally variable values in tropical savannas (wet and dry) and montane areas, and low values in arid, subtropical regions. Comparisons with synoptic approaches demonstrated the additional information available within the dynamic TSI dataset that is lost in equivalent synoptic products derived from long-term monthly averages. Conclusions The dynamic TSI dataset presented here provides a new product with far richer spatial and temporal information than any other presently available for Africa. As spatiotemporal malaria modeling endeavors evolve, dynamic predictor variables such as the malaria temperature suitability data developed here will be essential for the rational assessment of changing patterns of malaria risk.
Empirically-controlled mapping of the Caenorhabditis elegans protein-protein interactome network
To provide accurate biological hypotheses and inform upon global properties of cellular networks, systematic identification of protein�protein interactions has to meet high-quality standards. We present an expanded Caenorhabditis elegans protein-protein interaction network, or �interactome� map derived from testing a matrix of ~ 10,000 � ~ 10,000 proteins using a highly specific high-throughput yeast two-hybrid system. Through a new quality control empirical framework, We show that the resulting dataset (Worm Interactome 2007 or WI-2007) is similar in quality to low-throughput data curated from the literature. Previous interaction datasets have been filtered and integrated with WI-2007 to generate a high confidence consolidated map (Worm Interactome version 8 or WI8). This work allows us to estimate the size of the worm interactome at ~ 116,000 interactions. Comparison with other types of functional genomic data shows the complementarity of distinct experimental approaches in predicting different functional relationship features between genes or proteins.
Simonis, Nicolas; Rual, Jean-Francois; Carvunis, Anne-Ruxandra; Tasan, Murat; Lemmens, Irma; Hirozane-Kishikawa, Tomoko; Hao, Tong; Sahalie, Julie M; Venkatesan, Kavitha; Gebreab, Fana; Cevik, Sebiha; Klitgord, Niels; Fan, Changyu; Braun, Pascal; Li, Ning; Ayivi-Guedehoussou, Nono; Dann, Elizabeth; Bertin, Nicolas; Szeto, David; Dricot, Amelie; Yildirim, Muhammed A; Lin, Chenwei; de Smet, Anne-Sophie; Kao, Huey-Ling; Simon, Christophe; Smolyar, Alex; Ahn, Jin Sook; Tewari, Muneesh; Boxem, Mike; Milstein, Stuart; Yu, Haiyuan; Dreze, Matija; Vandenhaute, Jean; Gunsalus, Kristin C; Cusick, Michael E; Hill, David E; Tavernier, Jan; Roth, Frederick P; Vidal, Marc
Empirically controlled mapping of the Caenorhabditis elegans protein-protein interactome network.
To provide accurate biological hypotheses and elucidate global properties of cellular networks, systematic identification of protein-protein interactions must meet high quality standards.We present an expanded C. elegans protein-protein interaction network, or 'interactome' map, derived from testing a matrix of approximately 10,000 x approximately 10,000 proteins using a highly specific, high-throughput yeast two-hybrid system. Through a new empirical quality control framework, we show that the resulting data set (Worm Interactome 2007, or WI-2007) was similar in quality to low-throughput data curated from the literature. We filtered previous interaction data sets and integrated them with WI-2007 to generate a high-confidence consolidated map (Worm Interactome version 8, or WI8). This work allowed us to estimate the size of the worm interactome at approximately 116,000 interactions. Comparison with other types of functional genomic data shows the complementarity of distinct experimental approaches in predicting different functional relationships between genes or proteins PMID:19123269
Simonis, Nicolas; Rual, Jean-Fran�ois; Carvunis, Anne-Ruxandra; Tasan, Murat; Lemmens, Irma; Hirozane-Kishikawa, Tomoko; Hao, Tong; Sahalie, Julie M; Venkatesan, Kavitha; Gebreab, Fana; Cevik, Sebiha; Klitgord, Niels; Fan, Changyu; Braun, Pascal; Li, Ning; Ayivi-Guedehoussou, Nono; Dann, Elizabeth; Bertin, Nicolas; Szeto, David; Dricot, Am�lie; Yildirim, Muhammed A; Lin, Chenwei; de Smet, Anne-Sophie; Kao, Huey-Ling; Simon, Christophe; Smolyar, Alex; Ahn, Jin Sook; Tewari, Muneesh; Boxem, Mike; Milstein, Stuart; Yu, Haiyuan; Dreze, Matija; Vandenhaute, Jean; Gunsalus, Kristin C; Cusick, Michael E; Hill, David E; Tavernier, Jan; Roth, Frederick P; Vidal, Marc
Information Flow Analysis of Interactome Networks
Recent studies of cellular networks have revealed modular organizations of genes and proteins. For example, in interactome networks, a module refers to a group of interacting proteins that form molecular complexes and\\/or biochemical pathways and together mediate a biological process. However, it is still poorly understood how biological information is transmitted between different modules. We have developed information flow analysis,
Patrycja Vasilyev Missiuro; Kesheng Liu; Lihua Zou; Brian C. Ross; Guoyan Zhao; Jun S. Liu; Hui Ge
Information Flow Analysis of Interactome Networks
Recent studies of cellular networks have revealed modular organizations of genes and proteins. For example, in interactome networks, a module refers to a group of interacting proteins that form molecular complexes and/or biochemical pathways and together mediate a biological process. However, it is still poorly understood how biological information is transmitted between different modules. We have developed information flow analysis, a new computational approach that identifies proteins central to the transmission of biological information throughout the network. In the information flow analysis, we represent an interactome network as an electrical circuit, where interactions are modeled as resistors and proteins as interconnecting junctions. Construing the propagation of biological signals as flow of electrical current, our method calculates an information flow score for every protein. Unlike previous metrics of network centrality such as degree or betweenness that only consider topological features, our approach incorporates confidence scores of protein�protein interactions and automatically considers all possible paths in a network when evaluating the importance of each protein. We apply our method to the interactome networks of Saccharomyces cerevisiae and Caenorhabditis elegans. We find that the likelihood of observing lethality and pleiotropy when a protein is eliminated is positively correlated with the protein's information flow score. Even among proteins of low degree or low betweenness, high information scores serve as a strong predictor of loss-of-function lethality or pleiotropy. The correlation between information flow scores and phenotypes supports our hypothesis that the proteins of high information flow reside in central positions in interactome networks. We also show that the ranks of information flow scores are more consistent than that of betweenness when a large amount of noisy data is added to an interactome. Finally, we combine gene expression data with interaction data in C. elegans and construct an interactome network for muscle-specific genes. We find that genes that rank high in terms of information flow in the muscle interactome network but not in the entire network tend to play important roles in muscle function. This framework for studying tissue-specific networks by the information flow model can be applied to other tissues and other organisms as well.
Missiuro, Patrycja Vasilyev; Liu, Kesheng; Zou, Lihua; Ross, Brian C.; Zhao, Guoyan; Liu, Jun S.; Ge, Hui
Information flow analysis of interactome networks.
Recent studies of cellular networks have revealed modular organizations of genes and proteins. For example, in interactome networks, a module refers to a group of interacting proteins that form molecular complexes and/or biochemical pathways and together mediate a biological process. However, it is still poorly understood how biological information is transmitted between different modules. We have developed information flow analysis, a new computational approach that identifies proteins central to the transmission of biological information throughout the network. In the information flow analysis, we represent an interactome network as an electrical circuit, where interactions are modeled as resistors and proteins as interconnecting junctions. Construing the propagation of biological signals as flow of electrical current, our method calculates an information flow score for every protein. Unlike previous metrics of network centrality such as degree or betweenness that only consider topological features, our approach incorporates confidence scores of protein-protein interactions and automatically considers all possible paths in a network when evaluating the importance of each protein. We apply our method to the interactome networks of Saccharomyces cerevisiae and Caenorhabditis elegans. We find that the likelihood of observing lethality and pleiotropy when a protein is eliminated is positively correlated with the protein's information flow score. Even among proteins of low degree or low betweenness, high information scores serve as a strong predictor of loss-of-function lethality or pleiotropy. The correlation between information flow scores and phenotypes supports our hypothesis that the proteins of high information flow reside in central positions in interactome networks. We also show that the ranks of information flow scores are more consistent than that of betweenness when a large amount of noisy data is added to an interactome. Finally, we combine gene expression data with interaction data in C. elegans and construct an interactome network for muscle-specific genes. We find that genes that rank high in terms of information flow in the muscle interactome network but not in the entire network tend to play important roles in muscle function. This framework for studying tissue-specific networks by the information flow model can be applied to other tissues and other organisms as well. PMID:19503817
Missiuro, Patrycja Vasilyev; Liu, Kesheng; Zou, Lihua; Ross, Brian C; Zhao, Guoyan; Liu, Jun S; Ge, Hui
Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation
This paper proposes a method based on multivariate spatial and temporal correlation to improve prediction accuracy in data reduction for Wireless Sensor Networks (WSN). Prediction of data not sent to the sink node is a technique used to save energy in WSNs by reducing the amount of data traffic. However, it may not be very accurate. Simulations were made involving simple linear regression and multiple linear regression functions to assess the performance of the proposed method. The results show a higher correlation between gathered inputs when compared to time, which is an independent variable widely used for prediction and forecasting. Prediction accuracy is lower when simple linear regression is used, whereas multiple linear regression is the most accurate one. In addition to that, our proposal outperforms some current solutions by about 50% in humidity prediction and 21% in light prediction. To the best of our knowledge, we believe that we are probably the first to address prediction based on multivariate correlation for WSN data reduction.
Carvalho, Carlos; Gomes, Danielo G.; Agoulmine, Nazim; de Souza, Jose Neuman
Predicting Chronic Fine and Coarse Particulate Exposures Using Spatiotemporal Models for the Northeastern and Midwestern United States
Background Chronic epidemiologic studies of particulate matter (PM) are limited by the lack of monitoring data, relying instead on citywide ambient concentrations to estimate exposures. This method ignores within-city spatial gradients and restricts studies to areas with nearby monitoring data. This lack of data is particularly restrictive for fine particles (PM with aerodynamic diameter < 2.5 ?m; PM2.5) and coarse particles (PM with aerodynamic diameter 2.5�10 ?m; PM10�2.5), for which monitoring is limited before 1999. To address these limitations, we developed spatiotemporal models to predict monthly outdoor PM2.5 and PM10�2.5 concentrations for the northeastern and midwestern United States. Methods For PM2.5, we developed models for two periods: 1988�1998 and 1999�2002. Both models included smooth spatial and regression terms of geographic information system-based and meteorologic predictors. To compensate for sparse monitoring data, the pre-1999 model also included predicted PM10 (PM with aerodynamic diameter < 10 ?m) and extinction coefficients (km?1). PM10�2.5 levels were estimated as the difference in monthly predicted PM10 and PM2.5, with predicted PM10 from our previously developed PM10 model. Results Predictive performance for PM2.5 was strong (cross-validation R2 = 0.77 and 0.69 for post-1999 and pre-1999 PM2.5 models, respectively) with high precision (2.2 and 2.7 ?g/m3, respectively). Models performed well irrespective of population density and season. Predictive performance for PM10�2.5 was weaker (cross-validation R2 = 0.39) with lower precision (5.5 ?g/m3). PM10�2.5 levels exhibited greater local spatial variability than PM10 or PM2.5, suggesting that PM2.5 measurements at ambient monitoring sites are more representative for surrounding populations than for PM10 and especially PM10�2.5. Conclusions We provide semiempirical models to predict spatially and temporally resolved long-term average outdoor concentrations of PM2.5 and PM10�2.5 for estimating exposures of populations living in the northeastern and midwestern United States.
Yanosky, Jeff D.; Paciorek, Christopher J.; Suh, Helen H.
Inferring the Brassica rapa Interactome Using Protein-Protein Interaction Data from Arabidopsis thaliana
Following successful completion of the Brassica rapa sequencing project, the next step is to investigate functions of individual genes/proteins. For Arabidopsis thaliana, large amounts of protein�protein interaction (PPI) data are available from the major PPI databases (DBs). It is known that Brassica crop species are closely related to A. thaliana. This provides an opportunity to infer the B. rapa interactome using PPI data available from A. thaliana. In this paper, we present an inferred B. rapa interactome that is based on the A. thaliana PPI data from two resources: (i) A. thaliana PPI data from three major DBs, BioGRID, IntAct, and TAIR. (ii) ortholog-based A. thaliana PPI predictions. Linking between B. rapa and A. thaliana was accomplished in three complementary ways: (i) ortholog predictions, (ii) identification of gene duplication based on synteny and collinearity, and (iii) BLAST sequence similarity search. A complementary approach was also applied, which used known/predicted domain�domain interaction data. Specifically, since the two species are closely related, we used PPI data from A. thaliana to predict interacting domains that might be conserved between the two species. The predicted interactome was investigated for the component that contains known A. thaliana meiotic proteins to demonstrate its usability.
Yang, Jianhua; Osman, Kim; Iqbal, Mudassar; Stekel, Dov J.; Luo, Zewei; Armstrong, Susan J.; Franklin, F. Chris H.
Prediction of spatio-temporal bone formation in scaffold by diffusion equation.
Developing a successful bone tissue engineering strategy entails translation of experimental findings to clinical needs. A major leap forward toward this goal is developing a quantitative tool to predict spatial and temporal bone formation in scaffold. We hypothesized that bone formation in scaffold follows diffusion phenomenon. Subsequently, we developed an analytical formulation for bone formation, which had only three unknown parameters: C, the final bone volume fraction, ?, the so-called scaffold osteoconduction coefficient, and h, the so-called peri-scaffold osteoinduction coefficient. The three parameters were estimated by identifying the model within vivo data of polymeric scaffolds implanted in the femoral condyle of rats. In vivo data were obtained by longitudinal micro-CT scanning of the animals. Having identified the three parameters, we used the model to predict the course of bone formation in two previously published in vivo studies. We found the predicted values to be consistent with the experimental ones. Bone formation into a scaffold can then adequately be described through diffusion phenomenon. This model allowed us to spatially and temporally predict the outcome of tissue engineering scaffolds with only 3 physically relevant parameters. PMID:21700329
Roshan-Ghias, Alireza; Vogel, Arne; Rakotomanana, Lalaonirina; Pioletti, Dominique P
Predicting the spatio-temporal distribution of Culicoides imicola in Sardinia using a discrete-time population model
Background Culicoides imicola KIEFFER, 1913 (Diptera: Ceratopogonidae) is the principal vector of Bluetongue disease in the Mediterranean basin, Africa and Asia. Previous studies have identified a range of eco-climatic variables associated with the distribution of C. imicola, and these relationships have been used to predict the large-scale distribution of the vector. However, these studies are not temporally-explicit and can not be used to predict the seasonality in C. imicola abundances. Between 2001 and 2006, longitudinal entomological surveillance was carried out throughout Italy, and provided a comprehensive spatio-temporal dataset of C. imicola catches in Onderstepoort-type black-light traps, in particular in Sardinia where the species is considered endemic. Methods We built a dynamic model that allows describing the effect of eco-climatic indicators on the monthly abundances of C. imicola in Sardinia. Model precision and accuracy were evaluated according to the influence of process and observation errors. Results A first-order autoregressive cofactor, a digital elevation model and MODIS Land Surface Temperature (LST)/or temperatures acquired from weather stations explained ~77% of the variability encountered in the samplings carried out in 9 sites during 6?years. Incorporating Normalized Difference Vegetation Index (NDVI) or rainfall did not increase the model's predictive capacity. On average, dynamics simulations showed good accuracy (predicted vs. observed r corr?=?0.9). Although the model did not always reproduce the absolute levels of monthly abundances peaks, it succeeded in reproducing the seasonality in population level and allowed identifying the periods of low abundances and with no apparent activity. On that basis, we mapped C. imicola monthly distribution over the entire Sardinian region. Conclusions This study demonstrated prospects for modelling data arising from Culicoides longitudinal entomological surveillance. The framework explicitly incorporates the influence of eco-climatic factors on population growth rates and accounts for observation and process errors. Upon validation, such a model could be used to predict monthly population abundances on the basis of environmental conditions, and hence can potentially reduce the amount of entomological surveillance.
Mapping the functional yeast ABC transporter interactome
ABC transporters are a ubiquitous class of integral membrane proteins of immense clinical interest because of their strong association with human disease and pharmacology. To improve our understanding of these proteins, we used Membrane Yeast Two-Hybrid (MYTH) technology to map the protein interactome of all non-mitochondrial ABC transporters in the model organism Saccharomy cescerevisiae, and combined this data with previously reported yeast ABC transporter interactions in the BioGRID database to generate a comprehensive, integrated interactome. We show that ABC transporters physically associate with proteins involved in a surprisingly diverse range of functions. We specifically examine the importance of the physical interactions of ABC transporters in both the regulation of one another and in the modulation of proteins involved in zinc homeostasis. The interaction network presented here will be a powerful resource for increasing our fundamental understanding of the cellular role and regulation of ABC transporters.
Snider, Jamie; Hanif, Asad; Lee, Mid Eum; Jin, Ke; Yu, Analyn R.; Graham, Chris; Chuk, Matthew; Damjanovic, Dunja; Wierzbicka, Marta; Tang, Priscilla; Balderes, Dina; Wong, Victoria; Jessulat, Matthew; Darowski, Katelyn D.; Luis, Bryan-Joseph San; Shevelev, Igor; Sturley, Stephen L; Boone, Charles; Greenblatt, Jack F.; Zhang, Zhaolei; Paumi, Christian M.; Babu, Mohan; Park, Hay-Oak; Michaelis, Susan; Stagljar, Igor
Inferring modules from human protein interactome classes
Background The integration of protein-protein interaction networks derived from high-throughput screening approaches and complementary sources is a key topic in systems biology. Although integration of protein interaction data is conventionally performed, the effects of this procedure on the result of network analyses has not been examined yet. In particular, in order to optimize the fusion of heterogeneous interaction datasets, it is crucial to consider not only their degree of coverage and accuracy, but also their mutual dependencies and additional salient features. Results We examined this issue based on the analysis of modules detected by network clustering methods applied to both integrated and individual (disaggregated) data sources, which we call interactome classes. Due to class diversity, we deal with variable dependencies of data features arising from structural specificities and biases, but also from possible overlaps. Since highly connected regions of the human interactome may point to potential protein complexes, we have focused on the concept of modularity, and elucidated the detection power of module extraction algorithms by independent validations based on GO, MIPS and KEGG. From the combination of protein interactions with gene expressions, a confidence scoring scheme has been proposed before proceeding via GO with further classification in permanent and transient modules. Conclusions Disaggregated interactomes are shown to be informative for inferring modularity, thus contributing to perform an effective integrative analysis. Validation of the extracted modules by multiple annotation allows for the assessment of confidence measures assigned to the modules in a protein pathway context. Notably, the proposed multilayer confidence scheme can be used for network calibration by enabling a transition from unweighted to weighted interactomes based on biological evidence.
Dependence of the climate prediction skill on spatiotemporal scales: Internal versus radiatively-forced contribution
NASA Astrophysics Data System (ADS)
This article aims at quantifying the improvement in climate prediction skill as a function of temporal (from monthly to decadal) and spatial scales (from grid point to global) when initializing a perturbed parameter ensemble of the Hadley Centre Climate Model. The focus is on near-surface temperature and precipitation in the Tropical band, the Northern and Southern hemispheres. For temperature, the forecast system reproduces the dominant impact of the external forcing at global spatial scale and at decadal time scales. There are significant improvements with initialization for the first 40 forecast months in the global and tropical domains. In the Northern (Southern) hemisphere, the initialization increases the skill in the first 12 (20) months on regional but not hemispheric scales. The initialization has a stronger impact in the model variants with a weaker global-mean temperature trend. For precipitation, the initialization corrects the negative correlation found at global and tropical scales.
Volpi, D.; Doblas-Reyes, F. J.; Garc�A-Serrano, J.; Guemas, V.
Predicting breeding habitat for amphibians: a spatiotemporal analysis across Yellowstone National Park
The ability to predict amphibian breeding across landscapes is important for informing land management decisions and helping biologists better understand and remediate factors contributing to declines in amphibian populations. We built geospatial models of likely breeding habitats for each of four amphibian species that breed in Yellowstone National Park (YNP). We used field data collected in 2000-2002 from 497 sites among 16 basins and predictor variables from geospatial models produced from remotely sensed data (e.g., digital elevation model, complex topographic index, landform data, wetland probabililty, and vegetative cover). Except for 31 sites in one basin that were surveyed in both 2000 and 2002, all sites were surveyed once. We used polytomous regression to build statistical models for each species of amphibian from 1) field survey site data only, 2) field data combined with data from geospatial models, and 3) data from geospatial models only. Based on measures of receiver operating characteristic (ROC) scores, models of the second type best explained likely breeding habitat because they contained the most information (ROC values ranged from 0.70 - 0.88). However, models of the third type could be applied to the entire YNP landscape and produced maps that could be verified with reserve field data. Accuracy rates for models built for single years were highly variable, ranging from 0.30 to 0.78. Accuracy rates for models built with data combined from multiple years were higher and less variable, ranging from 0.60 to 0.80. Combining results from the geospatial multiyear models yielded maps of "core" breeding areas (areas with high probability values for all three years) surrounded by areas that scored high for only one or two years, providing an estimate of variability among years. Such information can highlight landscape options for amphibian conservation. For example, our models identify alternative for areas that could be protected for each species, including 6828-10 764 ha for tiger salamanders; 971-3017 ha for western toads; 4732-16 696 ha for boreal chorus frogs; 4940-19 690 hectares for Columbia spotted frogs.
Bartelt, Paul E.; Gallant, Alisa L.; Klaver, Robert W.; Wright, Christopher K.; Patla, Debra A.; Peterson, Charles R.
Predicting breeding habitat for amphibians: a spatiotemporal analysis across Yellowstone National Park.
The ability to predict amphibian breeding across landscapes is important for informing land management decisions and helping biologists better understand and remediate factors contributing to declines in amphibian populations. We built geospatial models of likely breeding habitats for each of four amphibian species that breed in Yellowstone National Park (YNP). We used field data collected in 2000-2002 from 497 sites among 16 basins and predictor variables from geospatial models produced from remotely sensed data (e.g., digital elevation model, complex topographic index, landform data, wetland probability, and vegetative cover). Except for 31 sites in one basin that were surveyed in both 2000 and 2002, all sites were surveyed once. We used polytomous regression to build statistical models for each species of amphibian from (1) field survey site data only, (2) field data combined with data from geospatial models, and (3) data from geospatial models only. Based on measures of receiver operating characteristic (ROC) scores, models of the second type best explained likely breeding habitat because they contained the most information (ROC values ranged from 0.70 to 0.88). However, models of the third type could be applied to the entire YNP landscape and produced maps that could be verified with reserve field data. Accuracy rates for models built for single years were highly variable, ranging from 0.30 to 0.78. Accuracy rates for models built with data combined from multiple years were higher and less variable, ranging from 0.60 to 0.80. Combining results from the geospatial multiyear models yielded maps of "core" breeding areas (areas with high probability values for all three years) surrounded by areas that scored high for only one or two years, providing an estimate of variability among years. Such information can highlight landscape options for amphibian conservation. For example, our models identify alternative areas that could be protected for each species, including 6828-10 764 ha for tiger salamanders, 971-3017 ha for western toads, 4732-16 696 ha for boreal chorus frogs, and 4940-19 690 ha for Columbia spotted frogs. PMID:22073642
Bartelt, Paul E; Gallant, Alisa L; Klaver, Robert W; Wright, Chris K; Patla, Debra A; Peterson, Charles R
Global protein interactome exploration through mining genome-scale data in Arabidopsis thaliana
Background Many essential cellular processes, such as cellular metabolism, transport, cellular metabolism and most regulatory mechanisms, rely on physical interactions between proteins. Genome-wide protein interactome networks of yeast, human and several other animal organisms have already been established, but this kind of network reminds to be established in the field of plant. Results We first predicted the protein protein interaction in Arabidopsis thaliana with methods, including ortholog, SSBP, gene fusion, gene neighbor, phylogenetic profile, coexpression, protein domain, and used Na�ve Bayesian approach next to integrate the results of these methods and text mining data to build a genome-wide protein interactome network. Furthermore, we adopted the data of GO enrichment analysis, pathway, published literature to validate our network, the confirmation of our network shows the feasibility of using our network to predict protein function and other usage. Conclusions Our interactome is a comprehensive genome-wide network in the organism plant Arabidopsis thaliana, and provides a rich resource for researchers in related field to study the protein function, molecular interaction and potential mechanism under different conditions.
A viral-human interactome based on structural motif-domain interactions captures the human infectome.
Protein interactions between a pathogen and its host are fundamental in the establishment of the pathogen and underline the infection mechanism. In the present work, we developed a single predictive model for building a host-viral interactome based on the identification of structural descriptors from motif-domain interactions of protein complexes deposited in the Protein Data Bank (PDB). The structural descriptors were used for searching, in a database of protein sequences of human and five clinically important viruses; therefore, viral and human proteins sharing a descriptor were predicted as interacting proteins. The analysis of the host-viral interactome allowed to identify a set of new interactions that further explain molecular mechanism associated with viral infections and showed that it was able to capture human proteins already associated to viral infections (human infectome) and non-infectious diseases (human diseasome). The analysis of human proteins targeted by viral proteins in the context of a human interactome showed that their neighbors are enriched in proteins reported with differential expression under infection and disease conditions. It is expected that the findings of this work will contribute to the development of systems biology for infectious diseases, and help guide the rational identification and prioritization of novel drug targets. PMID:23951184
Segura-Cabrera, Aldo; Garc�a-P�rez, Carlos A; Guo, Xianwu; Rodr�guez-P�rez, Mario A
A process-level evaluation of the spatiotemporal variability of CO2 fluxes predicted by terrestrial biosphere models using atmospheric data
NASA Astrophysics Data System (ADS)
Terrestrial biosphere models (TBMs) are used to extrapolate local observations and process-level understanding of land-atmosphere carbon exchange to larger regions, and can serve as a predictive tool for examining carbon-climate interactions and global change. Understanding and improving the performance of TBMs is thus crucial to the carbon cycle research community. In this work, we evaluate the spatiotemporal patterns of net ecosystem exchange (NEE) simulated by TBMs using atmospheric CO2 observations and a Geostatistical Inverse Modeling (GIM) framework. The evaluation methodology is based on the ability (or inability) of the spatiotemporal patterns in NEE estimates to explain the variability observed in atmospheric CO2 distribution. More simply, we examine whether the spatiotemporal patterns of NEE simulated by TBMs (including CASA-GFED, ORCHIDEE, VEGAS2 and SiB3) are consistent with the variations observed in the atmosphere. A similar GIM methodology is also applied using environmental variables (such as water availability, temperature, radiation, etc.) rather than TBMs, to explore the environmental processes associated with the variability of NEE, and determine which processes are associated with good/poor performance in TBMs. We find that NEE simulated by TBMs is consistent with that seen by atmospheric measurements more often during growing season months (Apr-Sept) than during the non-growing season. Over Temperate Broadleaf and Mixed Forests, Temperate Coniferous Forests and Temperate Grasslands, Savannas and Shrublands, atmospheric measurements are sufficiently sensitive to NEE fluxes to constrain the evaluation of model performance during the majority of the year (about 7-8 months in a year, mostly in growing season). For these regions and months, at least one of the TBMs is found to be able to reproduce the observed variability, but the most representative TBM varies by region and month. For the remaining months, none of the TBMs are able to reproduce the observed variability, whereas a linear combination of environmental variables is able to do so. By further comparing environmental processes associated with the spatiotemporal variability of NEE from the observations and from each TBM, we find that TBMs perform well when radiation-related processes are dominant; however, TBMs perform less well when water-availability (and temperature) are more important. It thus appears that the representation of water availability and its impact on fluxes within TBMs are areas of opportunity for improving TBM predictions, for example, in the seasonal transition. Further exploration of the processes associated with water availability in state-of-the-art TBMs, such as soil respiration, could therefore lead to improvements in their ability to represent the spatiotemporal variability of fluxes during the dormant season and their ability to represent phenology and carbon exchange during transition months.
Fang, Y.; Michalak, A. M.; Shiga, Y. P.; Yadav, V.
Identification of SRC as a potent drug target for asthma, using an integrative approach of protein interactome analysis and in silico drug discovery.
Network-biology inspired modeling of interactome data and computational chemistry have the potential to revolutionize drug discovery by complementing conventional methods. We consider asthma, a complex disease characterized by intricate molecular mechanisms, for our study. We aim to integrate prediction of potent drug targets using graph-theoretical methods and subsequent identification of small molecules capable of modulating activity of the best target. In this work, we construct the protein interactome underlying this disease: Asthma Protein Interactome (API). Using a strategy based on network analysis of the interactome, we identify a set of potential drug targets for asthma. Topologically and dynamically, v-src sarcoma (Schmidt-Ruppin A-2) viral oncogene homolog (SRC) emerges as the most central target in API. SRC is known to play an important role in promoting airway smooth muscle cell growth and facilitating migration in airway remodeling. From interactome analysis, and with the reported role in respiratory mechanisms, SRC emerges as a promising drug target for asthma. Further, we proceed to identify leads for SRC from a public database of small molecules. We predict two potential leads for SRC using ligand-based virtual screening methodology. PMID:22775150
Randhawa, Vinay; Bagler, Ganesh
How perfect can protein interactomes be?
Any engineered device should certainly not contain nonfunctional components, for this would be a waste of energy and money. In contrast, evolutionary theory tells us that biological systems need not be optimized and may very well accumulate nonfunctional elements. Mutational and demographic processes contribute to the cluttering of eukaryotic genomes and transcriptional networks with "junk" DNA and spurious DNA binding sites. Here, we question whether such a notion should be applied to protein interactomes-that is, whether these protein interactomes are expected to contain a fraction of nonselected, nonfunctional protein-protein interactions (PPIs), which we term "noisy." We propose a simple relationship between the fraction of noisy interactions expected in a given organism and three parameters: (i) the number of mutations needed to create and destroy interactions, (ii) the size of the proteome, and (iii) the fitness cost of noisy interactions. All three parameters suggest that noisy PPIs are expected to exist. Their existence could help to explain why PPIs determined from large-scale studies often lack functional relationships between interacting proteins, why PPIs are poorly conserved across organisms, and why the PPI space appears to be immensely large. Finally, we propose experimental strategies to estimate the fraction of evolutionary noise in PPI networks. PMID:19261595
Levy, Emmanuel D; Landry, Christian R; Michnick, Stephen W
Integration, visualization and analysis of human interactome.
Data integration and visualization are crucial to obtain meaningful hypotheses from the diversity of 'omics' fields and the large volume of heterogeneous and distributed data sets. In this review we focus on network analysis as a key technique to integrate, visualize and extrapolate relevant information from diverse data. We first describe challenges in integrating different types of data and then focus on systematically exploring network properties to gain insight into network function. We also describe the relationship between network structures and function of elements that form it. Next, we highlight the role of the interactome in connecting data derived from different experiments, and we stress the importance of network analysis to recognize interaction context-specific features. Finally, we present an example integration to demonstrate the value of the network approach in cancer research, and highlight the importance of dynamic data in the specific context of signaling pathways. PMID:24491561
Pastrello, Chiara; Pasini, Elisa; Kotlyar, Max; Otasek, David; Wong, Serene; Sangrar, Waheed; Rahmati, Sara; Jurisica, Igor
Comparative protein interactomics of neuroglobin and myoglobin
Neuroglobin is a hypoxia-inducible O2-binding protein with neuroprotective effects in cell and animal models of stroke and Alzheimer�s disease. The mechanism underlying neuroglobin�s cytoprotective action is unknown, although several possibilities have been proposed, including antioxidative and antiapoptotic effects. We used affinity purification-mass spectrometry methods to identify neuroglobin-interacting proteins in normoxic and hypoxic murine neuronal (HN33) cell lysates, and to compare these interactions with those of a structurally and functionally related protein, myoglobin. We report that the protein interactomes of neuroglobin and myoglobin overlap substantially and are modified by hypoxia. In addition, neuroglobin-interacting proteins include partners consistent with both antioxidative and antiapoptotic functions, as well as with a relationship to several neurodegenerative diseases.
Haines, Bryan A.; Davis, Darcy A.; Zykovich, Artem; Peng, Botao; Rao, Rammohan; Mooney, Sean D.; Jin, Kunlin; Greenberg, David A.
Alzheimer disease: An interactome of many diseases.
Alzheimer Disease (AD) is an outcome as well as source of many diseases. Alzheimer is linked with many other diseases like Diabetes type 2, cholesterolemia, hypertension and many more. But how each of these diseases affecting other is still unknown to scientific community. Signaling Pathways of one disease is interlinked with other disease. But to what extent healthy brain is affected when any signaling in human body is disturbed is the question that matters. There is a need of Pathway analysis, Protein-Protein interaction (PPI) and the conserved interactome study in AD and linked diseases. It will be helpful in finding the potent drug or vaccine target in conscious manner. In the present research the Protein-Protein interaction of all the proteins involved in Alzheimer Disease is analyzed using ViSANT and osprey tools and pathway analysis further reveals the significant genes/proteins linking AD with other diseases. PMID:24753659
Rao, Balaji S; Gupta, Krishna Kant; Karanam, Pujitha; Peruri, Anusha
Alzheimer disease: An interactome of many diseases
Alzheimer Disease (AD) is an outcome as well as source of many diseases. Alzheimer is linked with many other diseases like Diabetes type 2, cholesterolemia, hypertension and many more. But how each of these diseases affecting other is still unknown to scientific community. Signaling Pathways of one disease is interlinked with other disease. But to what extent healthy brain is affected when any signaling in human body is disturbed is the question that matters. There is a need of Pathway analysis, Protein-Protein interaction (PPI) and the conserved interactome study in AD and linked diseases. It will be helpful in finding the potent drug or vaccine target in conscious manner. In the present research the Protein-Protein interaction of all the proteins involved in Alzheimer Disease is analyzed using ViSANT and osprey tools and pathway analysis further reveals the significant genes/proteins linking AD with other diseases.
Rao, Balaji S.; Gupta, Krishna Kant; Karanam, Pujitha; Peruri, Anusha
The topology of the growing human interactome data.
We have long moved past the one-gene�one-function concept originally proposed by Beadle and Tatum back in 1941; but the full understanding of genotype�phenotype relations still largely relies on the analysis of static, snapshot-like, interaction data sets. Here, we look at what global patterns can be uncovered if we simply trace back the human interactome network over the last decade of protein- protein interaction (PPI) screening. We take a purely topological approach and find that as the human interactome is getting denser, it is not only gaining in structure (in terms of now being better fit by structured network models than before), but also there are patterns in the way in which it is growing: (a) newly added proteins tend to get linked to existing proteins in the interactome that are not know to interact; and (b) new proteins tend to link to already well connected proteins. Moreover, the alignment between human and yeast interactomes spanning over 40% of yeast�s proteins � that are involved in regulation of transcription, RNA splicing and other cellcycle-related processes�suggests the existence of a part of the interactome which remains topologically and functionally unaffected through evolution. Furthermore, we find a small sub-network, specific to the �core� of the human interactome and involved in regulation of transcription and cancer development, whose wiring has not changed within the human interactome over the last 10 years of interacome data acquisition. Finally, we introduce a generalisation of the clustering coefficient of a network as a new measure called the cycle coefficient, and use it to show that PPI networks of human and model organisms are wired in a tight way which forbids the occurrence large cycles. PMID:24953453
Janji?, Vuk; Pr�ulj, Nata�a
Evidence for network evolution in an Arabidopsis interactome map.
Plants have unique features that evolved in response to their environments and ecosystems. A full account of the complex cellular networks that underlie plant-specific functions is still missing. We describe a proteome-wide binary protein-protein interaction map for the interactome network of the plant Arabidopsis thaliana containing about 6200 highly reliable interactions between about 2700 proteins. A global organization of plant biological processes emerges from community analyses of the resulting network, together with large numbers of novel hypothetical functional links between proteins and pathways. We observe a dynamic rewiring of interactions following gene duplication events, providing evidence for a model of evolution acting upon interactome networks. This and future plant interactome maps should facilitate systems approaches to better understand plant biology and improve crops. PMID:21798944
Evidence for Network Evolution in an Arabidopsis Interactome Map
Plants have unique features that evolved in response to their environments and ecosystems. A full account of the complex cellular networks that underlie plant-specific functions is still missing. We describe a proteome-wide binary protein-protein interaction map for the interactome network of the plant Arabidopsis thaliana containing ~6,200 highly reliable interactions between ~2,700 proteins. A global organization of plant biological processes emerges from community analyses of the resulting network, together with large numbers of novel hypothetical functional links between proteins and pathways. We observe a dynamic rewiring of interactions following gene duplication events, providing evidence for a model of evolution acting upon interactome networks. This and future plant interactome maps should facilitate systems approaches to better understand plant biology and improve crops.
Xenopus Meiotic Microtubule-Associated Interactome
In metazoan oocytes the assembly of a microtubule-based spindle depends on the activity of a large number of accessory non-tubulin proteins, many of which remain unknown. In this work we isolated the microtubule-bound proteins from Xenopus eggs. Using mass spectrometry we identified 318 proteins, only 43 of which are known to bind microtubules. To integrate our results, we compiled for the first time a network of the meiotic microtubule-related interactome. The map reveals numerous interactions between spindle microtubules and the newly identified non-tubulin spindle components and highlights proteins absent from the mitotic spindle proteome. To validate newly identified spindle components, we expressed as GFP-fusions nine proteins identified by us and for first time demonstrated that Mgc68500, Loc398535, Nif3l1bp1/THOC7, LSM14A/RAP55A, TSGA14/CEP41, Mgc80361 and Mgc81475 are associated with spindles in egg extracts or in somatic cells. Furthermore, we showed that transfection of HeLa cells with siRNAs, corresponding to the human orthologue of Mgc81475 dramatically perturbs spindle formation in HeLa cells. These results show that our approach to the identification of the Xenopus microtubule-associated proteome yielded bona fide factors with a role in spindle assembly.
Winter, Christof; Juhem, Aurelie; Schroeder, Michael; Shevchenko, Andrej; Popov, Andrei V.
The Domain Landscape of Virus-Host Interactomes
Viral infections result in millions of deaths in the world today. A thorough analysis of virus-host interactomes may reveal insights into viral infection and pathogenic strategies. In this study, we presented a landscape of virus-host interactomes based on protein domain interaction. Compared to the analysis at protein level, this domain-domain interactome provided a unique abstraction of protein-protein interactome. Through comparisons among DNA, RNA, and retrotranscribing viruses, we identified a core of human domains, that viruses used to hijack the cellular machinery and evade the immune system, which might be promising antiviral drug targets. We showed that viruses preferentially interacted with host hub and bottleneck domains, and the degree and betweenness centrality among three categories of viruses are significantly different. Further analysis at functional level highlighted that different viruses perturbed the host cellular molecular network by common and unique strategies. Most importantly, we creatively proposed a viral disease network among viral domains, human domains and the corresponding diseases, which uncovered several unknown virus-disease relationships that needed further verification. Overall, it is expected that the findings will help to deeply understand the viral infection and contribute to the development of antiviral therapy.
Zhou, Yanhong; Li, Yixue
Crowd sourcing a new paradigm for interactome driven drug target identification in Mycobacterium tuberculosis.
A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative 'Connect to Decode' (C2D) to generate the first and largest manually curated interactome of Mtb termed 'interactome pathway' (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach. PMID:22808064
Vashisht, Rohit; Mondal, Anupam Kumar; Jain, Akanksha; Shah, Anup; Vishnoi, Priti; Priyadarshini, Priyanka; Bhattacharyya, Kausik; Rohira, Harsha; Bhat, Ashwini G; Passi, Anurag; Mukherjee, Keya; Choudhary, Kumari Sonal; Kumar, Vikas; Arora, Anshula; Munusamy, Prabhakaran; Subramanian, Ahalyaa; Venkatachalam, Aparna; Gayathri, S; Raj, Sweety; Chitra, Vijaya; Verma, Kaveri; Zaheer, Salman; Balaganesh, J; Gurusamy, Malarvizhi; Razeeth, Mohammed; Raja, Ilamathi; Thandapani, Madhumohan; Mevada, Vishal; Soni, Raviraj; Rana, Shruti; Ramanna, Girish Muthagadhalli; Raghavan, Swetha; Subramanya, Sunil N; Kholia, Trupti; Patel, Rajesh; Bhavnani, Varsha; Chiranjeevi, Lakavath; Sengupta, Soumi; Singh, Pankaj Kumar; Atray, Naresh; Gandhi, Swati; Avasthi, Tiruvayipati Suma; Nisthar, Shefin; Anurag, Meenakshi; Sharma, Pratibha; Hasija, Yasha; Dash, Debasis; Sharma, Arun; Scaria, Vinod; Thomas, Zakir; Chandra, Nagasuma; Brahmachari, Samir K; Bhardwaj, Anshu
Enhanced Prediction of Src Homology 2 (SH2) Domain Binding Potentials Using a Fluorescence Polarization-derived c-Met, c-Kit, ErbB, and Androgen Receptor Interactome.
Many human diseases are associated with aberrant regulation of phosphoprotein signaling networks. Src homology 2 (SH2) domains represent the major class of protein domains in metazoans that interact with proteins phosphorylated on the amino acid residue tyrosine. Although current SH2 domain prediction algorithms perform well at predicting the sequences of phosphorylated peptides that are likely to result in the highest possible interaction affinity in the context of random peptide library screens, these algorithms do poorly at predicting the interaction potential of SH2 domains with physiologically derived protein sequences. We employed a high throughput interaction assay system to empirically determine the affinity between 93 human SH2 domains and phosphopeptides abstracted from several receptor tyrosine kinases and signaling proteins. The resulting interaction experiments revealed over 1000 novel peptide-protein interactions and provided a glimpse into the common and specific interaction potentials of c-Met, c-Kit, GAB1, and the human androgen receptor. We used these data to build a permutation-based logistic regression classifier that performed considerably better than existing algorithms for predicting the interaction potential of several SH2 domains. PMID:24728074
Leung, Kin K; Hause, Ronald J; Barkinge, John L; Ciaccio, Mark F; Chuu, Chih-Pin; Jones, Richard B
High Quality Binary Protein Interaction Map of the Yeast Interactome Network
Current yeast interactome network maps contain several hundred molecular complexes with limited and somewhat controversial representation of direct binary interactions. We carried out a comparative quality assessment of current yeast interactome datasets, demonstrating that high-throughput yeast two-hybrid (Y2H) provides high-quality binary interaction information. As a large fraction of the yeast binary interactome remains to be mapped, we developed an empirically-controlled
Haiyuan Yu; Pascal Braun; Muhammed A. Yildirim; Irma Lemmens; Kavitha Venkatesan; Julie Sahalie; Tomoko Hirozane-Kishikawa; Fana Gebreab; Na Li; Nicolas Simonis; Tong Hao; Jean-Fran?ois Rual; Am�lie Dricot; Alexei Vazquez; Ryan R. Murray; Christophe Simon; Leah Tardivo; Stanley Tam; Nenad Svrzikapa; Changyu Fan; Anne-Sophie de Smet; Adriana Motyl; Michael E. Hudson; Xiaofeng Xin; Michael E. Cusick; Troy Moore; Charlie Boone; Michael Snyder; Frederick P. Roth; Albert-L�szl� Barab�si; Jan Tavernier; David E. Hill; Marc Vidal
High-Quality Binary Protein Interaction Map of the Yeast Interactome Network
Current yeast interactome network maps contain several hundred molecular complexes with limited and somewhat controversial representation of direct binary interactions. We carried out a comparative quality assessment of current yeast interactome data sets, demonstrating that high-throughput yeast two-hybrid (Y2H) screening provides high-quality binary interaction information. Because a large fraction of the yeast binary interactome remains to be mapped, we developed
Haiyuan Yu; Pascal Braun; Muhammed A. Yildirim; Irma Lemmens; Kavitha Venkatesan; Julie Sahalie; Tomoko Hirozane-Kishikawa; Fana Gebreab; Na Li; Nicolas Simonis; Tong Hao; Jean-Fran�ois Rual; Am�lie Dricot; Alexei Vazquez; Ryan R. Murray; Christophe Simon; Leah Tardivo; Stanley Tam; Nenad Svrzikapa; Changyu Fan; Anne-Sophie de Smet; Adriana Motyl; Michael E. Hudson; Xiaofeng Xin; Michael E. Cusick; Troy Moore; Charlie Boone; Michael Snyder; Frederick P. Roth; Albert-L�szl� Barab�si; Jan Tavernier; David E. Hill; Marc Vidal
A human skeletal muscle interactome centered on proteins involved in muscular dystrophies: LGMD interactome
Background The complexity of the skeletal muscle and the identification of numerous human disease-causing mutations in its constitutive proteins make it an interesting tissue for proteomic studies aimed at understanding functional relationships of interacting proteins in both health and diseases. Method We undertook a large-scale study using two-hybrid screens and a human skeletal-muscle cDNA library to establish a proteome-scale map of protein-protein interactions centered on proteins involved in limb-girdle muscular dystrophies (LGMD). LGMD is a group of more than 20 different neuromuscular disorders that principally affect the proximal pelvic and shoulder girdle muscles. Results and conclusion The interaction network we unraveled incorporates 1018 proteins connected by 1492 direct binary interactions and includes 1420 novel protein-protein interactions. Computational, experimental and literature-based analyses were performed to assess the overall quality of this network. Interestingly, LGMD proteins were shown to be highly interconnected, in particular indirectly through sarcomeric proteins. In-depth mining of the LGMD-centered interactome identified new candidate genes for orphan LGMDs and other neuromuscular disorders. The data also suggest the existence of functional links between LGMD2B/dysferlin and gene regulation, between LGMD2C/?-sarcoglycan and energy control and between LGMD2G/telethonin and maintenance of genome integrity. This dataset represents a valuable resource for future functional investigations.
Summer Rains and Dry Seasons in the Upper Blue Nile Basin: The Predictability of Half a Century of Past and Future Spatiotemporal Patterns
During the last 100 years the Ethiopian upper Blue Nile Basin (BNB) has undergone major changes in land use, and is now potentially facing changes in climate. Rainfall over BNB supplies over two-thirds of the water to the Nile and supports a large local population living mainly on subsistence agriculture. Regional food security is sensitive to both the amount and timing of rain and is already an important political challenge that will be further complicated if scenarios of climate change are realized. In this study a simple spatial model of the timing and duration of summer rains (Kiremt) and dry season (Bega), and annual rain over the upper BNB was established from observed data between 1952 and 2004. The model was used to explore potential impacts of climate change on these rains, using a down-scaled ECHAM5/MP1-OM scenario between 2050 and 2100. Over the observed period the amount, onset and duration of Kiremt rains and rain-free Bega days have exhibited a consistent spatial pattern. The spatially averaged annual rainfall was 1490 mm of which 93% was Kiremt rain. The average Kiremt rain and number of rainy days was higher in the southwest (322 days) and decreased towards the north (136 days). Under the 2050�2100 scenario, the annual mean rainfall is predicted to increase by 6% and maintain the same spatial pattern as in the past. A larger change in annual rainfall is expected in the southwest (ca. +130 mm) with a gradually smaller change towards the north (ca. +70 mm). Results highlight the need to account for the characteristic spatiotemporal zonation when planning water management and climate adaptation within the upper BNB. The presented simple spatial resolved models of the presence of Kiremt and annual total rainfall could be used as a baseline for such long-term planning.
Mellander, Per-Erik; Gebrehiwot, Solomon G.; Gardenas, Annemieke I.; Bewket, Woldeamlak; Bishop, Kevin
Discovering the Hippo pathway protein-protein interactome.
The Hippo pathway is a signal transduction pathway that regulates organ growth, stem cell biology, regeneration and cancer. Three recent proteomic studies with Hippo pathway components uncovered extensive networks of interacting proteins revealing novel connections to cell-cell junctions, regulation by vesicle trafficking, and phosphorylation-dependent remodeling of the interactome, and provide a rich landscape of novel interactors ripe for mechanistic studies. PMID:24418760
Moya, Iv�n M; Halder, Georg
Charting the NF-?B Pathway Interactome Map
Inflammation is part of a complex physiological response to harmful stimuli and pathogenic stress. The five components of the Nuclear Factor ?B (NF-?B) family are prominent mediators of inflammation, acting as key transcriptional regulators of hundreds of genes. Several signaling pathways activated by diverse stimuli converge on NF-?B activation, resulting in a regulatory system characterized by high complexity. It is increasingly recognized that the number of components that impinges upon phenotypic outcomes of signal transduction pathways may be higher than those taken into consideration from canonical pathway representations. Scope of the present analysis is to provide a wider, systemic picture of the NF-?B signaling system. Data from different sources such as literature, functional enrichment web resources, protein-protein interaction and pathway databases have been gathered, curated, integrated and analyzed in order to reconstruct a single, comprehensive picture of the proteins that interact with, and participate to the NF-?B activation system. Such a reconstruction shows that the NF-?B interactome is substantially different in quantity and quality of components with respect to canonical representations. The analysis highlights that several neglected but topologically central proteins may play a role in the activation of NF-?B mediated responses. Moreover the interactome structure fits with the characteristics of a bow tie architecture. This interactome is intended as an open network resource available for further development, refinement and analysis.
Tieri, Paolo; Termanini, Alberto; Bellavista, Elena; Salvioli, Stefano; Capri, Miriam; Franceschi, Claudio
Charting the NF-?B pathway interactome map.
Inflammation is part of a complex physiological response to harmful stimuli and pathogenic stress. The five components of the Nuclear Factor ?B (NF-?B) family are prominent mediators of inflammation, acting as key transcriptional regulators of hundreds of genes. Several signaling pathways activated by diverse stimuli converge on NF-?B activation, resulting in a regulatory system characterized by high complexity. It is increasingly recognized that the number of components that impinges upon phenotypic outcomes of signal transduction pathways may be higher than those taken into consideration from canonical pathway representations. Scope of the present analysis is to provide a wider, systemic picture of the NF-?B signaling system. Data from different sources such as literature, functional enrichment web resources, protein-protein interaction and pathway databases have been gathered, curated, integrated and analyzed in order to reconstruct a single, comprehensive picture of the proteins that interact with, and participate to the NF-?B activation system. Such a reconstruction shows that the NF-?B interactome is substantially different in quantity and quality of components with respect to canonical representations. The analysis highlights that several neglected but topologically central proteins may play a role in the activation of NF-?B mediated responses. Moreover the interactome structure fits with the characteristics of a bow tie architecture. This interactome is intended as an open network resource available for further development, refinement and analysis. PMID:22403694
Tieri, Paolo; Termanini, Alberto; Bellavista, Elena; Salvioli, Stefano; Capri, Miriam; Franceschi, Claudio
Identifying protein complexes from interactome based on essential proteins and local fitness method.
High-throughput experimental technologies, along with computational predictions, have promoted the emergence of large-scale interactome for numerous organisms. Identification of protein complexes from these interactome networks is crucial to understand principles of cellular organization and predict protein functions. Protein complexes are generally considered as dense subgraphs. However, the real protein complexes do not always have highly connected topologies. In this paper, a novel protein complex identifying method, named EPOF, is proposed, using essential proteins and the local metric of vertex fitness. In EPOF, cliques in the subnetwork which is consisted by the essential proteins are firstly considered as seeds, which are ordered according to their size and the number of their neighbors. A protein complex is extended from a seed based on the evaluation of its neighbors' fitness value. Then, the similar procedure is applied to the cliques identified in the subnetwork which is consisted by the proteins which is not clustered in the first step. When EPOF identifies complexes by expanding essential protein cliques, the essential proteins have higher priority and lower threshold. When it identifies complexes by expanding nonessential protein cliques, the nonessential proteins have higher priority and lower threshold. Finally, the last step, we output the identified complexes set. The proposed algorithm EPOF is applied to the unweighted and weighted interaction networks of S. cerevisiae and detects many well known protein complexes. We compare the performances of EPOF to other ten previous algorithms, including EAGLE, NFC, MCODE, DPClus, IPCA, CPM, MCL, CMC, SPICi, and Core-Attachment. Experimental results show that EPOF outperforms other previous competing algorithms in terms of matching with known complexes, sensitivity, specificity, f-measure, function enrichment and accuracy. The program and related files available on https://github.com/gangchen/epof. PMID:22711784
Wang, Jianxin; Chen, Gang; Liu, Binbin; Li, Min; Pan, Yi
Identification of human disease genes from interactome network using graphlet interaction.
Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes. PMID:24465923
Wang, Xiao-Dong; Huang, Jia-Liang; Yang, Lun; Wei, Dong-Qing; Qi, Ying-Xin; Jiang, Zong-Lai
Immobilizing chemically synthesized analogues of PI(3,4,5)P3 onto Affi-10 beads and incorporating them into liposomes allowed their use as affinity absorbents in the comprehensive analysis of the phosphoinositide interactome using cytosolic cell extracts of the LIM1215 colon cancer cell line. This led to the identification of 282 proteins that either interact with PI(3,4,5)P3 or are indirectly captured as part of a complex containing a PI(3,4,5)P3 binding partner. Identification of the proteins was achieved using affinity/LC-MS/MS experiments. PMID:19463016
Catimel, Bruno; Yin, Meng-Xin; Schieber, Christine; Condron, Melanie; Patsiouras, Heather; Catimel, Jenny; Robinson, Diane E J E; Wong, Leon S-M; Nice, Edouard C; Holmes, Andrew B; Burgess, Antony W
A Human XPC Protein Interactome--A Resource
Global genome nucleotide excision repair (GG-NER) is responsible for identifying and removing bulky adducts from non-transcribed DNA that result from damaging agents such as UV radiation and cisplatin. Xeroderma pigmentosum complementation group C (XPC) is one of the essential damage recognition proteins of the GG-NER pathway and its dysfunction results in xeroderma pigmentosum (XP), a disorder involving photosensitivity and a predisposition to cancer. To better understand the identification of DNA damage by XPC in the context of chromatin and the role of XPC in the pathogenesis of XP, we characterized the interactome of XPC using a high throughput yeast two-hybrid screening. Our screening showed 49 novel interactors of XPC involved in DNA repair and replication, proteolysis and post-translational modifications, transcription regulation, signal transduction, and metabolism. Importantly, we validated the XPC-OTUD4 interaction by co-IP and provided evidence that OTUD4 knockdown in human cells indeed affects the levels of ubiquitinated XPC, supporting a hypothesis that the OTUD4 deubiquitinase is involved in XPC recycling by cleaving the ubiquitin moiety. This high-throughput characterization of the XPC interactome provides a resource for future exploration and suggests that XPC may have many uncharacterized cellular functions.
Lubin, Abigail; Zhang, Ling; Chen, Hua; White, Victoria M.; Gong, Feng
A protein domain-based interactome network for C. elegans early embryogenesis.
Many protein-protein interactions are mediated through independently folding modular domains. Proteome-wide efforts to model protein-protein interaction or "interactome" networks have largely ignored this modular organization of proteins. We developed an experimental strategy to efficiently identify interaction domains and generated a domain-based interactome network for proteins involved in C. elegans early-embryonic cell divisions. Minimal interacting regions were identified for over 200 proteins, providing important information on their domain organization. Furthermore, our approach increased the sensitivity of the two-hybrid system, resulting in a more complete interactome network. This interactome modeling strategy revealed insights into C. elegans centrosome function and is applicable to other biological processes in this and other organisms. PMID:18692475
Boxem, Mike; Maliga, Zoltan; Klitgord, Niels; Li, Na; Lemmens, Irma; Mana, Miyeko; de Lichtervelde, Lorenzo; Mul, Joram D; van de Peut, Diederik; Devos, Maxime; Simonis, Nicolas; Yildirim, Muhammed A; Cokol, Murat; Kao, Huey-Ling; de Smet, Anne-Sophie; Wang, Haidong; Schlaitz, Anne-Lore; Hao, Tong; Milstein, Stuart; Fan, Changyu; Tipsword, Mike; Drew, Kevin; Galli, Matilde; Rhrissorrakrai, Kahn; Drechsel, David; Koller, Daphne; Roth, Frederick P; Iakoucheva, Lilia M; Dunker, A Keith; Bonneau, Richard; Gunsalus, Kristin C; Hill, David E; Piano, Fabio; Tavernier, Jan; van den Heuvel, Sander; Hyman, Anthony A; Vidal, Marc
Cost effective strategies for completing the Interactome
Comprehensive protein interaction mapping projects are underway for many model species and humans. A key step in these projects is estimating the time, cost, and personnel required for obtaining an accurate and complete map. Here, we model the cost of interaction map completion across a spectrum of experimental designs. We show that current efforts may require up to 20 independent tests covering each protein pair to approach completion. We explore designs for reducing this cost substantially, including prioritization of protein pairs, probability thresholding, and interaction prediction. The best designs lower cost by four-fold overall and >100-fold in early stages of mapping. We demonstrate the best strategy in an ongoing project in Drosophila, in which we map 450 high-confidence interactions using 47 microtiter plates, versus thousands of plates expected using current designs. This study provides a framework for assessing the feasibility of interaction mapping projects and for future efforts to increase their efficiency.
Schwartz, Ariel S.; Yu, Jingkai; Gardenour, Kyle R.; Finley, Russell L.; Ideker, Trey
Photoreactive Stapled BH3 Peptides to Dissect the BCL-2 Family Interactome
SUMMARY Defining protein interactions forms the basis for discovery of biological pathways, disease mechanisms, and opportunities for therapeutic intervention. To harness the robust binding affinity and selectivity of structured peptides for interactome discovery, we engineered photoreactive stapled BH3 peptide helices that covalently capture their physiologic BCL-2 family targets. The crosslinking ?-helices covalently trap both static and dynamic protein interactors, and enable rapid identification of interaction sites, providing a critical link between interactome discovery and targeted drug design.
Braun, Craig R.; Mintseris, Julian; Gavathiotis, Evripidis; Bird, Gregory H.; Gygi, Steven P.; Walensky, Loren D.
Interactomics of Qa-SNARE in Arabidopsis thaliana.
Membrane trafficking in plants is involved in cellular development and the adaptation to various environmental changes. SNARE (soluble N-ethylmaleimide-sensitive factor attachment receptor) proteins mediate the fusion between vesicles and organelles to facilitate transport cargo proteins in cells. To characterize further the SNARE protein networks in cells, we carried out interactome analysis of SNARE proteins using 12 transgenic Arabidopsis thaliana plants expressing green fluorescent protein (GFP)-tagged Qa-SNAREs (SYP111, SYP121, SYP122, SYP123, SYP132, SYP21, SYP22, SYP31, SYP32, SYP41, SYP42 and SYP43). Microsomal fractions were prepared from each transgenic root, and subjected to immunoprecipitation (IP) using micromagnetic beads coupled to anti-GFP antibodies. To identify Qa-SNARE-interacting proteins, all immunoprecipitated products were then subjected to mass spectrometric (IP-MS) analysis. The IP-MS data revealed not only known interactions of SNARE proteins, but also unknown interactions. The IP-MS results were next categorized by gene ontology analysis. The data revealed that categories of cellular component organization, the cytoskeleton and endosome were enriched in the SYP2, SYP3 and SYP4 groups. In contrast, transporter activity was classified specifically in the SYP132 group. We also identified a novel interaction between SYP22 and VAMP711, which was validated using co-localization analysis with confocal microscopy and IP. Additional novel SNARE-interacting proteins play roles in vesicle transport and lignin biosynthesis, and were identified as membrane microdomain-related proteins. We propose that Qa-SNARE interactomics is useful for understanding SNARE interactions across the whole cell. PMID:24556609
Fujiwara, Masayuki; Uemura, Tomohiro; Ebine, Kazuo; Nishimori, Yuka; Ueda, Takashi; Nakano, Akihiko; Sato, Masa H; Fukao, Yoichiro
Tools and strategies for DNA damage interactome analysis.
DNA is the target of multiple endogenous and exogenous agents generating chemical lesions on the double helix. Cellular DNA damage response pathways rely on a myriad of proteins interacting with DNA alterations. The cartography of this interactome currently includes well known actors of chromatin remodelling, DNA repair or proteins hijacked from their natural functions such as transcription factors. In order to go further into the characterisation of these protein networks, proteomics-based methods began to be used in the early 2000s. The strategies are diverse and include mainly (i) damaged DNA molecules used as targets on protein microarrays, (ii) damaged DNA probes used to trap within complex cellular extracts proteins that are then separated and identified by proteomics, (iii) identification of chromatin- bound proteins after a genotoxic stress, or (iv) identification of proteins associated with other proteins already known to be part of DNA damage interactome. All these approaches have already been performed to find new proteins recognizing oxidised bases, abasic sites, strand breaks or crosslinks generated by anticancer drugs such as nitrogen mustards and platinating agents. Identified interactions are generally confirmed using complementary methods such as electromobility shift assays or surface plasmon resonance. These strategies allowed, for example, demonstration of interactions between cisplatin-DNA crosslinks and PARP-1 or the protein complex PTW/PP. The next challenging step will be to understand the biological repercussions of these newly identified interactions which may help to unravel new mechanisms involved in genetic toxicology, discover new cellular responses to anticancer drugs or identify new biomarkers and therapeutic targets. PMID:23220222
Bounaix Morand du Puch, Christophe; Barbier, Ewa; Sauvaigo, Sylvie; Gasparutto, Didier; Breton, Jean
In vitro nuclear interactome of the HIV-1 Tat protein
Background One facet of the complexity underlying the biology of HIV-1 resides not only in its limited number of viral proteins, but in the extensive repertoire of cellular proteins they interact with and their higher-order assembly. HIV-1 encodes the regulatory protein Tat (86�101aa), which is essential for HIV-1 replication and primarily orchestrates HIV-1 provirus transcriptional regulation. Previous studies have demonstrated that Tat function is highly dependent on specific interactions with a range of cellular proteins. However they can only partially account for the intricate molecular mechanisms underlying the dynamics of proviral gene expression. To obtain a comprehensive nuclear interaction map of Tat in T-cells, we have designed a proteomic strategy based on affinity chromatography coupled with mass spectrometry. Results Our approach resulted in the identification of a total of 183 candidates as Tat nuclear partners, 90% of which have not been previously characterised. Subsequently we applied in silico analysis, to validate and characterise our dataset which revealed that the Tat nuclear interactome exhibits unique signature(s). First, motif composition analysis highlighted that our dataset is enriched for domains mediating protein, RNA and DNA interactions, and helicase and ATPase activities. Secondly, functional classification and network reconstruction clearly depicted Tat as a polyvalent protein adaptor and positioned Tat at the nexus of a densely interconnected interaction network involved in a range of biological processes which included gene expression regulation, RNA biogenesis, chromatin structure, chromosome organisation, DNA replication and nuclear architecture. Conclusion We have completed the in vitro Tat nuclear interactome and have highlighted its modular network properties and particularly those involved in the coordination of gene expression by Tat. Ultimately, the highly specialised set of molecular interactions identified will provide a framework to further advance our understanding of the mechanisms of HIV-1 proviral gene silencing and activation.
Gautier, Virginie W; Gu, Lili; O'Donoghue, Niaobh; Pennington, Stephen; Sheehy, Noreen; Hall, William W
ANIA: ANnotation and Integrated Analysis of the 14-3-3 interactome
The dimeric 14-3-3 proteins dock onto pairs of phosphorylated Ser and Thr residues on hundreds of proteins, and thereby regulate many events in mammalian cells. To facilitate global analyses of these interactions, we developed a web resource named ANIA: ANnotation and Integrated Analysis of the 14-3-3 interactome, which integrates multiple data sets on 14-3-3-binding phosphoproteins. ANIA also pinpoints candidate 14-3-3-binding phosphosites using predictor algorithms, assisted by our recent discovery that the human 14-3-3-interactome is highly enriched in 2R-ohnologues. 2R-ohnologues are proteins in families of two to four, generated by two rounds of whole genome duplication at the origin of the vertebrate animals. ANIA identifies candidate �lynchpins�, which are 14-3-3-binding phosphosites that are conserved across members of a given 2R-ohnologue protein family. Other features of ANIA include a link to the catalogue of somatic mutations in cancer database to find cancer polymorphisms that map to 14-3-3-binding phosphosites, which would be expected to interfere with 14-3-3 interactions. We used ANIA to map known and candidate 14-3-3-binding enzymes within the 2R-ohnologue complement of the human kinome. Our projections indicate that 14-3-3s dock onto many more human kinases than has been realized. Guided by ANIA, PAK4, 6 and 7 (p21-activated kinases 4, 6 and 7) were experimentally validated as a 2R-ohnologue family of 14-3-3-binding phosphoproteins. PAK4 binding to 14-3-3 is stimulated by phorbol ester, and involves the �lynchpin� site phosphoSer99 and a major contribution from Ser181. In contrast, PAK6 and PAK7 display strong phorbol ester-independent binding to 14-3-3, with Ser113 critical for the interaction with PAK6. These data point to differential 14-3-3 regulation of PAKs in control of cell morphology. Database URL: https://ania-1433.lifesci.dundee.ac.uk/prediction/webserver/index.py
Tinti, Michele; Madeira, Fabio; Murugesan, Gavuthami; Hoxhaj, Gerta; Toth, Rachel; MacKintosh, Carol
Calciomics: integrative studies of Ca2+-binding proteins and their interactomes in biological systems
Calcium ion (Ca2+), the fifth most common chemical element in the earth�s crust, represents the most abundant mineral in the human body. By binding to a myriad of proteins distributed in different cellular organelles, Ca2+ impacts nearly every aspect of cellular life. In prokaryotes, Ca2+ plays an important role in bacterial movement, chemotaxis, survival reactions and sporulation. In eukaryotes, Ca2+ has been chosen through evolution to function as a universal and versatile intracellular signal. Viruses, as obligate intracellular parasites, also develop smart strategies to manipulate the host Ca2+ signaling machinery to benefit their own life cycles. This review focuses on recent advances in applying both bioinformatic and experimental approaches to predict and validate Ca2+-binding proteins and their interactomes in biological systems on genome-wide scale (termed �calciomics�). Calmodulin is used as an example of Ca2+-binding protein (CaBP) to demonstrate the role of CaBPs on the regulation of biological functions. This review is anticipated to rekindle interest in investigating Ca2+-binding proteins and Ca2+-modulated functions at the systems level in the post-genomic era.
Zhou, Yubin; Xue, Shenghui; Yang, Jenny J.
Spatiotemporal discrete multicolor solitons
NASA Astrophysics Data System (ADS)
We have found various families of two-dimensional spatiotemporal solitons in quadratically nonlinear waveguide arrays. The families of unstaggered odd, even, and twisted stationary solutions are thoroughly characterized and their stability against perturbations is investigated. We show that the twisted and even solitons display instability, while most of the odd solitons show remarkable stability upon evolution.
Xu, Zhiyong; Kartashov, Yaroslav V.; Crasovan, Lucian-Cornel; Mihalache, Dumitru; Torner, Lluis
Examining the interactome of huperzine A by magnetic biopanning.
Huperzine A is a bioactive compound derived from traditional Chinese medicine plant Qian Ceng Ta (Huperzia serrata), and was found to have multiple neuroprotective effects. In addition to being a potent acetylcholinesterase inhibitor, it was thought to act through other mechanisms such as antioxidation, antiapoptosis, etc. However, the molecular targets involved with these mechanisms were not identified. In this study, we attempted to exam the interactome of Huperzine A using a cDNA phage display library and also mammalian brain tissue extracts. The drugs were chemically linked on the surface of magnetic particles and the interactive phages or proteins were collected and analyzed. Among the various cDNA expressing phages selected, one was identified to encode the mitochondria NADH dehydrogenase subunit 1. Specific bindings between the drug and the target phages and target proteins were confirmed. Another enriched phage clone was identified as mitochondria ATP synthase, which was also panned out from the proteome of mouse brain tissue lysate. These data indicated the possible involvement of mitochondrial respiratory chain matrix enzymes in Huperzine A's pharmacological effects. Such involvement had been suggested by previous studies based on enzyme activity changes. Our data supported the new mechanism. Overall we demonstrated the feasibility of using magnetic biopanning as a simple and viable method for investigating the complex molecular mechanisms of bioactive molecules. PMID:22615909
Guo, Wei; Liu, Shupeng; Peng, Jinliang; Wei, Xiaohui; Sun, Ye; Qiu, Yangsheng; Gao, Guangwei; Wang, Peng; Xu, Yuhong
Transcriptional atlas of cardiogenesis maps congenital heart disease interactome.
Mammalian heart development is built on highly conserved molecular mechanisms with polygenetic perturbations resulting in a spectrum of congenital heart diseases (CHD). However, knowledge of cardiogenic ontogeny that regulates proper cardiogenesis remains largely based on candidate-gene approaches. Mapping the dynamic transcriptional landscape of cardiogenesis from a genomic perspective is essential to integrate the knowledge of heart development into translational applications that accelerate disease discovery efforts toward mechanistic-based treatment strategies. Herein, we designed a time-course transcriptome analysis to investigate the genome-wide dynamic expression landscape of innate murine cardiogenesis ranging from embryonic stem cells to adult cardiac structures. This comprehensive analysis generated temporal and spatial expression profiles, revealed stage-specific gene functions, and mapped the dynamic transcriptome of cardiogenesis to curated pathways. Reconciling known genetic underpinnings of CHD, we deconstructed a disease-centric dynamic interactome encoded within this cardiogenic atlas to identify stage-specific developmental disturbances clustered on regulation of epithelial-to-mesenchymal transition (EMT), BMP signaling, NF-AT signaling, TGFb-dependent EMT, and Notch signaling. Collectively, this cardiogenic transcriptional landscape defines the time-dependent expression of cardiac ontogeny and prioritizes regulatory networks at the interface between health and disease. PMID:24803680
Li, Xing; Martinez-Fernandez, Almudena; Hartjes, Katherine A; Kocher, Jean-Pierre A; Olson, Timothy M; Terzic, Andre; Nelson, Timothy J
Competing endogenous RNA and interactome bioinformatic analyses on human telomerase.
We present a classic interactome bioinformatic analysis and a study on competing endogenous (ce) RNAs for hTERT. The hTERT gene codes for the catalytic subunit and limiting component of the human telomerase complex. Human telomerase reverse transcriptase (hTERT) is essential for the integrity of telomeres. Telomere dysfunctions have been widely reported to be involved in aging, cancer, and cellular senescence. The hTERT gene network has been analyzed using the BioGRID interaction database (http://thebiogrid.org/) and related analysis tools such as Osprey (http://biodata.mshri.on.ca/osprey/servlet/Index) and GeneMANIA (http://genemania.org/). The network of interaction of hTERT transcripts has been further analyzed following the competing endogenous (ce) RNA hypotheses (messenger [m] RNAs cross-talk via micro [mi] RNAs) using the miRWalk database and tools (www.ma.uni-heidelberg.de/apps/zmf/mirwalk/). These analyses suggest a role for Akt, nuclear factor-?B (NF-?B), heat shock protein 90 (HSP90), p70/p80 autoantigen, 14-3-3 proteins, and dynein in telomere functions. Roles for histone acetylation/deacetylation and proteoglycan metabolism are also proposed. PMID:24713059
Arancio, Walter; Pizzolanti, Giuseppe; Genovese, Swonild Ilenia; Baiamonte, Concetta; Giordano, Carla
Identification of functional hubs and modules by converting interactome networks into hierarchical ordering of proteins
Background Protein-protein interactions play a key role in biological processes of proteins within a cell. Recent high-throughput techniques have generated protein-protein interaction data in a genome-scale. A wide range of computational approaches have been applied to interactome network analysis for uncovering functional organizations and pathways. However, they have been challenged because ofcomplex connectivity. It has been investigated that protein interaction networks are typically characterized by intrinsic topological features: high modularity and hub-oriented structure. Elucidating the structural roles of modules and hubs is a critical step in complex interactome network analysis. Results We propose a novel approach to convert the complex structure of an interactome network into hierarchical ordering of proteins. This algorithm measures functional similarity between proteins based on the path strength model, and reveals a hub-oriented tree structure hidden in the complex network. We score hub confidence and identify functional modules in the tree structure of proteins, retrieved by our algorithm. Our experimental results in the yeast protein interactome network demonstrate that the selected hubs are essential proteins for performing functions. In network topology, they have a role in bridging different functional modules. Furthermore, our approach has high accuracy in identifying functional modules hierarchically distributed. Conclusions Decomposing, converting, and synthesizing complex interaction networks are fundamental tasks for modeling their structural behaviors. In this study, we systematically analyzed complex interactome network structures for retrievingfunctional information. Unlike previous hierarchical clustering methods, this approach dynamically explores the hierarchical structure of proteins in a global view. It is well-applicable to the interactome networks in high-level organisms because of its efficiency and scalability.
Spatiotemporal drought forecasting using nonlinear models
NASA Astrophysics Data System (ADS)
Spatiotemporal data mining is the extraction of unknown and implicit knowledge, structures, spatiotemporal relationships, or patterns not explicitly stored in spatiotemporal databases. As one of data mining techniques, forecasting is widely used to predict the unknown future based upon the patterns hidden in the current and past data. In order to achieve spatiotemporal forecasting, some mature analysis tools, e.g., time series and spatial statistics are extended to the spatial dimension and the temporal dimension, respectively. Drought forecasting plays an important role in the planning and management of natural resources and water resource systems in a river basin. Early and timelines forecasting of a drought event can help to take proactive measures and set out drought mitigation strategies to alleviate the impacts of drought. Despite the widespread application of nonlinear mathematical models, comparative studies on spatiotemporal drought forecasting using different models are still a huge task for modellers. This study uses a promising approach, the Gamma Test (GT), to select the input variables and the training data length, so that the trial and error workload could be greatly reduced. The GT enables to quickly evaluate and estimate the best mean squared error that can be achieved by a smooth model on any unseen data for a given selection of inputs, prior to model construction. The GT is applied to forecast droughts using monthly Standardized Precipitation Index (SPI) timeseries at multiple timescales in several precipitation stations at Pinios river basin in Thessaly region, Greece. Several nonlinear models have been developed efficiently, with the aid of the GT, for 1-month up to 12-month ahead forecasting. Several temporal and spatial statistical indices were considered for the performance evaluation of the models. The predicted results show reasonably good agreement with the actual data for short lead times, whereas the forecasting accuracy decreases with increase in lead time. Finally, the developed nonlinear models could be used in an early warning system for risk and decision analyses at the study area.
Vasiliades, Lampros; Loukas, Athanasios
Neurodevelopmental disorders: mechanisms and boundary definitions from genomes, interactomes and proteomes.
Neurodevelopmental disorders such as intellectual disability, autism spectrum disorder and schizophrenia lack precise boundaries in their clinical definitions, epidemiology, genetics and protein-protein interactomes. This calls into question the appropriateness of current categorical disease concepts. Recently, there has been a rising tide to reformulate neurodevelopmental nosological entities from biology upward. To facilitate this developing trend, we propose that identification of unique proteomic signatures that can be strongly associated with patient's risk alleles and proteome-interactome-guided exploration of patient genomes could define biological mechanisms necessary to reformulate disorder definitions. PMID:24301647
Mullin, A P; Gokhale, A; Moreno-De-Luca, A; Sanyal, S; Waddington, J L; Faundez, V
Neurodevelopmental disorders: mechanisms and boundary definitions from genomes, interactomes and proteomes
Neurodevelopmental disorders such as intellectual disability, autism spectrum disorder and schizophrenia lack precise boundaries in their clinical definitions, epidemiology, genetics and protein�protein interactomes. This calls into question the appropriateness of current categorical disease concepts. Recently, there has been a rising tide to reformulate neurodevelopmental nosological entities from biology upward. To facilitate this developing trend, we propose that identification of unique proteomic signatures that can be strongly associated with patient's risk alleles and proteome-interactome-guided exploration of patient genomes could define biological mechanisms necessary to reformulate disorder definitions.
Mullin, A P; Gokhale, A; Moreno-De-Luca, A; Sanyal, S; Waddington, J L; Faundez, V
\\u000a Spatio-temporal clustering is a process of grouping objects based on their spatial and temporal similarity. It is relatively\\u000a new subfield of data mining which gained high popularity especially in geographic information sciences due to the pervasiveness\\u000a of all kinds of location-based or environmental devices that record position, time or\\/and environmental properties of an object\\u000a or set of objects in real-time.
Slava Kisilevich; Florian Mansmann; Mirco Nanni; Salvatore Rinzivillo
Spatio-temporal variability in Ebro river basin (NE Spain): Global SST as potential source of predictability on decadal time scales
NASA Astrophysics Data System (ADS)
SummaryThis paper investigates the spatial and temporal variability of streamflow in the Ebro river basin and its potential predictability. Principal Component Analysis applied to monthly streamflow series from 83 gauging stations distributed through the basin, reveals three homogeneous regions: Basque-Cantabrian, Pyrenees and Southern Mediterranean. Attending to this classification the main characteristic time scales of the maximum monthly streamflows are studied by Singular Spectral Analysis (SSA). Decadal variations in streamflow make particularly large contributions to year-to-year streamflow variance in stations placed in the Basque-Cantabrian and Southern Mediterranean regions, while for the Pyrenees flows the interannual contribution is more important. The predictability of the Ebro flow anomalies has been investigated using a combined methodology: at decadal time scales SST anomalies from several regions provide a significant source of predictability for the Ebro flow, while at interannual time scales autoregressive-moving-average modelling, applied to the time series previously filtered by SSA, is able to provide potential skill in forecasting. For gauging stations associated to the Basque-Cantabrian region significant correlations between the maximum monthly streamflow anomalies and a tripole-like pattern in the North Atlantic SSTs during the previous spring are found. This association is found maximum and stable for the tropical part of the pattern (approximately 0-20�N). For the gauging stations placed to the southeast of basin some influence from the Pacific Decadal Oscillation (PDO) is found. This method allows evaluating, independently, the decadal and interannual predictability of the streamflow series. In addition, the combination of both modelling techniques gives as result a methodology that has the capacity to provide basin-specific hydroclimatic predictions which vary (for the 1990-2003 validation period) between 62% for the Basque-Cantabrian region, 76% for the Southern Mediterranean and 81% for the Pyrenees. In summary, this work shows the existence of a valuable decadal and interannual predictability of the Ebro streamflow, a result which may be useful to water resources management.
G�miz-Fortis, S. R.; Hidalgo-Mu�oz, J. M.; Arg�eso, D.; Esteban-Parra, M. J.; Castro-D�ez, Y.
Pushing Structural Information into the Yeast Interactome by High-Throughput Protein Docking Experiments
The last several years have seen the consolidation of high-throughput proteomics initiatives to identify and characterize protein interactions and macromolecular complexes in model organisms. In particular, more that 10,000 high-confidence protein-protein interactions have been described between the roughly 6,000 proteins encoded in the budding yeast genome (Saccharomyces cerevisiae). However, unfortunately, high-resolution three-dimensional structures are only available for less than one hundred of these interacting pairs. Here, we expand this structural information on yeast protein interactions by running the first-ever high-throughput docking experiment with some of the best state-of-the-art methodologies, according to our benchmarks. To increase the coverage of the interaction space, we also explore the possibility of using homology models of varying quality in the docking experiments, instead of experimental structures, and assess how it would affect the global performance of the methods. In total, we have applied the docking procedure to 217 experimental structures and 1,023 homology models, providing putative structural models for over 3,000 protein-protein interactions in the yeast interactome. Finally, we analyze in detail the structural models obtained for the interaction between SAM1-anthranilate synthase complex and the MET30-RNA polymerase III to illustrate how our predictions can be straightforwardly used by the scientific community. The results of our experiment will be integrated into the general 3D-Repertoire pipeline, a European initiative to solve the structures of as many as possible protein complexes in yeast at the best possible resolution. All docking results are available at http://gatealoy.pcb.ub.es/HT_docking/.
Mosca, Roberto; Pons, Carles; Fernandez-Recio, Juan; Aloy, Patrick
Construction and application of a protein and genetic interaction network (yeast interactome)
Cytoscape is a bioinformatic data analysis and visualization platform that is well-suited to the analysis of gene expression data. To facilitate the analysis of yeast microarray data using Cytoscape, we constructed an interaction network (interactome) using the curated interaction data available from the Saccharomyces Genome Database (www.yeastgenome.org) and the database of yeast transcription factors at YEASTRACT (www.yeastract.com). These data were formatted and imported into Cytoscape using semi-automated methods, including Linux-based scripts, that simplified the process while minimizing the introduction of processing errors. The methods described for the construction of this yeast interactome are generally applicable to the construction of any interactome. Using Cytoscape, we illustrate the use of this interactome through the analysis of expression data from a recent yeast diauxic shift experiment. We also report and briefly describe the complex associations among transcription factors that result in the regulation of thousands of genes through coordinated changes in expression of dozens of transcription factors. These cells are thus able to sensitively regulate cellular metabolism in response to changes in genetic or environmental conditions through relatively small changes in the expression of large numbers of genes, affecting the entire yeast metabolome.
Stuart, Gregory R.; Copeland, William C.; Strand, Micheline K.
Visualisation and graph-theoretic analysis of a large-scale protein structural interactome
Background: Large-scale protein interaction maps provide a new, global perspective with which to analyse protein function. PSIMAP, the Protein Structural Interactome Map, is a database of all the structurally observed interactions between superfamilies of protein domains with known three-dimensional structure in the PDB. PSIMAP incorporates both functional and evolutionary information into a single network. Results: We present a global analysis
Dan M. Bolser; Panos Dafas; Richard Harrington; Jong Park; Michael Schroeder
High-quality binary protein interaction map of the yeast interactome network.
Current yeast interactome network maps contain several hundred molecular complexes with limited and somewhat controversial representation of direct binary interactions. We carried out a comparative quality assessment of current yeast interactome data sets, demonstrating that high-throughput yeast two-hybrid (Y2H) screening provides high-quality binary interaction information. Because a large fraction of the yeast binary interactome remains to be mapped, we developed an empirically controlled mapping framework to produce a "second-generation" high-quality, high-throughput Y2H data set covering approximately 20% of all yeast binary interactions. Both Y2H and affinity purification followed by mass spectrometry (AP/MS) data are of equally high quality but of a fundamentally different and complementary nature, resulting in networks with different topological and biological properties. Compared to co-complex interactome models, this binary map is enriched for transient signaling interactions and intercomplex connections with a highly significant clustering between essential proteins. Rather than correlating with essentiality, protein connectivity correlates with genetic pleiotropy. PMID:18719252
Yu, Haiyuan; Braun, Pascal; Yildirim, Muhammed A; Lemmens, Irma; Venkatesan, Kavitha; Sahalie, Julie; Hirozane-Kishikawa, Tomoko; Gebreab, Fana; Li, Na; Simonis, Nicolas; Hao, Tong; Rual, Jean-Fran�ois; Dricot, Am�lie; Vazquez, Alexei; Murray, Ryan R; Simon, Christophe; Tardivo, Leah; Tam, Stanley; Svrzikapa, Nenad; Fan, Changyu; de Smet, Anne-Sophie; Motyl, Adriana; Hudson, Michael E; Park, Juyong; Xin, Xiaofeng; Cusick, Michael E; Moore, Troy; Boone, Charlie; Snyder, Michael; Roth, Frederick P; Barab�si, Albert-L�szl�; Tavernier, Jan; Hill, David E; Vidal, Marc
Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets
We present the first analysis of the human proteome with regard to interactions between proteins. We also compare the human interactome with the available interaction datasets from yeast (Saccharomyces cerevisiae), worm (Caenorhabditis elegans) and fly (Drosophila melanogaster). Of >70,000 binary interactions, only 42 were common to human, worm and fly, and only 16 were common to all four datasets. An
T K B Gandhi; Jun Zhong; Suresh Mathivanan; L Karthick; K N Chandrika; S Sujatha Mohan; Salil Sharma; Stefan Pinkert; Shilpa Nagaraju; Balamurugan Periaswamy; Goparani Mishra; Kannabiran Nandakumar; Beiyi Shen; Nandan Deshpande; Rashmi Nayak; Malabika Sarker; Jef D Boeke; Giovanni Parmigiani; J�rg Schultz; Joel S Bader; Akhilesh Pandey
The interactomes of POU5F1 and SOX2 enhancers in human embryonic stem cells
The genes POU5F1 and SOX2 are critical for pluripotency and reprogramming, yet the chromosomal organization around these genes remains poorly understood. We assayed long-range chromosomal interactions on putative enhancers of POU5F1 and SOX2 genes in human embryonic stem cells (hESCs) using 4C-Seq technique. We discovered that their frequent interacting regions mainly overlap with early DNA replication domains. The interactomes are associated with active histone marks and enriched with 5-hydroxymethylcytosine sites. In hESCs, genes within the interactomes have elevated expression. Additionally, some genes associated with the POU5F1 enhancer contribute to pluripotency. Binding sites for multiple DNA binding proteins, including ATF3, CTCF, GABPA, JUND, NANOG, RAD21 and YY1, are enriched in both interactomes. The RARG locus, frequently interacting with the POU5F1 locus, has abundant RAD21 binding sites co-localized with other protein binding sites. Thus the interactomes of these two pluripotency genes could be an important part of the regulatory network in hESCs.
Gao, Fan; Wei, Zong; An, Woojin; Wang, Kai; Lu, Wange
In cell scalaradial interactome profiling using a bio-orthogonal clickable probe.
A bio-orthogonal click-chemistry procedure was developed to allow the in cell interactome profiling of scalaradial, an anti-inflammatory marine natural product. The results were validated through the application of the classical in vitro chemical proteomics and several bio-physical methods; peroxiredoxins, 14-3-3 isoforms and proteasomes were recognized as main scalaradial targets. PMID:24769547
Cassiano, C; Margarucci, L; Esposito, R; Riccio, R; Tosco, A; Casapullo, A; Monti, M C
Interactomics profiling of the negative regulatory function of carbon monoxide on RANKL-treated RAW 264.7 cells during osteoclastogenesis
Background During osteoclastogenesis, the maturation of osteoclast (OC) progenitors is stimulated by the receptor activator of nuclear factor-?B ligand (RANKL). Excess OC production plays a critical role in the pathogenesis of inflammatory bone disorders. Conversely, the inhibition of abnormal OC proliferation reduces inflammation-induced bone loss. Low concentrations of carbon monoxide (CO) are known to decrease inflammation and OC-mediated bone erosion but the molecular mechanism is unknown. Results To obtain insight into the biological function of CO, cultured RANKL-treated RAW 264.7 cells were used in an in vitro experimental model of osteoclastogenesis. The results showed that CO inhibited: 1) tartrate-resistant acid phosphatase (TRAP)-positive cell formation; 2) F-actin ring production; 3) c-fos pathway activation; 4) the expression of cathepsin K, TRAP, calcitonin receptor, and matrix metalloproteinase-9 mRNAs; 5) the expression of nuclear factor of activated T cells, cytoplasmic, calcineurin-dependent 1 in translation. Protein-protein interaction analysis predicted mitogen-activated protein kinase kinase kinase 4 as the controlling hub. Conclusions Low-concentrations of CO (250�ppm) may inhibit osteoclastogenesis. Data from STRING- and IPA-based interactome analyses suggested that the expression of proteins with the functions of signal transduction, enzymes, and epigenetic regulation are significantly altered by CO during RANKL-induced osteoclastogenesis. Our study provides the first interactome analysis of osteoclastogenesis, the results of which supported the negative regulation of OC differentiation by CO.
A "Candidate-Interactome" Aggregate Analysis of Genome-Wide Association Data in Multiple Sclerosis
Though difficult, the study of gene-environment interactions in multifactorial diseases is crucial for interpreting the relevance of non-heritable factors and prevents from overlooking genetic associations with small but measurable effects. We propose a �candidate interactome� (i.e. a group of genes whose products are known to physically interact with environmental factors that may be relevant for disease pathogenesis) analysis of genome-wide association data in multiple sclerosis. We looked for statistical enrichment of associations among interactomes that, at the current state of knowledge, may be representative of gene-environment interactions of potential, uncertain or unlikely relevance for multiple sclerosis pathogenesis: Epstein-Barr virus, human immunodeficiency virus, hepatitis B virus, hepatitis C virus, cytomegalovirus, HHV8-Kaposi sarcoma, H1N1-influenza, JC virus, human innate immunity interactome for type I interferon, autoimmune regulator, vitamin D receptor, aryl hydrocarbon receptor and a panel of proteins targeted by 70 innate immune-modulating viral open reading frames from 30 viral species. Interactomes were either obtained from the literature or were manually curated. The P values of all single nucleotide polymorphism mapping to a given interactome were obtained from the last genome-wide association study of the International Multiple Sclerosis Genetics Consortium & the Wellcome Trust Case Control Consortium, 2. The interaction between genotype and Epstein Barr virus emerges as relevant for multiple sclerosis etiology. However, in line with recent data on the coexistence of common and unique strategies used by viruses to perturb the human molecular system, also other viruses have a similar potential, though probably less relevant in epidemiological terms.
Policano, Claudia; Annibali, Viviana; Coarelli, Giulia; Ricigliano, Vito A. G.; Vittori, Danila; Fornasiero, Arianna; Buscarinu, Maria Chiara; Romano, Silvia; Salvetti, Marco; Ristori, Giovanni
Contrast Adaptation Implies Two Spatiotemporal Channels but Three Adapting Processes
The contrast gain control model of adaptation predicts that the effects of contrast adaptation correlate with contrast sensitivity. This article reports that the effects of high contrast spatiotemporal adaptors are maximum when adapting around 19 Hz, which is a factor of two or more greater than the peak in contrast sensitivity. To explain the discrepancy, the predictions made by parallel
Keith Langley; Peter J. Bex
An integrative in silico approach for discovering candidates for drug-targetable protein-protein interactions in interactome data
BACKGROUND: Protein-protein interactions (PPIs) are challenging but attractive targets for small chemical drugs. Whole PPIs, called the 'interactome', have been emerged in several organisms, including human, based on the recent development of high-throughput screening (HTS) technologies. Individual PPIs have been targeted by small drug-like chemicals (SDCs), however, interactome data have not been fully utilized for exploring drug targets due to
Nobuyoshi Sugaya; Kazuyoshi Ikeda; Toshiyuki Tashiro; Shizu Takeda; Jun Otomo; Yoshiko Ishida; Akiko Shiratori; Atsushi Toyoda; Hideki Noguchi; Tadayuki Takeda; Satoru Kuhara; Yoshiyuki Sakaki; Takao Iwayanagi
Mycobacterium tuberculosis interactome analysis unravels potential pathways to drug resistance
Background Emergence of drug resistant varieties of tuberculosis is posing a major threat to global tuberculosis eradication programmes. Although several approaches have been explored to counter resistance, there has been limited success due to a lack of understanding of how resistance emerges in bacteria upon drug treatment. A systems level analysis of the proteins involved is essential to gain insights into the routes required for emergence of drug resistance. Results We derive a genome-scale protein-protein interaction network for Mycobacterium tuberculosis H37Rv from the STRING database, with proteins as nodes and interactions as edges. A set of proteins involved in both intrinsic and extrinsic drug resistance mechanisms are identified from literature. We then compute shortest paths from different drug targets to the set of resistance proteins in the protein-protein interactome, to derive a sub-network relevant to study emergence of drug resistance. The shortest paths are then scored and ranked based on a new scheme that considers (a) drug-induced gene upregulation data, from microarray experiments reported in literature, for the individual nodes and (b) edge-hubness, a network parameter which signifies centrality of a given edge in the network. High-scoring paths identified from this analysis indicate most plausible pathways for the emergence of drug resistance. Different targets appear to have different propensities for four drug resistance mechanisms. A new concept of 'co-targets' has been proposed to counter drug resistance, co-targets being defined as protein(s) that need to be simultaneously inhibited along with the intended target(s), to check emergence of resistance to a given drug. Conclusion The study leads to the identification of possible pathways for drug resistance, providing novel insights into the problem of resistance. Knowledge of important proteins in such pathways enables identification of appropriate 'co-targets', best examples being RecA, Rv0823c, Rv0892 and DnaE1, for drugs targeting the mycolic acid pathway. Insights obtained about the propensity of a drug to trigger resistance will be useful both for more careful identification of drug targets as well as to identify target-co-target pairs, both implementable in early stages of drug discovery itself. This approach is also inherently generic, likely to significantly impact drug discovery.
Raman, Karthik; Chandra, Nagasuma
Indeterminacy of spatiotemporal cardiac alternans.
Cardiac alternans, a beat-to-beat alternation in action potential duration (at the cellular level) or in electrocardiogram morphology (at the whole heart level), is a marker of ventricular fibrillation, a fatal heart rhythm that kills hundreds of thousands of people in the United States each year. Investigating cardiac alternans may lead to a better understanding of the mechanisms of cardiac arrhythmias and eventually better algorithms for the prediction and prevention of such dreadful diseases. In paced cardiac tissue, alternans develops under increasingly shorter pacing period. Existing experimental and theoretical studies adopt the assumption that alternans in homogeneous cardiac tissue is exclusively determined by the pacing period. In contrast, we find that, when calcium-driven alternans develops in cardiac fibers, it may take different spatiotemporal patterns depending on the pacing history. Because there coexist multiple alternans solutions for a given pacing period, the alternans pattern on a fiber becomes unpredictable. Using numerical simulation and theoretical analysis, we show that the coexistence of multiple alternans patterns is induced by the interaction between electrotonic coupling and an instability in calcium cycling. PMID:18763977
Zhao, Xiaopeng
Towards Personalized Medicine Mediated by in Vitro Virus-Based Interactome Approaches
We have developed a simple in vitro virus (IVV) selection system based on cell-free co-translation, using a highly stable and efficient mRNA display method. The IVV system is applicable to the high-throughput and comprehensive analysis of proteins and protein�ligand interactions. Huge amounts of genomic sequence data have been generated over the last decade. The accumulated genetic alterations and the interactome networks identified within cells represent a universal feature of a disease, and knowledge of these aspects can help to determine the optimal therapy for the disease. The concept of the �integrome� has been developed as a means of integrating large amounts of data. We have developed an interactome analysis method aimed at providing individually-targeted health care. We also consider future prospects for this system.
Ohashi, Hiroyuki; Miyamoto-Sato, Etsuko
Dissecting spatio-temporal protein networks driving human heart development and related disorders
Aberrant organ development is associated with a wide spectrum of disorders, from schizophrenia to congenital heart disease, but systems-level insight into the underlying processes is very limited. Using heart morphogenesis as general model for dissecting the functional architecture of organ development, we combined detailed phenotype information from deleterious mutations in 255 genes with high-confidence experimental interactome data, and coupled the results to thorough experimental validation. Hereby, we made the first systematic analysis of spatio-temporal protein networks driving many stages of a developing organ identifying several novel signaling modules. Our results show that organ development relies on surprisingly few, extensively recycled, protein modules that integrate into complex higher-order networks. This design allows the formation of a complicated organ using simple building blocks, and suggests how mutations in the same genes can lead to diverse phenotypes. We observe a striking temporal correlation between organ complexity and the number of discrete functional modules coordinating morphogenesis. Our analysis elucidates the organization and composition of spatio-temporal protein networks that drive the formation of organs, which in the future may lay the foundation of novel approaches in treatments, diagnostics, and regenerative medicine.
Lage, Kasper; M?llgard, Kjeld; Greenway, Steven; Wakimoto, Hiroko; Gorham, Joshua M; Workman, Christopher T; Bendsen, Eske; Hansen, Niclas T; Rigina, Olga; Roque, Francisco S; Wiese, Cornelia; Christoffels, Vincent M; Roberts, Amy E; Smoot, Leslie B; Pu, William T; Donahoe, Patricia K; Tommerup, Niels; Brunak, S?ren; Seidman, Christine E; Seidman, Jonathan G; Larsen, Lars A
A surface display yeast two-hybrid screening system for high-throughput protein interactome mapping
Despite the wide acceptance of yeast two-hybrid (Y2H) system for protein�protein interaction analysis and discovery, conventional Y2H assays are not well suited for high-throughput screening of the protein interaction network (�interactome�) on a genomic scale due to several limitations, including labor-intensive agar plating and colony selection methods associated with the use of nutrient selection markers, complicated reporter analysis methods associated
Jun Chen; Jianhong Zhou; Claire K. Sanders; John P. Nolan; Hong Cai
Expression of DISC1-Interactome Members Correlates with Cognitive Phenotypes Related to Schizophrenia
Cognitive dysfunction is central to the schizophrenia phenotype. Genetic and functional studies have implicated Disrupted-in-Schizophrenia 1 (DISC1), a leading candidate gene for schizophrenia and related psychiatric conditions, in cognitive function. Altered expression of DISC1 and DISC1-interactors has been identified in schizophrenia. Dysregulated expression of DISC1-interactome genes might, therefore, contribute to schizophrenia susceptibility via disruption of molecular systems required for normal cognitive function. Here, the blood RNA expression levels of DISC1 and DISC1-interacting proteins were measured in 63 control subjects. Cognitive function was assessed using neuropsychiatric tests and functional magnetic resonance imaging was used to assess the activity of prefrontal cortical regions during the N-back working memory task, which is abnormal in schizophrenia. Pairwise correlations between gene expression levels and the relationship between gene expression levels and cognitive function and N-back-elicited brain activity were assessed. Finally, the expression levels of DISC1, AKAP9, FEZ1, NDEL1 and PCM1 were compared between 63 controls and 69 schizophrenic subjects. We found that DISC1-interactome genes showed correlated expression in the blood of healthy individuals. The expression levels of several interactome members were correlated with cognitive performance and N-back-elicited activity in the prefrontal cortex. In addition, DISC1 and NDEL1 showed decreased expression in schizophrenic subjects compared to healthy controls. Our findings highlight the importance of the coordinated expression of DISC1-interactome genes for normal cognitive function and suggest that dysregulated DISC1 and NDEL1 expression might, in part, contribute to susceptibility for schizophrenia via disruption of prefrontal cortex-dependent cognitive functions.
Rampino, Antonio; Walker, Rosie May; Torrance, Helen Scott; Anderson, Susan Maguire; Fazio, Leonardo; Di Giorgio, Annabella; Taurisano, Paolo; Gelao, Barbara; Romano, Raffaella; Masellis, Rita; Ursini, Gianluca; Caforio, Grazia; Blasi, Giuseppe; Millar, J. Kirsty; Porteous, David John; Thomson, Pippa Ann; Bertolino, Alessandro; Evans, Kathryn Louise
Spatial and Spatiotemporal Data Mining: Recent Advances
Explosive growth in geospatial data and the emergence of new spatial technologies emphasize the need for automated discovery of spatial knowledge. Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial databases. The complexity of spatial data and intrinsic spatial relationships limits the usefulness of conventional data mining techniques for extracting spatial patterns. In this chapter we explore the emerging field of spatial data mining, focusing on four major topics: prediction and classification, outlier detection, co-location mining, and clustering. Spatiotemporal data mining is also briefly discussed.
Shekhar, Shashi [University of Minnesota; Vatsavai, Raju [ORNL; Celik, Mete [University of Minnesota
The Symbiosis Interactome: a computational approach reveals novel components, functional interactions and modules in Sinorhizobium meliloti
Background Rhizobium-Legume symbiosis is an attractive biological process that has been studied for decades because of its importance in agriculture. However, this system has undergone extensive study and although many of the major factors underpinning the process have been discovered using traditional methods, much remains to be discovered. Results Here we present an analysis of the 'Symbiosis Interactome' using novel computational methods in order to address the complex dynamic interactions between proteins involved in the symbiosis of the model bacteria Sinorhizobium meliloti with its plant hosts. Our study constitutes the first large-scale analysis attempting to reconstruct this complex biological process, and to identify novel proteins involved in establishing symbiosis. We identified 263 novel proteins potentially associated with the Symbiosis Interactome. The topology of the Symbiosis Interactome was used to guide experimental techniques attempting to validate novel proteins involved in different stages of symbiosis. The contribution of a set of novel proteins was tested analyzing the symbiotic properties of several S. meliloti mutants. We found mutants with altered symbiotic phenotypes suggesting novel proteins that provide key complementary roles for symbiosis. Conclusion Our 'systems-based model' represents a novel framework for studying host-microbe interactions, provides a theoretical basis for further experimental validations, and can also be applied to the study of other complex processes such as diseases.
Rodriguez-Llorente, Ignacio; Caviedes, Miguel A; Dary, Mohammed; Palomares, Antonio J; Canovas, Francisco M; Peregrin-Alvarez, Jose M
Use of biotinylated ubiquitin for analysis of rat brain mitochondrial proteome and interactome.
Applicability of in vitro biotinylated ubiquitin for evaluation of endogenous ubiquitin conjugation and analysis of ubiquitin-associated protein-protein interactions has been investigated. Incubation of rat brain mitochondria with biotinylated ubiquitin followed by affinity chromatography on avidin-agarose, intensive washing, tryptic digestion of proteins bound to the affinity sorbent and their mass spectrometry analysis resulted in reliable identification of 50 proteins belonging to mitochondrial and extramitochondrial compartments. Since all these proteins were bound to avidin-agarose only after preincubation of the mitochondrial fraction with biotinylated ubiquitin, they could therefore be referred to as specifically bound proteins. A search for specific ubiquitination signature masses revealed several extramitochondrial and intramitochondrial ubiquitinated proteins representing about 20% of total number of proteins bound to avidin-agarose. The interactome analysis suggests that the identified non-ubiquitinated proteins obviously form tight complexes either with ubiquitinated proteins or with their partners and/or mitochondrial membrane components. Results of the present study demonstrate that the use of biotinylated ubiquitin may be considered as the method of choice for in vitro evaluation of endogenous ubiquitin-conjugating machinery in particular subcellular organelles and changes in ubiquitin/organelle associated interactomes. This may be useful for evaluation of changes in interactomes induced by protein ubiquitination under norm and various brain pathologies. PMID:23109873
Buneeva, Olga A; Medvedeva, Marina V; Kopylov, Arthur T; Zgoda, Victor G; Medvedev, Alexei E
Use of Biotinylated Ubiquitin for Analysis of Rat Brain Mitochondrial Proteome and Interactome
Applicability of in vitro biotinylated ubiquitin for evaluation of endogenous ubiquitin conjugation and analysis of ubiquitin-associated protein-protein interactions has been investigated. Incubation of rat brain mitochondria with biotinylated ubiquitin followed by affinity chromatography on avidin-agarose, intensive washing, tryptic digestion of proteins bound to the affinity sorbent and their mass spectrometry analysis resulted in reliable identification of 50 proteins belonging to mitochondrial and extramitochondrial compartments. Since all these proteins were bound to avidin-agarose only after preincubation of the mitochondrial fraction with biotinylated ubiquitin, they could therefore be referred to as specifically bound proteins. A search for specific ubiquitination signature masses revealed several extramitochondrial and intramitochondrial ubiquitinated proteins representing about 20% of total number of proteins bound to avidin-agarose. The interactome analysis suggests that the identified non-ubiquitinated proteins obviously form tight complexes either with ubiquitinated proteins or with their partners and/or mitochondrial membrane components. Results of the present study demonstrate that the use of biotinylated ubiquitin may be considered as the method of choice for in vitro evaluation of endogenous ubiquitin-conjugating machinery in particular subcellular organelles and changes in ubiquitin/organelle associated interactomes. This may be useful for evaluation of changes in interactomes induced by protein ubiquitination under norm and various brain pathologies.
Buneeva, Olga A.; Medvedeva, Marina V.; Kopylov, Arthur T.; Zgoda, Victor G.; Medvedev, Alexei E.
Label-free quantitative proteomic analysis of the YAP/TAZ interactome.
The function of an individual protein is typically defined by protein-protein interactions orchestrating the formation of large complexes critical for a wide variety of biological processes. Over the last decade the analysis of purified protein complexes by mass spectrometry became a key technique to identify protein-protein interactions. We present a fast and straightforward approach for analyses of interacting proteins combining a Flp-in single-copy cellular integration system and single-step affinity purification with single-shot mass spectrometry analysis. We applied this protocol to the analysis of the YAP and TAZ interactome. YAP and TAZ are the downstream effectors of the mammalian Hippo tumor suppressor pathway. Our study provides comprehensive interactomes for both YAP and TAZ and does not only confirm the majority of previously described interactors but, strikingly, revealed uncharacterized interaction partners that affect YAP/TAZ TEAD-dependent transcription. Among these newly identified candidates are Rassf8, thymopoetin, and the transcription factors CCAAT/enhancer-binding protein (C/EBP)?/? and core-binding factor subunit ? (Cbfb). In addition, our data allowed insights into complex stoichiometry and uncovered discrepancies between the YAP and TAZ interactomes. Taken together, the stringent approach presented here could help to significantly sharpen the understanding of protein-protein networks. PMID:24573087
Kohli, Priyanka; Bartram, Malte P; Habbig, Sandra; Pahmeyer, Caroline; Lamkemeyer, Tobias; Benzing, Thomas; Schermer, Bernhard; Rinschen, Markus M
Perturbation of the mutated EGFR interactome identifies vulnerabilities and resistance mechanisms
We hypothesized that elucidating the interactome of epidermal growth factor receptor (EGFR) forms that are mutated in lung cancer, via global analysis of protein�protein interactions, phosphorylation, and systematically perturbing the ensuing network nodes, should offer a new, more systems-level perspective of the molecular etiology. Here, we describe an EGFR interactome of 263 proteins and offer a 14-protein core network critical to the viability of multiple EGFR-mutated lung cancer cells. Cells with acquired resistance to EGFR tyrosine kinase inhibitors (TKIs) had differential dependence of the core network proteins based on the underlying molecular mechanisms of resistance. Of the 14 proteins, 9 are shown to be specifically associated with survival of EGFR-mutated lung cancer cell lines. This included EGFR, GRB2, MK12, SHC1, ARAF, CD11B, ARHG5, GLU2B, and CD11A. With the use of a drug network associated with the core network proteins, we identified two compounds, midostaurin and lestaurtinib, that could overcome drug resistance through direct EGFR inhibition when combined with erlotinib. Our results, enabled by interactome mapping, suggest new targets and combination therapies that could circumvent EGFR TKI resistance.
Li, Jiannong; Bennett, Keiryn; Stukalov, Alexey; Fang, Bin; Zhang, Guolin; Yoshida, Takeshi; Okamoto, Isamu; Kim, Jae-Young; Song, Lanxi; Bai, Yun; Qian, Xiaoning; Rawal, Bhupendra; Schell, Michael; Grebien, Florian; Winter, Georg; Rix, Uwe; Eschrich, Steven; Colinge, Jacques; Koomen, John; Superti-Furga, Giulio; Haura, Eric B
Spatio-temporal soil moisture distribution in a Maize field
The spatio-temporal distribution of water content is important for predicting water flow and solute transport in the unsaturated zone. In a cropped field, this distribution is affected by the interception and redistribution of water by the plants, by surface runoff, by root water uptake, and by the distribution of soil hydraulic properties and boundary conditions of the system. This study
Laure Beff; Valentin Couvreur; Mathieu Javaux
Spatiotemporal exploratory models for broad-scale survey data.
The distributions of animal populations change and evolve through time. Migratory species exploit different habitats at different times of the year. Biotic and abiotic features that determine where a species lives vary due to natural and anthropogenic factors. This spatiotemporal variation needs to be accounted for in any modeling of species' distributions. In this paper we introduce a semiparametric model that provides a flexible framework for analyzing dynamic patterns of species occurrence and abundance from broad-scale survey data. The spatiotemporal exploratory model (STEM) adds essential spatiotemporal structure to existing techniques for developing species distribution models through a simple parametric structure without requiring a detailed understanding of the underlying dynamic processes. STEMs use a multi-scale strategy to differentiate between local and global-scale spatiotemporal structure. A user-specified species distribution model accounts for spatial and temporal patterning at the local level. These local patterns are then allowed to "scale up" via ensemble averaging to larger scales. This makes STEMs especially well suited for exploring distributional dynamics arising from a variety of processes. Using data from eBird, an online citizen science bird-monitoring project, we demonstrate that monthly changes in distribution of a migratory species, the Tree Swallow (Tachycineta bicolor), can be more accurately described with a STEM than a conventional bagged decision tree model in which spatiotemporal structure has not been imposed. We also demonstrate that there is no loss of model predictive power when a STEM is used to describe a spatiotemporal distribution with very little spatiotemporal variation; the distribution of a nonmigratory species, the Northern Cardinal (Cardinalis cardinalis). PMID:21265447
Fink, Daniel; Hochachka, Wesley M; Zuckerberg, Benjamin; Winkler, David W; Shaby, Ben; Munson, M Arthur; Hooker, Giles; Riedewald, Mirek; Sheldon, Daniel; Kelling, Steve
Spatiotemporal dynamics of HIV infection
NASA Astrophysics Data System (ADS)
Mathematical models of the dynamics of infection with the human immunodeficiency virus (HIV) have contributed to tremendous advances over the past 20 years. This thesis extends this previous work by exploring the importance of spatial heterogeneity in HIV infection both in vitro and in vivo in patients treated with highly-active antiretroviral therapy. Viral infections propagate locally in space, yet HIV infection has been widely regarded as equilibrated over the entire body of an infected patient. This dissertation constructs and explores a cellular automata model of viral spread at the cellular level. Coupling the automata to a blood compartment represented by a differential equation leads to a whole-body model of HIV infection that explicitly includes spatial effects at both the cellular and tissue levels. These models are tested by comparison with experimental data. A central prediction of the spatial model is that, due to competition between Brownian motion and viral lability, HIV infectivity increases with target cell density. This production is verified in a series of in vitro experiments in cell culture. The predicted independence of inhibitory concentrations of antiretoviral agents is verified for nevirapine, but azidothymidine inhibits HIV replication less efficiently in more dense cultures. These in vitro results suggest that systems allowing cell concentrations closer to tissue densities would better reflect virus replication kinetics, although standard measures of relative drug susceptibility may accurately reflect in vivo conditions. The coupled spatial model of in vivo dynamics is compared with novel mathematical analysis of experiments in HIV-infected patients. These analyses indicate that HIV DNA provides a useful marker of the size of long-lived cellular reservoirs of HIV. Levels of HIV DNA in peripheral blood are predictive of the average rate of residual virus production after years of treatment, regardless of whether patients initiate therapy during the primary or chronic stages of HIV infection. The nonlinear clearance of HIV DNA therefore predicts lifelong virus production, even in treated patients. Collectively, these results demonstrate that important features of the spatiotemporal dynamics of HIV infection both in vitro and in vivo are best explained with explicit spatial models.
Strain, Matthew Carl
Impaired autophagy and APP processing in Alzheimer's disease: The potential role of Beclin 1 interactome.
The accumulation of amyloid-?-containing neuritic plaques and intracellular tau protein tangles are key histopathological hallmarks of Alzheimer's disease (AD). This type of pathology clearly indicates that the mechanisms of neuronal housekeeping and protein quality control are compromised in AD. There is mounting evidence that the autophagosome-lysosomal degradation is impaired, which could disturb the processing of APP and provoke AD pathology. Beclin 1 is a molecular platform assembling an interactome with stimulating and suppressive components which regulate the initiation of the autophagosome formation. Recent studies have indicated that the expression Beclin 1 is reduced in AD brain. Moreover, the deficiency of Beclin 1 in cultured neurons and transgenic mice provokes the deposition of amyloid-? peptides whereas its overexpression reduces the accumulation of amyloid-?. There are several potential mechanisms, which could inhibit the function of Beclin 1 interactome and thus impair autophagy and promote AD pathology. The mechanisms include (i) reduction of Beclin 1 expression or its increased proteolytic cleavage by caspases, (ii) sequestration of Beclin 1 to non-functional locations, such as tau tangles, (iii) formation of inhibitory complexes between Beclin 1 and antiapoptotic Bcl-2 proteins or inflammasomes, (iv) interaction of Beclin 1 with inhibitory neurovirulent proteins, e.g. herpex simplex ICP34.5, or (v) inhibition of the Beclin 1/Vps34 complex through the activation of CDK1 and CDK5. We will shortly introduce the function of Beclin 1 interactome in autophagy and phagocytosis, review the recent evidence indicating that Beclin 1 regulates autophagy and APP processing in AD, and finally examine the potential mechanisms through which Beclin 1 dysfunction could be involved in the pathogenesis of AD. PMID:23827971
Salminen, Antero; Kaarniranta, Kai; Kauppinen, Anu; Ojala, Johanna; Haapasalo, Annakaisa; Soininen, Hilkka; Hiltunen, Mikko
The PI(3,5)P2 and PI(4,5)P2 interactomes.
A comprehensive analysis of the phosphoinositide interactome has been performed using analogues of PI(3,5)P2 and PI(4,5)P2 phosphatidyl phospholipids which were immobilized onto Affi-10 beads or incorporated into liposomes for use as affinity absorbents with cytosolic extracts from colonic carcinoma cell lines. Affinity/LC/MS/MS experiments allowed identification of 388 proteins/protein complexes that appeared to interact specifically with the phosphoinositide targets: a number of novel potential phosphoinositide interacting proteins have been identified. PMID:19367725
Catimel, Bruno; Schieber, Christine; Condron, Melanie; Patsiouras, Heather; Connolly, Lisa; Catimel, Jenny; Nice, Edouard C; Burgess, Antony W; Holmes, Andrew B
Spatiotemporal Wave Patterns: Information Dynamics
Pattern formation has traditionally been studied in non-equilibrium physics from the viewpoint of describing the basic structures and their interactions. While this is still an important area of research, the emphasis in the last few years has shifted towards analysis of specific properties of patterns in various complex media. For example, diverse and unexpected phenomena occur in neuro-like media that are characterized by highly non-trivial local dynamics. We carried out an active research program on analysis of spatio-temporal patterns in various physical systems (convection, oscillating fluid layer, soap film), as well as in neuro-like media, with an emphasis on informational aspects of the dynamics. Nonlinear nonequilibrium media and their discrete analogs have a unique ability to represent, memorize, and process the information contained in spatio-temporal patterns. Recent neurophysiological experiments demonstrated a certain universality of spatio-temporal representation of information by neural ensembles. Information processing is also revealed in the spatio-temporal dynamics of cellular patterns in nonequilibrium media. It is extremely important for many applications to study the informational aspects of these dynamics, including the origins and mechanisms of information generation, propagation and storage. Some of our results are: the discovery of self-organization of periodically oscillatory patterns in chaotic heterogeneous media; the analysis of the propagation of the information along a chaotic media as function of the entropy of the signal; the analysis of wave propagation in discrete non-equilibrium media with autocatalytic properties, which simulates the calcium dynamics in cellular membranes. Based on biological experiments we suggest the mechanism by which the spatial sensory information is transferred into the spatio-temporal code in the neural media. We also found a new mechanism of self-pinning in cellular structures and the related phenomenon of stick-slip front motion.
Mikhail Rabinovich; Lev Tsimring
Cross-Species Protein Interactome Mapping Reveals Species-Specific Wiring of Stress-Response Pathways1
The fission yeast Schizosaccharomyces pombe has more metazoan-like features than the budding yeast Saccharomyces cerevisiae, yet it has similarly facile genetics. Here, we present a large-scale verified binary protein-protein interactome network, �StressNet�, based on high-throughput yeast two-hybrid screens of interacting proteins classified as part of stress-response and signal transduction pathways in S. pombe. We performed systematic, cross-species interactome mapping using StressNet and a protein interactome network of orthologous proteins in S. cerevisiae. With cross-species comparative network studies, we detected a previously unidentified component (Snr1) of the S. pombe mitogen-activated protein kinase Sty1 pathway. Coimmunoprecipitation experiments showed that Snr1 interacted with Sty1 and that deletion of snr1 increased the sensitivity of S. pombe cells to stress. Comparison of StressNet with the interactome network of orthologous proteins in S. cerevisiae showed that the majority of interactions among these stress-response and signaling proteins are not conserved between species, but are �rewired;� orthologous proteins have different binding partners in both species. In particular, transient interactions connecting proteins in different functional modules were more likely to be rewired than conserved. By directly testing interactions between proteins in one yeast species and their corresponding binding partners in the other yeast species with yeast two-hybrid assays, we found that about half of the interactions traditionally considered �conserved� form modified interaction interfaces that may potentially accommodate novel functions.
Wei, Xiaomu; Mellor, Joseph C.; Tong, Virginia; Degatano, Andrew G.; Wang, Xiujuan; Wang, Lihua; Cordero, Nicolas A.; Kruer-Zerhusen, Nathan; Matsuyama, Akihisa; Pleiss, Jeffrey A.; Lipkin, Steven M.; Yoshida, Minoru; Roth, Frederick P.; Yu, Haiyuan
Characterization of carbonic anhydrase IX interactome reveals proteins assisting its nuclear localization in hypoxic cells.
Carbonic anhydrase IX (CA IX) is a transmembrane protein affecting pH regulation, cell migration/invasion, and survival in hypoxic tumors. Although the pathways related to CA IX have begun to emerge, molecular partners mediating its functions remain largely unknown. Here we characterize the CA IX interactome in hypoxic HEK-293 cells. Most of the identified CA IX-binding partners contain the HEAT/ARM repeat domain and belong to the nuclear transport machinery. We show that the interaction with two of these proteins, namely XPO1 exportin and TNPO1 importin, occurs via the C-terminal region of CA IX and increases with protein phosphorylation. We also demonstrate that nuclear CA IX is enriched in hypoxic cells and is present in renal cell carcinoma tissues. These data place CA IX among the cell-surface signal transducers undergoing nuclear translocation. Accordingly, CA IX interactome involves also CAND1, which participates in both gene transcription and assembly of SCF ubiquitin ligase complexes. It is noteworthy that down-regulation of CAND1 leads to decreased CA IX protein levels apparently via affecting its stability. Our findings provide the first evidence that CA IX interacts with proteins involved in nuclear/cytoplasmic transport, gene transcription, and protein stability, and suggest the existence of nuclear CA IX protein subpopulations with a potential intracellular function, distinct from the crucial CA IX role at the cell surface. PMID:23181366
Buanne, Pasquale; Renzone, Giovanni; Monteleone, Francesca; Vitale, Monica; Monti, Simona Maria; Sandomenico, AnnaMaria; Garbi, Corrado; Montanaro, Donatella; Accardo, Marina; Troncone, Giancarlo; Zatovicova, Miriam; Csaderova, Lucia; Supuran, Claudiu T; Pastorekova, Silvia; Scaloni, Andrea; De Simone, Giuseppina; Zambrano, Nicola
Clathrin complexes with the inhibitor kappa B kinase signalosome: imaging the interactome.
Many receptors involved with innate immunity activate the inhibitor kappa B kinase signalosome (IKK). The active complex appears to be assembled from the two kinase units, IKK? and IKK? with the regulatory protein NEMO. Because we previously found that RNA silencing of clathrin heavy chains (CHC), in transformed human lung pneumocytes (A549), decreased TNF?-induced signaling and phosphorylation of inhibitor kappa B (I?B), we hypothesized that CHC forms cytoplasmic complexes with members of the IKK signalosome. Widely available antibodies were used to immunoprecipitate IKK? and NEMO interactomes. Analysis of the affinity interactomes by mass spectrometry detected clathrin with both baits with high confidence. Using the same antibodies for indirect digital immunofluorescence microscopy and FRET, the CHC-IKK complexes were visualized together with NEMO or HSP90. The natural variability of protein amounts in unsynchronized A549 cells was used to obtain statistical correlation for several complexes, at natural levels and without invasive labeling. Analyses of voxel numbers indicated that: (i) CHC-IKK complexes are not part of the IKK signalosome itself but, likely, precursors of IKK-NEMO complexes. (ii) CHC-IKK? complexes may arise from IKK?-HSP90 complexes. PMID:24994893
Gamboni, Fabia; Escobar, Guillermo A; Moore, Ernest E; Dzieciatkowska, Monika; Hansen, Kirk C; Mitra, Sanchayita; Nydam, Trevor A; Silliman, Christopher C; Banerjee, Anirban
Spatiotemporally varying visual hallucinations: II. Spectral classification and comparison with theory.
In order to better understand the nature of visual hallucinations, and to test predictions of spatiotemporally oscillating hallucinations from a recent corticothalamic model of visual dynamics, clinical descriptions of hallucinations are used to establish boundaries on the spatiotemporal frequencies observed in various disorders. Detailed comparisons with hallucinations during migraine aura demonstrate that key features are consistent with corticothalamic origin and specific abnormalities, but underline the need for more detailed quantitative data to be obtained on temporally oscillating hallucinations more generally. PMID:24874516
Henke, H; Robinson, P A; Drysdale, P M
Dynamics of Turing Patterns under Spatiotemporal Forcing
NASA Astrophysics Data System (ADS)
We study, both theoretically and experimentally, the dynamical response of Turing patterns to a spatiotemporal forcing in the form of a traveling-wave modulation of a control parameter. We show that from strictly spatial resonance, it is possible to induce new, generic dynamical behaviors, including temporally modulated traveling waves and localized traveling solitonlike solutions. The latter make contact with the soliton solutions of Coullet [
R�diger, S.; M�guez, D. G.; Mu�uzuri, A. P.; Sagu�s, F.; Casademunt, J.
Finding Spatio-Temporal Patterns in Earth Science Data
This paper presents preliminary work in using data mining techniques to find interesting spatio-temporal patterns from Earth Science data. The data consists of time series measurements for various Earth science and climate variables (e.g. soil moisture, temperature, and precipitation), along with additional data from existing ecosystem models (e.g. Net Primary Production). The ecological patterns of interest include associations, clusters, predictive
Pang-Ning Tan; Michael Steinbach; Vipin Kumar; Christopher Potter; Steven Klooster; Alicia Torregrosa
Mercury Toolset for Spatiotemporal Metadata
Mercury (http:\\/\\/mercury.ornl.gov) is a set of tools for federated harvesting, searching, and retrieving metadata, particularly spatiotemporal metadata. Version 3.0 of the Mercury toolset provides orders of magnitude improvements in search speed, support for additional metadata formats, integration with Google Maps for spatial queries, facetted type search, support for RSS (Really Simple Syndication) delivery of search results, and enhanced customization to
Ranjeet Devarakonda; Giri Palanisamy; James Green; Bruce Wilson; B. Timothy Rhyne; Chris Lindsley
A global S. cerevisiae small ubiquitin-related modifier (SUMO) system interactome.
The small ubiquitin-related modifier (SUMO) system has been implicated in a number of biological functions, yet the individual components of the SUMO machinery involved in each of these activities were largely unknown. Here we report the first global SUMO system interactome. Using affinity purification coupled with mass spectrometry, we identify >450 protein-protein interactions surrounding the SUMO E2, Siz type E3s and SUMO-specific proteases in budding yeast. Exploiting this information-rich resource, we validate several Siz1- and Siz2-specific substrates, identify a nucleoporin required for proper Ulp1 localization, and uncover important new roles for Ubc9 and Ulp2 in the maintenance of ribosomal DNA. PMID:23712011
Srikumar, Tharan; Lewicki, Megan C; Raught, Brian
A global S. cerevisiae small ubiquitin-related modifier (SUMO) system interactome
The small ubiquitin-related modifier (SUMO) system has been implicated in a number of biological functions, yet the individual components of the SUMO machinery involved in each of these activities were largely unknown. Here we report the first global SUMO system interactome. Using affinity purification coupled with mass spectrometry, we identify >450 protein�protein interactions surrounding the SUMO E2, Siz type E3s and SUMO-specific proteases in budding yeast. Exploiting this information-rich resource, we validate several Siz1- and Siz2-specific substrates, identify a nucleoporin required for proper Ulp1 localization, and uncover important new roles for Ubc9 and Ulp2 in the maintenance of ribosomal DNA.
Srikumar, Tharan; Lewicki, Megan C; Raught, Brian
A Systems Biology Approach for the Investigation of the Heparin/Heparan Sulfate Interactome*
A large body of evidence supports the involvement of heparan sulfate (HS) proteoglycans in physiological processes such as development and diseases including cancer and neurodegenerative disorders. The role of HS emerges from its ability to interact and regulate the activity of a vast number of extracellular proteins including growth factors and extracellular matrix components. A global view on how protein-HS interactions influence the extracellular proteome and, consequently, cell function is currently lacking. Here, we systematically investigate the functional and structural properties that characterize HS-interacting proteins and the network they form. We collected 435 human proteins interacting with HS or the structurally related heparin by integrating literature-derived and affinity proteomics data. We used this data set to identify the topological features that distinguish the heparin/HS-interacting network from the rest of the extracellular proteome and to analyze the enrichment of gene ontology terms, pathways, and domain families in heparin/HS-binding proteins. Our analysis revealed that heparin/HS-binding proteins form a highly interconnected network, which is functionally linked to physiological and pathological processes that are characteristic of higher organisms. Therefore, we then investigated the existence of a correlation between the expansion of domain families characteristic of the heparin/HS interactome and the increase in biological complexity in the metazoan lineage. A strong positive correlation between the expansion of the heparin/HS interactome and biosynthetic machinery and organism complexity emerged. The evolutionary role of HS was reinforced by the presence of a rudimentary HS biosynthetic machinery in a unicellular organism at the root of the metazoan lineage.
Ori, Alessandro; Wilkinson, Mark C.; Fernig, David G.
Decomposing spatiotemporal brain patterns into topographic latent sources.
This paper extends earlier work on spatial modeling of fMRI data to the temporal domain, providing a framework for analyzing high temporal resolution brain imaging modalities such as electroencapholography (EEG). The central idea is to decompose brain imaging data into a covariate-dependent superposition of functions defined over continuous time and space (what we refer to as topographic latent sources). The continuous formulation allows us to parametrically model spatiotemporally localized activations. To make group-level inferences, we elaborate the model hierarchically by sharing sources across subjects. We describe a variational algorithm for parameter estimation that scales efficiently to large data sets. Applied to three EEG data sets, we find that the model produces good predictive performance and reproduces a number of classic findings. Our results suggest that topographic latent sources serve as an effective hypothesis space for interpreting spatiotemporal brain imaging data. PMID:24791745
Gershman, Samuel J; Blei, David M; Norman, Kenneth A; Sederberg, Per B
Spatio-temporal analysis of Salmonella surveillance data in Thailand.
SUMMARY This study evaluates the usefulness of spatio-temporal statistical tools to detect outbreaks using routine surveillance data where limited epidemiological information is available. A dataset from 2002 to 2007 containing information regarding date, origin, source and serotype of 29�586 Salmonella isolates from Thailand was analysed. Data was grouped into human and non-human categories and the analysis was performed for the top five occurring serovars for each year of the study period. A total 91 human and 39 non-human significant spatio-temporal clusters were observed, accounting for 11% and 16% of the isolates, respectively. Serovar-specific associations between human and non-human clusters were also evaluated. Results show that these statistical tools can provide information for use in outbreak prevention and detection, in countries where only limited data is available. Moreover, it is suggested that monitoring non-human reservoirs can be relevant in predicting future Salmonella human cases. PMID:24103334
Domingues, A R; Vieira, A R; Hendriksen, R S; Pulsrikarn, C; Aarestrup, F M
Precursor of transition to turbulence: Spatiotemporal wave front.
To understand transition to turbulence via 3D disturbance growth, we report here results obtained from the solution of Navier-Stokes equation (NSE) to reproduce experimental results obtained by minimizing background disturbances and imposing deterministic excitation inside the shear layer. A similar approach was adopted in Sengupta and Bhaumik [Phys. Rev. Lett. 107, 154501 (2011)], where a route of transition from receptivity to fully developed turbulent stage was explained for 2D flow in terms of the spatio-temporal wave-front (STWF). The STWF was identified as the unit process of 2D turbulence creation for low amplitude wall excitation. Theoretical prediction of STWF for boundary layer was established earlier in Sengupta, Rao, and Venkatasubbaiah [Phys. Rev. Lett. 96, 224504 (2006)] from the Orr-Sommerfeld equation as due to spatiotemporal instability. Here, the same unit process of the STWF during transition is shown to be present for 3D disturbance field from the solution of governing NSE. PMID:24827343
Bhaumik, S; Sengupta, T K
Enhancing chaoticity of spatiotemporal chaos.
In some practical situations strong chaos is needed. This introduces the task of chaos control with enhancing chaoticity rather than suppressing chaoticity. In this paper a simple method of linear amplifications incorporating modulo operations is suggested to make spatiotemporal systems, which may be originally chaotic or nonchaotic, strongly chaotic. Specifically, this control can eliminate periodic windows, increase the values and the number of positive Lyapunov exponents, make the probability distributions of the output chaotic sequences more homogeneous, and reduce the correlations of chaotic outputs for different times and different space units. The applicability of the method to practical tasks, in particular to random number generators and secure communications, is briefly discussed. PMID:15697707
Li, Xiaowen; Zhang, Heqiao; Xue, Yu; Hu, Gang
Exhaustive search of the SNP-SNP interactome identifies epistatic effects on brain volume in two cohorts
The SNP-SNP interactome has rarely been explored in the context of neuroimaging genetics mainly due to the complexity of conducting ?1011 pairwise statistical tests. However, recent advances in machine learning, specifically the iterative sure independence screening (SIS) method, have enabled the analysis of datasets where the number of predictors is much larger than the number of observations. Using an implementation of the SIS algorithm (called EPISIS), we used exhaustive search of the genome-wide, SNP-SNP interactome to identify and prioritize SNPs for interaction analysis. We identified a significant SNP pair, rs1345203 and rs1213205, associated with temporal lobe volume. We further examined the full-brain, voxelwise effects of the interaction in the ADNI dataset and separately in an independent dataset of healthy twins (QTIM). We found that each additional loading in the epistatic effect was associated with ?5% greater brain regional brain volume (a protective effect) in both the ADNI and QTIM samples.
Hibar, Derrek P.; Stein, Jason L.; Jahanshad, Neda; Kohannim, Omid; Toga, Arthur W.; McMahon, Katie L.; de Zubicaray, Greig I.; Montgomery, Grant W.; Martin, Nicholas G.; Wright, Margaret J.; Weiner, Michael W.; Thompson, Paul M.
Cardiolipin defines the interactome of the major ADP/ATP carrier protein of the mitochondrial inner membrane
Defined mutations in the mitochondrial ADP/ATP carrier (AAC) are associated with certain types of progressive external ophthalmoplegia. AAC is required for oxidative phosphorylation (OXPHOS), and dysregulation of AAC has been implicated in apoptosis. Little is known about the AAC interactome, aside from a known requirement for the phospholipid cardiolipin (CL) and that it is thought to function as a homodimer. Using a newly developed dual affinity tag, we demonstrate that yeast AAC2 physically participates in several protein complexes of distinct size and composition. The respiratory supercomplex and several smaller AAC2-containing complexes, including other members of the mitochondrial carrier family, are identified here. In the absence of CL, most of the defined interactions are destabilized or undetectable. The absence of CL and/or AAC2 results in distinct yet additive alterations in respiratory supercomplex structure and respiratory function. Thus, a single lipid can significantly alter the functional interactome of an individual protein.
Claypool, Steven M.; Oktay, Yavuz; Boontheung, Pinmanee; Loo, Joseph A.; Koehler, Carla M.
Spatiotemporal Thinking in the Geosciences
NASA Astrophysics Data System (ADS)
Reasoning about spatial relations is a critical skill for geoscientists. Within the geosciences different disciplines may reason about different sorts of relationships. These relationships may span vastly different spatial and temporal scales (from the spatial alignment in atoms in crystals to the changes in the shape of plates). As part of work in a research center on spatial thinking in STEM education, we have been working to classify the spatial skills required in geology, develop tests for each spatial skill, and develop the cognitive science tools to promote the critical spatial reasoning skills. Research in psychology, neurology and linguistics supports a broad classification of spatial skills along two dimensions: one versus many objects (which roughly translates to object- focused and navigation focused skills) and static versus dynamic spatial relations. The talk will focus on the interaction of space and time in spatial cognition in the geosciences. We are working to develop measures of skill in visualizing spatiotemporal changes. A new test developed to measure visualization of brittle deformations will be presented. This is a skill that has not been clearly recognized in the cognitive science research domain and thus illustrates the value of interdisciplinary work that combines geosciences with cognitive sciences. Teaching spatiotemporal concepts can be challenging. Recent theoretical work suggests analogical reasoning can be a powerful tool to aid student learning to reason about temporal relations using spatial skills. Recent work in our lab has found that progressive alignment of spatial and temporal scales promotes accurate reasoning about temporal relations at geological time scales.
Shipley, T. F.; Manduca, C. A.; Ormand, C. J.; Tikoff, B.
Spatiotemporal evolution of ventricular fibrillation
NASA Astrophysics Data System (ADS)
Sudden cardiac death is the leading cause of death in the industrialized world, with the majority of such tragedies being due to ventricular fibrillation. Ventricular fibrillation is a frenzied and irregular disturbance of the heart rhythm that quickly renders the heart incapable of sustaining life. Rotors, electrophysiological structures that emit rotating spiral waves, occur in several systems that all share with the heart the functional properties of excitability and refractoriness. These re-entrant waves, seen in numerical solutions of simplified models of cardiac tissue, may occur during ventricular tachycardias,. It has been difficult to detect such forms of re-entry in fibrillating mammalian ventricles. Here we show that, in isolated perfused dog hearts, high spatial and temporal resolution mapping of optical transmembrane potentials can easily detect transiently erupting rotors during the early phase of ventricular fibrillation. This activity is characterized by a relatively high spatiotemporal cross-correlation. During this early fibrillatory interval, frequent wavefront collisions and wavebreak generation are also dominant features. Interestingly, this spatiotemporal pattern undergoes an evolution to a less highly spatially correlated mechanism that lacks the epicardial manifestations of rotors despite continued myocardial perfusion.
Witkowski, Francis X.; Leon, L. Joshua; Penkoske, Patricia A.; Giles, Wayne R.; Spano, Mark L.; Ditto, William L.; Winfree, Arthur T.
Spatiotemporal behavior and nonlinear dynamics in a phase conjugate resonator
NASA Technical Reports Server (NTRS)
The work described can be divided into two parts. The first part is an investigation of the transient behavior and stability property of a phase conjugate resonator (PCR) below threshold. The second part is an experimental and theoretical study of the PCR's spatiotemporal dynamics above threshold. The time-dependent coupled wave equations for four-wave mixing (FWM) in a photorefractive crystal, with two distinct interaction regions caused by feedback from an ordinary mirror, was used to model the transient dynamics of a PCR below threshold. The conditions for self-oscillation were determined and the solutions were used to define the PCR's transfer function and analyze its stability. Experimental results for the buildup and decay times confirmed qualitatively the predicted behavior. Experiments were carried out above threshold to study the spatiotemporal dynamics of the PCR as a function of Pragg detuning and the resonator's Fresnel number. The existence of optical vortices in the wavefront were identified by optical interferometry. It was possible to describe the transverse dynamics and the spatiotemporal instabilities by modeling the three-dimensional-coupled wave equations in photorefractive FWM using a truncated modal expansion approach.
Liu, Siuying Raymond
Optimal spatiotemporal reduced order modeling, Part I: proposed framework
NASA Astrophysics Data System (ADS)
An optimal spatiotemporal reduced order modeling framework is proposed for nonlinear dynamical systems in continuum mechanics. In this paper, Part I, the governing equations for a general system are modified for an under-resolved simulation in space and time with an arbitrary discretization scheme. Basic filtering concepts are used to demonstrate the manner in which subgrid-scale dynamics arise with a coarse computational grid. Models are then developed to account for the underlying spatiotemporal structure via inclusion of statistical information into the governing equations on a multi-point stencil. These subgrid-scale models are designed to provide closure by accounting for the interactions between spatiotemporal microscales and macroscales as the system evolves. Predictions for the modified system are based upon principles of mean-square error minimization, conditional expectations and stochastic estimation, thus rendering the optimal solution with respect to the chosen resolution. Practical methods are suggested for model construction, appraisal, error measure and implementation. The companion paper, Part II, is devoted to demonstrating the methodology through a computational study of a nonlinear beam.
LaBryer, Allen; Attar, Peter J.; Vedula, Prakash
NSDL National Science Digital Library
Students must be guided to state not only what they think will happen, but also a reason or explanation for what will happen based upon their prior knowledge. Therefore, the predictions students write should activate prior knowledge, relate to their focus
Klentschy, Michael P.
Forecasting the Spatio-Temporal Dynamics of the Magnetosphere
NASA Astrophysics Data System (ADS)
The spatio-temporal dynamics of the magnetosphere is a crucial component of effective space weather forecasting. The extensive data of the solar wind-magnetosphere interaction has been used to build predictive models of the magnetosphere based on nonlinear dynamical approaches. The time series data of the distributed observations are used to develop spatio-temporal dynamics of the magnetosphere. In this approach the solar wind - magnetosphere coupling is modeled as an input-output system with the solar wind variables as the input and the magnetic field variations at the ground stations as the magnetospheric response. The magnetic field perturbation at the ground and the corresponding solar wind data stations during the solar maximum period are compiled for these studies. The ground magnetometer data are from from CANOPUS, IMAGE and WDC magnetometer chain of stations. This new data set is used to study the spatio-temporal structure, including the coupling between the high and mid-latitude regions. A technique that utilizes the daily rotation of the Earth as a longitudinal sampling process is used to construct a two dimensional representation of the high latitude magnetic perturbations both in magnetic latitude and magnetic local time. This nonlinear model is used to predict the spatial structure of geomagnetic disturbances during intense geospace storms. In order to understand the predictability of space weather, the correlated database is used to study the causal relationships based on information theoretic approaches. This yields the mutual information between the solar wind variables and the ground magnetic field variations, and among the ground stations themselves. The information flow within the coupled system is analyzed by computing the transfer entropy among them.
Chen, J.; Sharma, A.; Veeramani, T.
3D hybrid wound devices for spatiotemporally controlled release kinetics.
This paper presents localized and temporal control of release kinetics over 3-dimensional (3D) hybrid wound devices to improve wound-healing process. Imaging study is performed to extract wound bed geometry in 3D. Non-Uniform Rational B-Splines (NURBS) based surface lofting is applied to generate functionally graded regions. Diffusion-based release kinetics model is developed to predict time-based release of loaded modifiers for functionally graded regions. Multi-chamber single nozzle solid freeform dispensing system is used to fabricate wound devices with controlled dispensing concentration. Spatiotemporal control of biological modifiers thus enables a way to achieve target delivery to improve wound healing. PMID:22672934
Ozbolat, Ibrahim T; Koc, Bahattin
Protein-drug interactome analysis of SSRI-mediated neurorecovery following stroke.
Serotonin selective reuptake inhibitors (SSRIs) have been widely used as first-line drugs in the treatment of a range of depressive and anxiety disorders. Recently, clinical studies found that this class of agents also shows significant efficacy in promoting neurogenesis, neuroplasticity and neurorecovery following stroke. Here, we attempt to elucidate molecular mechanism and biological implication underlying the SSRI-mediated neurorecovery. In the procedure, a comprehensive protein-drug interactome (PDI) was constructed for various SSRIs and their major metabolites as well as a group of control drugs across a large panel of human neuroproteins via a high-throughput molecular docking approach. The obtained PDI was then analyzed at systematic level to extract unexpected targets for SSRIs/metabolites. Biological network analysis and gene ontology (GO) enrichment solidified that the inferred targets have high potential to be directly or indirectly involved in diverse neural events, and further molecular dynamics (MD) simulation and post molecular mechanics-Poisson Boltzmann/surface area (MM-PB/SA) characterization revealed a stable complex architecture and high-affinity interaction between the targets and SSRIs/metabolites. Specifically, two human proteins, i.e. neurogenic locus notch homolog protein 1 (NOTCH 1) and Rho-associated protein kinase 1 (ROCK 1), were suggested as promising regulators in the SSRI-mediated neurorecovery, which can be targeted efficiently by fluoxetine and paroxetine, respectively, as well as other SSRIs and metabolites. PMID:24705245
Chen, Hong-Fang; Pan, Xiao-Ling; Wang, Jian-Wei; Kong, Hui-Mei; Fu, Ya-Ming
Characterization of the EGFR interactome reveals associated protein complex networks and intracellular receptor dynamics.
Growth factor receptor mediated signaling is meanwhile recognized as a complex signaling network, which is initiated by recruiting specific patterns of adaptor proteins to the intracellular domain of epidermal growth factor receptor (EGFR). Approaches to globally identify EGFR-binding proteins are required to elucidate this network. We affinity-purified EGFR with its interacting proteins by coprecipitation from lysates of A431 cells. A total of 183 proteins were repeatedly detected in high-resolution MS measurements. For 15 of these, direct interactions with EGFR were listed in the iRefIndex interaction database, including Grb2, shc-1, SOS1 and 2, STAT 1 and 3, AP2, UBS3B, and ERRFI. The newly developed Cytoscape plugin ModuleGraph allowed retrieving and visualizing 93 well-described protein complexes that contained at least one of the proteins found to interact with EGFR in our experiments. Abundances of 14 proteins were modulated more than twofold upon EGFR activation whereof clathrin-associated adaptor complex AP-2 showed 4.6-fold enrichment. These proteins were further annotated with different cellular compartments. Finally, interactions of AP-2 proteins and the newly discovered interaction of CIP2A could be verified. In conclusion, a powerful technique is presented that allowed identification and quantitative assessment of the EGFR interactome to provide further insight into EGFR signaling. PMID:23956138
Foerster, Sarah; Kacprowski, Tim; Dhople, Vishnu Mukund; Hammer, Elke; Herzog, Susann; Saafan, Hisham; Bien-M�ller, Sandra; Albrecht, Mario; V�lker, Uwe; Ritter, Christoph A
Interactome maps of mouse gene regulatory domains reveal basic principles of transcriptional regulation.
A key finding of the ENCODE project is that the enhancer landscape of mammalian cells undergoes marked alterations during ontogeny. However, the nature and extent of these changes are unclear. As�part of the NIH Mouse Regulome Project, we here combined DNaseI hypersensitivity, ChIP-seq, and ChIA-PET technologies to map the promoter-enhancer interactomes of pluripotent ES cells and differentiated B lymphocytes. We confirm that enhancer usage varies widely across tissues. Unexpectedly, we find that this feature extends to broadly transcribed genes, including Myc and Pim1 cell-cycle regulators, which associate with an entirely different set of enhancers in ES and B cells. By means of high-resolution CpG methylomes, genome editing, and digital footprinting, we show that these enhancers recruit lineage-determining factors. Furthermore, we demonstrate that the turning on and off of enhancers during development correlates with promoter activity. We propose that organisms rely on a dynamic enhancer landscape to control basic cellular functions in a tissue-specific manner. PMID:24360274
Kieffer-Kwon, Kyong-Rim; Tang, Zhonghui; Mathe, Ewy; Qian, Jason; Sung, Myong-Hee; Li, Guoliang; Resch, Wolfgang; Baek, Songjoon; Pruett, Nathanael; Gr�ntved, Lars; Vian, Laura; Nelson, Steevenson; Zare, Hossein; Hakim, Ofir; Reyon, Deepak; Yamane, Arito; Nakahashi, Hirotaka; Kovalchuk, Alexander L; Zou, Jizhong; Joung, J Keith; Sartorelli, Vittorio; Wei, Chia-Lin; Ruan, Xiaoan; Hager, Gordon L; Ruan, Yijun; Casellas, Rafael
A comprehensive Plasmodium falciparum protein interaction map reveals a distinct architecture of a core interactome
We derive a map of protein interactions in the parasite P. falciparum from conserved interactions in S. cerevisiae, C. elegans, D. melanogaster and E. coli and pool them with experimental interaction data. The application of a clique-percolation algorithm allows us to find overlapping clusters, strongly correlated with yeast specific conserved protein complexes. Such clusters contain core activities that govern gene expression, largely dominated by components of protein production and degradation processes as well as RNA metabolism. A critical role of protein hubs in the interactome of P. falciparum is supported by their appearance in multiple clusters and the tendencies of their interactions to reach into many distinct protein clusters. Parasite proteins with a human ortholog tend to appear in single complexes. Annotating each protein with the stage where it is maximally expressed we observe a high level of cluster integrity in the ring stage. While we find no signal in the trophozoite phase, expression patterns are reversed in the schizont phase, implying a preponderance of parasite specific functions in this late, invasive schizont stage. As such, the inference of potential protein interactions and their analysis contributes to our understanding of the parasite, indicating basic pathways and processes as unique targets for therapeutic intervention.
Wuchty, Stefan; Adams, John H.; Ferdig, Michael T.
Characterization of hampin/MSL1 as a node in the nuclear interactome
Hampin, homolog of drosophila MSL1, is a partner of histone acetyltransferase MYST1/MOF. Functions of these proteins remain poorly understood beyond their participation in chromatin remodeling complex MSL. In order to identify new proteins interacting with hampin, we screened a mouse cDNA library in yeast two-hybrid system with mouse hampin as bait and found five high-confidence interactors: MYST1, TPR proteins TTC4 and KIAA0103, NOP17 (homolog of a yeast nucleolar protein) and transcription factor GC BP. Subsequently, all these proteins were used as baits in library screenings and more new interactions were found: tumor suppressor RASSF1C and spliceosome component PRP3 for KIAA0103, ring finger RNF10 for RASSF1C, and RNA polymerase II regulator NELF-C for MYST1. The majority of the observed interactions was confirmed in vitro by pull-down of bacterially expressed proteins. Reconstruction of a fragment of mammalian interactome suggests that hampin may be linked to diverse regulatory processes in the nucleus.
Dmitriev, Ruslan I.; Korneenko, Tatyana V.; Bessonov, Alexander A.; Shakhparonov, Mikhail I.; Modyanov, Nikolai N.; Pestov, Nikolay B.
Characterization of hampin/MSL1 as a node in the nuclear interactome
Hampin, homolog of Drosophila MSL1, is a partner of histone acetyltransferase MYST1/MOF. Functions of these proteins remain poorly understood beyond their participation in chromatin remodeling complex MSL. In order to identify new proteins interacting with hampin, we screened a mouse cDNA library in yeast two-hybrid system with mouse hampin as bait and found five high-confidence interactors: MYST1, TPR proteins TTC4 and KIAA0103, NOP17 (homolog of a yeast nucleolar protein), and transcription factor GC BP. Subsequently, all these proteins were used as baits in library screenings and more new interactions were found: tumor suppressor RASSF1C and spliceosome component PRP3 for KIAA0103, ring finger RNF10 for RASSF1C, and RNA polymerase II regulator NELF-C for MYST1. The majority of the observed interactions was confirmed in vitro by pull-down of bacterially expressed proteins. Reconstruction of a fragment of mammalian interactome suggests that hampin may be linked to diverse regulatory processes in the nucleus.
Dmitriev, Ruslan I. [Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow 117997 (Russian Federation); Korneenko, Tatyana V. [Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow 117997 (Russian Federation); Department of Physiology, Pharmacology, Metabolism, and Cardiovascular Sciences, University of Toledo College of Medicine, Toledo, OH 43614 (United States); Bessonov, Alexander A. [Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow 117997 (Russian Federation); Shakhparonov, Mikhail I. [Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow 117997 (Russian Federation); Modyanov, Nikolai N. [Department of Physiology, Pharmacology, Metabolism, and Cardiovascular Sciences, University of Toledo College of Medicine, Toledo, OH 43614 (United States); Pestov, Nikolay B. [Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow 117997 (Russian Federation); Department of Physiology, Pharmacology, Metabolism, and Cardiovascular Sciences, University of Toledo College of Medicine, Toledo, OH 43614 (United States); E-mail: firstname.lastname@example.com
An extracellular interactome of Immunoglobulin and LRR proteins reveals receptor-ligand networks
Extracellular domains of cell-surface receptors and ligands mediate cell-cell communication, adhesion, and initiation of signaling events, but most existing protein-protein �interactome� datasets lack information for extracellular interactions. We probed interactions between receptor extracellular domains, focusing on the Immunoglobulin Superfamily (IgSF), Fibronectin type-III (FnIII) and Leucine-rich repeat (LRR) families of Drosophila, a set of 202 proteins, many of which are known to be important in neuronal and developmental functions. Out of 20503 candidate protein pairs tested, we observed 106 interactions, 83 of which were previously unknown. We �deorphanized� the 20-member subfamily of defective in proboscis IgSF proteins, showing that they selectively interact with an 11-member subfamily of previously uncharacterized IgSF proteins. Both subfamilies interact with a single common �orphan� LRR protein. We also observed new interactions between Hedgehog and EGFR pathway components. Several of these interactions could be visualized in live-dissected embryos, demonstrating that this approach can identify physiologically relevant receptor-ligand pairs.
Ozkan, Engin; Carrillo, Robert A.; Eastman, Catharine L.; Weiszmann, Richard; Waghray, Deepa; Johnson, Karl G.; Zinn, Kai; Celniker, Susan E.; Garcia, K. Christopher
Genome-wide bimolecular fluorescence complementation analysis of SUMO interactome in yeast
The definition of protein�protein interactions (PPIs) in the natural cellular context is essential for properly understanding various biological processes. So far, however, most large-scale PPI analyses have not been performed in the natural cellular context. Here, we describe the construction of a Saccharomyces cerevisiae fusion library in which each endogenous gene is C-terminally tagged with the N-terminal fragment of Venus (VN) for a genome-wide bimolecular fluorescence complementation assay, a powerful technique for identifying PPIs in living cells. We illustrate the utility of the VN fusion library by systematically analyzing the interactome of the small ubiquitin-related modifier (SUMO) and provide previously unavailable information on the subcellular localization, types, and protease dependence of SUMO interactions. Our data set is highly complementary to the existing data sets and represents a useful resource for expanding the understanding of the physiological roles of SUMO. In addition, the VN fusion library provides a useful research tool that makes it feasible to systematically analyze PPIs in the natural cellular context.
Sung, Min-Kyung; Lim, Gyubum; Yi, Dae-Gwan; Chang, Yeon Ji; Yang, Eun Bin; Lee, KiYoung; Huh, Won-Ki
The identification of short linear motif-mediated interfaces within the human interactome
Motivation: Eukaryotic proteins are highly modular, containing multiple interaction interfaces that mediate binding to a network of regulators and effectors. Recent advances in high-throughput proteomics have rapidly expanded the number of known protein�protein interactions (PPIs); however, the molecular basis for the majority of these interactions remains to be elucidated. There has been a growing appreciation of the importance of a subset of these PPIs, namely those mediated by short linear motifs (SLiMs), particularly the canonical and ubiquitous SH2, SH3 and PDZ domain-binding motifs. However, these motif classes represent only a small fraction of known SLiMs and outside these examples little effort has been made, either bioinformatically or experimentally, to discover the full complement of motif instances. Results: In this article, interaction data are analysed to identify and characterize an important subset of PPIs, those involving SLiMs binding to globular domains. To do this, we introduce iELM, a method to identify interactions mediated by SLiMs and add molecular details of the interaction interfaces to both interacting proteins. The method identifies SLiM-mediated interfaces from PPI data by searching for known SLiM�domain pairs. This approach was applied to the human interactome to identify a set of high-confidence putative SLiM-mediated PPIs. Availability: iELM is freely available at http://elmint.embl.de Contact: firstname.lastname@example.com Supplementary information: Supplementary data are available at Bioinformatics online.
Weatheritt, R. J.; Luck, K.; Petsalaki, E.; Davey, N. E.; Gibson, T. J.
"Stop Ne(c)king around": How interactomics contributes to functionally characterize Nek family kinases
Aside from Polo and Aurora, a third but less studied kinase family involved in mitosis regulation is the never in mitosis-gene A (NIMA)-related kinases (Neks). The founding member of this family is the sole member NIMA of Aspergillus nidulans, which is crucial for the initiation of mitosis in that organism. All 11 human Neks have been functionally assigned to one of the three core functions established for this family in mammals: (1) centrioles/mitosis; (2) primary ciliary function/ciliopathies; and (3) DNA damage response (DDR). Recent findings, especially on Nek 1 and 8, showed however, that several Neks participate in parallel in at least two of these contexts: primary ciliary function and DDR. In the core section of this in-depth review, we report the current detailed functional knowledge on each of the 11 Neks. In the discussion, we return to the cross-connections among Neks and point out how our and other groups� functional and interactomics studies revealed that most Neks interact with protein partners associated with two if not all three of the functional contexts. We then raise the hypothesis that Neks may be the connecting regulatory elements that allow the cell to fine tune and synchronize the cellular events associated with these three core functions. The new and exciting findings on the Nek family open new perspectives and should allow the Neks to finally claim the attention they deserve in the field of kinases and cell cycle biology.
Meirelles, Gabriela Vaz; Perez, Arina Marina; de Souza, Edmarcia Elisa; Basei, Fernanda Luisa; Papa, Priscila Ferreira; Melo Hanchuk, Talita Diniz; Cardoso, Vanessa Bomfim; Kobarg, Jorg
Intrinsic Disorder in PTEN and its Interactome Confers Structural Plasticity and Functional Versatility
IDPs, while structurally poor, are functionally rich by virtue of their flexibility and modularity. However, how mutations in IDPs elicit diseases, remain elusive. Herein, we have identified tumor suppressor PTEN as an intrinsically disordered protein (IDP) and elucidated the molecular principles by which its intrinsically disordered region (IDR) at the carboxyl-terminus (C-tail) executes its functions. Post-translational modifications, conserved eukaryotic linear motifs and molecular recognition features present in the C-tail IDR enhance PTEN's protein-protein interactions that are required for its myriad cellular functions. PTEN primary and secondary interactomes are also enriched in IDPs, most being cancer related, revealing that PTEN functions emanate from and are nucleated by the C-tail IDR, which form pliable network-hubs. Together, PTEN higher order functional networks operate via multiple IDP-IDP interactions facilitated by its C-tail IDR. Targeting PTEN IDR and its interaction hubs emerges as a new paradigm for treatment of PTEN related pathologies.
Malaney, Prerna; Pathak, Ravi Ramesh; Xue, Bin; Uversky, Vladimir N.; Dave, Vrushank
A Synthetic Coiled-Coil Interactome Provides Heterospecific Modules for Molecular Engineering
The versatile coiled-coil protein motif is widely used to induce and control macromolecular interactions in biology and materials science. Yet the types of interaction patterns that can be constructed using known coiled coils are limited. Here we greatly expand the coiled-coil toolkit by measuring the complete pairwise interactions of 48 synthetic coiled coils and 7 human bZIP coiled coils using peptide microarrays. The resulting 55-member protein 'interactome' includes 27 pairs of interacting peptides that preferentially heteroassociate. The 27 pairs can be used in combinations to assemble sets of 3 to 6 proteins that compose networks of varying topologies. Of special interest are heterospecific peptide pairs that participate in mutually orthogonal interactions. Such pairs provide the opportunity to dimerize two separate molecular systems without undesired crosstalk. Solution and structural characterization of two such sets of orthogonal heterodimers provide details of their interaction geometries. The orthogonal pair, along with the many other network motifs discovered in our screen, provide new capabilities for synthetic biology and other applications.
Reinke, Aaron W.; Grant, Robert A.; Keating, Amy E. (MIT)
Generating spatiotemporal datasets on the WWW
Efficient storage, indexing and retrieval of time-evolving spatial data are some of the tasks that a Spatiotemporal Database Management System (STDBMS) must support. Aiming at designers of indexing methods and access structures, in this article we review the GSTD algorithm for generating spatiotemporal datasets according to several user-defined parameters, and introduce a WWW-based environment for generating and visualizing such datasets.
Yannis Theodoridis; Mario A. Nascimento
Rice Mitogen-Activated Protein Kinase Interactome Analysis Using the Yeast Two-Hybrid System1[C][W
Mitogen-activated protein kinase (MAPK) cascades support the flow of extracellular signals to intracellular target molecules and ultimately drive a diverse array of physiological functions in cells, tissues, and organisms by interacting with other proteins. Yet, our knowledge of the global physical MAPK interactome in plants remains largely fragmented. Here, we utilized the yeast two-hybrid system and coimmunoprecipitation, pull-down, bimolecular fluorescence complementation, subcellular localization, and kinase assay experiments in the model crop rice (Oryza sativa) to systematically map what is to our knowledge the first plant MAPK-interacting proteins. We identified 80 nonredundant interacting protein pairs (74 nonredundant interactors) for rice MAPKs and elucidated the novel proteome-wide network of MAPK interactors. The established interactome contains four membrane-associated proteins, seven MAP2Ks (for MAPK kinase), four MAPKs, and 59 putative substrates, including 18 transcription factors. Several interactors were also validated by experimental approaches (in vivo and in vitro) and literature survey. Our results highlight the importance of OsMPK1, an ortholog of tobacco (Nicotiana benthamiana) salicyclic acid-induced protein kinase and Arabidopsis (Arabidopsis thaliana) AtMPK6, among the rice MAPKs, as it alone interacts with 41 unique proteins (51.2% of the mapped MAPK interaction network). Additionally, Gene Ontology classification of interacting proteins into 34 functional categories suggested MAPK participation in diverse physiological functions. Together, the results obtained essentially enhance our knowledge of the MAPK-interacting protein network and provide a valuable research resource for developing a nearly complete map of the rice MAPK interactome.
Singh, Raksha; Lee, Mi-Ok; Lee, Jae-Eun; Choi, Jihyun; Park, Ji Hun; Kim, Eun Hye; Yoo, Ran Hee; Cho, Jung-Il; Jeon, Jong-Seong; Rakwal, Randeep; Agrawal, Ganesh Kumar; Moon, Jae Sun; Jwa, Nam-Soo
Spatiotemporal analysis of brightness induction
Brightness induction refers to a class of visual illusions in which the perceived intensity of a region of space is influenced by the luminance of surrounding regions. These illusions are significant because they provide insight into the neural organization of the visual system. A novel quadrature-phase motion cancelation technique was developed to measure the magnitude of the grating induction brightness illusion across a wide range of spatial frequencies, temporal frequencies and test field heights. Canceling contrast is greatest at low frequencies and declines with increasing frequency in both dimensions, and with increasing test field height. Canceling contrast scales as the product of inducing grating spatial frequency and test field height (the number of inducing grating cycles per test field height). When plotted using a spatial axis which indexes this product, the spatiotemporal induction surfaces for four test field heights can be described as four partially overlapping sections of a single larger surface. These properties of brightness induction are explained in the context of multiscale spatial filtering. The present study is the first to measure the magnitude of grating induction as a function of temporal frequency. Taken in conjunction with several other studies (Blakeslee & McCourt, 2008; Robinson & de Sa, 2008; Magnussen & Glad, 1975) the results of this study illustrate that at least one form of brightness induction is very much faster than that reported by DeValois et al. (1986) and Rossi and Paradiso (1996), and are inconsistent with the proposition that brightness induction results from a slow �filling in� process.
McCourt, Mark E.
Spatiotemporal control of cardiac alternans
NASA Astrophysics Data System (ADS)
Electrical alternans are believed to be linked to the onset of life-threatening ventricular arrhythmias and sudden cardiac death. Recent studies have shown that alternans can be suppressed temporally by dynamic feedback control of the pacing interval. Here we investigate theoretically whether control can suppress alternans both temporally and spatially in homogeneous tissue paced at a single site. We first carry out ionic model simulations in a one-dimensional cable geometry which show that control is only effective up to a maximum cable length that decreases sharply away from the alternans bifurcation point. We then explain this finding by a linear stability analysis of an amplitude equation that describes the spatiotemporal evolution of alternans. This analysis reveals that control failure above a critical cable length is caused by the formation of standing wave patterns of alternans that are eigenfunctions of a forced Helmholtz equation, and therefore remarkably analogous to sound harmonics in an open pipe. We discuss the implications of these results for using control to suppress alternans in the human ventricles as well as to probe fundamental aspects of alternans morphogenesis.
Echebarria, Blas; Karma, Alain
Bayesian Spatio-Temporal Analysis and Geospatial Risk Factors of Human Monocytic Ehrlichiosis
Variations in spatio-temporal patterns of Human Monocytic Ehrlichiosis (HME) infection in the state of Kansas, USA were examined and the relationship between HME relative risk and various environmental, climatic and socio-economic variables were evaluated. HME data used in the study was reported to the Kansas Department of Health and Environment between years 2005�2012, and geospatial variables representing the physical environment [National Land cover/Land use, NASA Moderate Resolution Imaging Spectroradiometer (MODIS)], climate [NASA MODIS, Prediction of Worldwide Renewable Energy (POWER)], and socio-economic conditions (US Census Bureau) were derived from publicly available sources. Following univariate screening of candidate variables using logistic regressions, two Bayesian hierarchical models were fit; a partial spatio-temporal model with random effects and a spatio-temporal interaction term, and a second model that included additional covariate terms. The best fitting model revealed that spatio-temporal autocorrelation in Kansas increased steadily from 2005�2012, and identified poverty status, relative humidity, and an interactive factor, �diurnal temperature range x mixed forest area� as significant county-level risk factors for HME. The identification of significant spatio-temporal pattern and new risk factors are important in the context of HME prevention, for future research in the areas of ecology and evolution of HME, and as well as climate change impacts on tick-borne diseases.
Raghavan, Ram K.; Neises, Daniel; Goodin, Douglas G.; Andresen, Daniel A.; Ganta, Roman R.
Bayesian spatio-temporal analysis and geospatial risk factors of human monocytic ehrlichiosis.
Variations in spatio-temporal patterns of Human Monocytic Ehrlichiosis (HME) infection in the state of Kansas, USA were examined and the relationship between HME relative risk and various environmental, climatic and socio-economic variables were evaluated. HME data used in the study was reported to the Kansas Department of Health and Environment between years 2005-2012, and geospatial variables representing the physical environment [National Land cover/Land use, NASA Moderate Resolution Imaging Spectroradiometer (MODIS)], climate [NASA MODIS, Prediction of Worldwide Renewable Energy (POWER)], and socio-economic conditions (US Census Bureau) were derived from publicly available sources. Following univariate screening of candidate variables using logistic regressions, two Bayesian hierarchical models were fit; a partial spatio-temporal model with random effects and a spatio-temporal interaction term, and a second model that included additional covariate terms. The best fitting model revealed that spatio-temporal autocorrelation in Kansas increased steadily from 2005-2012, and identified poverty status, relative humidity, and an interactive factor, 'diurnal temperature range x mixed forest area' as significant county-level risk factors for HME. The identification of significant spatio-temporal pattern and new risk factors are important in the context of HME prevention, for future research in the areas of ecology and evolution of HME, and as well as climate change impacts on tick-borne diseases. PMID:24992684
Raghavan, Ram K; Neises, Daniel; Goodin, Douglas G; Andresen, Daniel A; Ganta, Roman R
Interactomes to Biological Phase Space: a call to begin thinking at a new level in computational biology.
Techniques for high throughput determinations of interactomes, together with high resolution protein collocalizations maps within organelles and through membranes will soon create a vast resource. With these data, biological descriptions, akin to the high dimensional phase spaces familiar to physicists, will become possible. These descriptions will capture sufficient information to make possible realistic, system-level models of cells. The descriptions and the computational models they enable will require powerful computing techniques. This report is offered as a call to the computational biology community to begin thinking at this scale and as a challenge to develop the required algorithms and codes to make use of the new data.3
Davidson, George S.; Brown, William Michael
Using proteomics to identify the HBx interactome in hepatitis B virus: how can this inform the clinic?
Hepatitis B virus (HBV) is a small and enveloped DNA virus, of which chronic infection is the main risk factor of liver cirrhosis and hepatocellular carcinoma. Hepatitis B virus X protein (HBx) is a multifunctional protein encoded by HBV genome, which have significant effects on HBV replication and pathogenesis. Through directly interacting with cellular proteins, HBx is capable to promote HBV replication, regulate transcription of host genes, disrupt protein degradation, modulate signaling pathway, manipulate cell death and deregulate cell cycle. In this review, we briefly discuss the diversified effects of HBx-interactome and their potential clinical significances. PMID:24308553
Xie, Na; Chen, Xiang; Zhang, Tao; Liu, Bo; Huang, Canhua
Spatio-temporal avalanche forecasting with Support Vector Machines
NASA Astrophysics Data System (ADS)
This paper explores the use of the Support Vector Machine (SVM) as a data exploration tool and a predictive engine for spatio-temporal forecasting of snow avalanches. Based on the historical observations of avalanche activity, meteorological conditions and snowpack observations in the field, an SVM is used to build a data-driven spatio-temporal forecast for the local mountain region. It incorporates the outputs of simple physics-based and statistical approaches used to interpolate meteorological and snowpack-related data over a digital elevation model of the region. The interpretation of the produced forecast is discussed, and the quality of the model is validated using observations and avalanche bulletins of the recent years. The insight into the model behaviour is presented to highlight the interpretability of the model, its abilities to produce reliable forecasts for individual avalanche paths and sensitivity to input data. Estimates of prediction uncertainty are obtained with ensemble forecasting. The case study was carried out using data from the avalanche forecasting service in the Locaber region of Scotland, where avalanches are forecast on a daily basis during the winter months.
Pozdnoukhov, A.; Matasci, G.; Kanevski, M.; Purves, R. S.
Adaptive spatio-temporal filtering of multichannel surface EMG signals.
A motor unit (MU) is defined as an anterior horn cell, its axon, and the muscle fibres innervated by the motor neuron. A surface electromyogram (EMG) is a superposition of many different MU action potentials (MUAPs) generated by active MUs. The objectives of this study were to introduce a new adaptive spatio-temporal filter, here called maximum kurtosis filter (MKF), and to compare it with existing filters, on its performance to detect a single MUAP train from multichannel surface EMG signals. The MKF adaptively chooses the filter coefficients by maximising the kurtosis of the output. The proposed method was compared with five commonly used spatial filters, the weighted low-pass differential filter (WLPD) and the marginal distribution of a continuous wavelet transform. The performance was evaluated using simulated EMG signals. In addition, results from a multichannel surface EMG measurement fro from a subject who had been previously exposed to radiation due to cancer were used to demonstrate an application of the method. With five time lags of the MKF, the sensitivity was 98.7% and the highest sensitivity of the traditional filters was 86.8%, which was obtained with the WLPD. The positive predictivities of these filters were 87.4 and 80.4%, respectively. Results from simulations showed that the proposed spatio-temporal filtration technique significantly improved performance as compared with existing filters, and the sensitivity and the positive predictivity increased with an increase in number of time lags in the filter. PMID:16937162
Ostlund, Nils; Yu, Jun; Karlsson, J Stefan
DTome: a web-based tool for drug-target interactome construction
Background Understanding drug bioactivities is crucial for early-stage drug discovery, toxicology studies and clinical trials. Network pharmacology is a promising approach to better understand the molecular mechanisms of drug bioactivities. With a dramatic increase of rich data sources that document drugs' structural, chemical, and biological activities, it is necessary to develop an automated tool to construct a drug-target network for candidate drugs, thus facilitating the drug discovery process. Results We designed a computational workflow to construct drug-target networks from different knowledge bases including DrugBank, PharmGKB, and the PINA database. To automatically implement the workflow, we created a web-based tool called DTome (Drug-Target interactome tool), which is comprised of a database schema and a user-friendly web interface. The DTome tool utilizes web-based queries to search candidate drugs and then construct a DTome network by extracting and integrating four types of interactions. The four types are adverse drug interactions, drug-target interactions, drug-gene associations, and target-/gene-protein interactions. Additionally, we provided a detailed network analysis and visualization process to illustrate how to analyze and interpret the DTome network. The DTome tool is publicly available at http://bioinfo.mc.vanderbilt.edu/DTome. Conclusions As demonstrated with the antipsychotic drug clozapine, the DTome tool was effective and promising for the investigation of relationships among drugs, adverse interaction drugs, drug primary targets, drug-associated genes, and proteins directly interacting with targets or genes. The resultant DTome network provides researchers with direct insights into their interest drug(s), such as the molecular mechanisms of drug actions. We believe such a tool can facilitate identification of drug targets and drug adverse interactions.
Uncovering new signaling proteins and potential drug targets through the interactome analysis of Mycobacterium tuberculosis
Background Analysis of the pathogen interactome is a powerful approach for dissecting potential signal transduction and virulence pathways. It also offers opportunities for exploring new drug targets. Results In this study, a protein-protein interaction (PPI) network of Mycobacterium tuberculosis H37Rv was constructed using a homogenous protein mapping method, which has shown molecular chaperones, ribosomal proteins and ABC transporters to be highly interconnected proteins. A further analysis of this network unraveled the function of hypothetical proteins as well as a potential signaling pathway. A hypothetical protein, Rv2752c, which was linked to a metal cation-transporting ATPase, was characterized as a metal-beta-lactamase, through domain analysis in combination with an in vitro activity experiment. A second hypothetical protein, Rv1354c, and an unknown protein kinase, PknK, interacted with a similar group of inner membrane-associated ABC transporters in the PPI network. The interactions of Rv1354 with these proteins were also confirmed by a further bacterial two-hybrid analysis. According to protein domain structures, the unique M. tuberculosis Rv1354c gene was proposed, for the first time, to be responsible for the turnover of cyclic-di-GMP, a second messenger molecule in this bacterium. A further structure-based inhibitors screening for Rv1354c was also performed in silicon. Conclusion We constructed a comprehensive protein-protein interaction network for M. tuberculosis consisting of 738 proteins and 5639 interaction pairs. Our analysis unraveled the function of hypothetical proteins as well as a potential signaling pathway. The group of ABC transporters, PknK, and Rv1354c were proposed to constitute a potential membrane-associated signaling pathway that cooperatively responds to environmental stresses in M. tuberculosis. The study therefore provides valuable clues in exploring new signaling proteins, virulence pathways, and drug targets.
Cui, Tao; Zhang, Lei; Wang, Xizhou; He, Zheng-Guo
Spatiotemporal Interactions in Retinal Prosthesis Subjects
Purpose. Vision loss due to retinitis pigmentosa affects an estimated 15 million people worldwide. Through collaboration between Second Sight Medical Products, Inc., and the Doheny Eye Institute, six blind human subjects underwent implantation with epiretinal 4 � 4 electrode arrays designed to directly stimulate the remaining cells of the retina, with the goal of restoring functional vision by applying spatiotemporal patterns of stimulation. To better understand spatiotemporal interactions between electrodes during synchronous and asynchronous stimulation, the authors investigated how percepts changed as a function of pulse timing across the electrodes. Methods. Pulse trains (20, 40, 80, and 160 Hz) were presented on groups of electrodes with 800, 1600, or 2400 ?m center-to-center separation. Stimulation was either synchronous (pulses were presented simultaneously across electrodes) or asynchronous (pulses were phase shifted). Using a same-different discrimination task, the authors were able to evaluate how the perceptual quality of the stimuli changed as a function of phase shifts across multiple electrodes. Results. Even after controlling for electric field interactions, subjects could discriminate between spatiotemporal pulse train patterns based on differences of phase across electrodes as small as 3 ms. These findings suggest that the quality of the percept is affected not only by electric field interactions but also by spatiotemporal interactions at the neural level. Conclusions. During multielectrode stimulation, interactions between electrodes have a significant influence on the quality of the percept. Understanding how these spatiotemporal interactions at the neural level influence percepts during multielectrode stimulation is fundamental to the successful design of a retinal prosthesis.
Greenberg, Robert J.; Fine, Ione
Formally grounding spatio-temporal thinking.
To navigate through daily life, humans use their ability to conceptualize spatio-temporal information, which ultimately leads to a system of categories. Likewise, the spatial sciences rely heavily on conceptualization and categorization as means to create knowledge when they process spatio-temporal data. In the spatial sciences and in related branches of artificial intelligence, an approach has been developed for processing spatio-temporal data on the level of coarse categories: qualitative spatio-temporal representation and reasoning (QSTR). Calculi developed in QSTR allow for the meaningful processing of and reasoning with spatio-temporal information. While qualitative calculi are widely acknowledged in the cognitive sciences, there is little behavioral assessment whether these calculi are indeed cognitively adequate. This is an astonishing conundrum given that these calculi are ubiquitous, are often intended to improve processes at the human-machine interface, and are on several occasions claimed to be cognitively adequate. We have systematically evaluated several approaches to formally characterize spatial relations from a cognitive-behavioral perspective for both static and dynamically changing spatial relations. This contribution will detail our framework, which is addressing the question how formal characterization of space can help us understand how people think with, in, and about space. PMID:22806649
Klippel, Alexander; Wallgr�n, Jan Oliver; Yang, Jinlong; Li, Rui; Dylla, Frank
Mercury Toolset for Spatiotemporal Metadata
NASA Technical Reports Server (NTRS)
Mercury (http://mercury.ornl.gov) is a set of tools for federated harvesting, searching, and retrieving metadata, particularly spatiotemporal metadata. Version 3.0 of the Mercury toolset provides orders of magnitude improvements in search speed, support for additional metadata formats, integration with Google Maps for spatial queries, facetted type search, support for RSS (Really Simple Syndication) delivery of search results, and enhanced customization to meet the needs of the multiple projects that use Mercury. It provides a single portal to very quickly search for data and information contained in disparate data management systems, each of which may use different metadata formats. Mercury harvests metadata and key data from contributing project servers distributed around the world and builds a centralized index. The search interfaces then allow the users to perform a variety of fielded, spatial, and temporal searches across these metadata sources. This centralized repository of metadata with distributed data sources provides extremely fast search results to the user, while allowing data providers to advertise the availability of their data and maintain complete control and ownership of that data. Mercury periodically (typically daily) harvests metadata sources through a collection of interfaces and re-indexes these metadata to provide extremely rapid search capabilities, even over collections with tens of millions of metadata records. A number of both graphical and application interfaces have been constructed within Mercury, to enable both human users and other computer programs to perform queries. Mercury was also designed to support multiple different projects, so that the particular fields that can be queried and used with search filters are easy to configure for each different project.
Wilson, Bruce E.; Palanisamy, Giri; Devarakonda, Ranjeet; Rhyne, B. Timothy; Lindsley, Chris; Green, James
Mercury Toolset for Spatiotemporal Metadata
NASA Astrophysics Data System (ADS)
Mercury (http://mercury.ornl.gov) is a set of tools for federated harvesting, searching, and retrieving metadata, particularly spatiotemporal metadata. Version 3.0 of the Mercury toolset provides orders of magnitude improvements in search speed, support for additional metadata formats, integration with Google Maps for spatial queries, facetted type search, support for RSS (Really Simple Syndication) delivery of search results, and enhanced customization to meet the needs of the multiple projects that use Mercury. It provides a single portal to very quickly search for data and information contained in disparate data management systems, each of which may use different metadata formats. Mercury harvests metadata and key data from contributing project servers distributed around the world and builds a centralized index. The search interfaces then allow the users to perform a variety of fielded, spatial, and temporal searches across these metadata sources. This centralized repository of metadata with distributed data sources provides extremely fast search results to the user, while allowing data providers to advertise the availability of their data and maintain complete control and ownership of that data. Mercury periodically (typically daily)harvests metadata sources through a collection of interfaces and re-indexes these metadata to provide extremely rapid search capabilities, even over collections with tens of millions of metadata records. A number of both graphical and application interfaces have been constructed within Mercury, to enable both human users and other computer programs to perform queries. Mercury was also designed to support multiple different projects, so that the particular fields that can be queried and used with search filters are easy to configure for each different project.
Devarakonda, Ranjeet; Palanisamy, Giri; Green, James; Wilson, Bruce; Rhyne, B. Timothy; Lindsley, Chris
Spatiotemporal testing and modeling of catfish retinal neurons.
The responses of retinal neurons depend on the interaction of both temporal and spatial aspects of a light stimulus. We developed a linear spatiotemporal model of receptor and horizontal cell layers in the catfish retina based on reciprocal interactions between both layers and coupling within each. Horizontal cell transfer properties were measured experimentally using white-noise intensity modulated light spots of different diameters and were compared with analytical predictions based on the model. Good agreement was obtained with a reasonable choice of model space-constants and feedback parameters. Furthermore, the same set of parameter values determined from spot experiments enabled accurate prediction of experimental horizontal cell responses to traveling gratings. The proposed feedback connections from horizontal cells to receptors quicken the time-course of responses in both layers and sharpen receptive fields.
Krausz, H I; Naka, K
The Cellular Interactome of the Coronavirus Infectious Bronchitis Virus Nucleocapsid Protein and Functional Implications for Virus Biology
The coronavirus nucleocapsid (N) protein plays a multifunctional role in the virus life cycle, from regulation of replication and transcription and genome packaging to modulation of host cell processes. These functions are likely to be facilitated by interactions with host cell proteins. The potential interactome of the infectious bronchitis virus (IBV) N protein was mapped using stable isotope labeling with amino acids in cell culture (SILAC) coupled to a green fluorescent protein-nanotrap pulldown methodology and liquid chromatography-tandem mass spectrometry. The addition of the SILAC label allowed discrimination of proteins that were likely to specifically bind to the N protein over background binding. Overall, 142 cellular proteins were selected as potentially binding to the N protein, many as part of larger possible complexes. These included ribosomal proteins, nucleolar proteins, translation initiation factors, helicases, and hnRNPs. The association of selected cellular proteins with IBV N protein was confirmed by immunoblotting, cosedimentation, and confocal microscopy. Further, the localization of selected proteins in IBV-infected cells as well as their activity during virus infection was assessed by small interfering RNA-mediated depletion, demonstrating the functional importance of cellular proteins in the biology of IBV. This interactome not only confirms previous observations made with other coronavirus and IBV N proteins with both overexpressed proteins and infectious virus but also provides novel data that can be exploited to understand the interaction between the virus and the host cell.
Munday, Diane; Bickerton, Erica; Britton, Paul; Rodgers, Mark A.; Whitehouse, Adrian; Zhou, En-Min
Susceptibility genes are enriched in those of the herpes simplex virus 1/host interactome in psychiatric and neurological disorders.
Herpes simplex virus 1 (HSV-1) can promote beta-amyloid deposition and tau phosphorylation, demyelination or cognitive deficits relevant to Alzheimer's disease or multiple sclerosis and to many neuropsychiatric disorders with which it has been implicated. A seroprevalence much higher than disease incidence has called into question any primary causal role. However, as also the case with risk-promoting polymorphisms (also present in control populations), any causal effects are likely to be conditional. During its life cycle, the virus binds to many proteins and modifies the expression of multiple genes creating a host/pathogen interactome involving 1347 host genes. This data set is heavily enriched in the susceptibility genes for multiple sclerosis (P = 1.3E-99) > Alzheimer's disease > schizophrenia > Parkinsonism > depression > bipolar disorder > childhood obesity > chronic fatigue > autism > and anorexia (P = 0.047) but not attention deficit hyperactivity disorder, a relationship maintained for genome-wide association study data sets in multiple sclerosis and Alzheimer's disease. Overlapping susceptibility gene/interactome data sets disrupt signalling networks relevant to each disease, suggesting that disease susceptibility genes may filter the attentions of the pathogen towards particular pathways and pathologies. In this way, the same pathogen could contribute to multiple diseases in a gene-dependent manner and condition the risk-promoting effects of the genes whose function it disrupts. PMID:23913659
Carter, Chris J
Division protein interaction web: identification of a phylogenetically conserved common interactome between Streptococcus pneumoniae and Escherichia coli.
The ability of each of the 11 Streptococcus pneumoniae division proteins to interact with itself and with each of the remaining proteins was studied in 66 combinations of protein pairs, using a bacterial two-hybrid system. Interactions (homo- or hetero-dimerizations) were detected between 37 protein pairs, whereas 29 protein pairs did not interact. In some cases, positive interactions of the S. pneumoniae proteins were confirmed by co-immunoprecipitation experiments in Escherichia coli. Comparison between the S. pneumoniae division protein interaction web and that of E. coli, the only micro-organisms for which the whole division interactome has been described systematically, was also performed. At least nine division proteins, ZapA, FtsZ, FtsA, FtsK, FtsQ/DivIB, FtsB/DivIC, FtsL, FtsI and FtsW, are believed to have a conserved function between these bacteria and thus we may say that a significant part of the interactions are conserved. Out of 45 protein pairs tested in both bacteria, 30 showed the same behaviour: 23 interacted while seven did not. In agreement with these results, cross-interactions between S. pneumoniae proteins and the corresponding E. coli orthologues were observed. Taken together, these results suggest a phylogenetically conserved minimal common interactome of the division proteins. PMID:18832310
Maggi, Silvia; Massidda, Orietta; Luzi, Giuseppe; Fadda, Daniela; Paolozzi, Luciano; Ghelardini, Patrizia
Toxoplasmosis and Polygenic Disease Susceptibility Genes: Extensive Toxoplasma gondii Host/Pathogen Interactome Enrichment in Nine Psychiatric or Neurological Disorders
Toxoplasma gondii is not only implicated in schizophrenia and related disorders, but also in Alzheimer's or Parkinson's disease, cancer, cardiac myopathies, and autoimmune disorders. During its life cycle, the pathogen interacts with ~3000 host genes or proteins. Susceptibility genes for multiple sclerosis, Alzheimer's disease, schizophrenia, bipolar disorder, depression, childhood obesity, Parkinson's disease, attention deficit hyperactivity disorder (P??from??8.01E ? 05??(ADHD)??to??1.22E ? 71) (multiple sclerosis), and autism (P = 0.013), but not anorexia or chronic fatigue are highly enriched in the human arm of this interactome and 18 (ADHD) to 33% (MS) of the susceptibility genes relate to it. The signalling pathways involved in the susceptibility gene/interactome overlaps are relatively specific and relevant to each disease suggesting a means whereby susceptibility genes could orient the attentions of a single pathogen towards disruption of the specific pathways that together contribute (positively or negatively) to the endophenotypes of different diseases. Conditional protein knockdown, orchestrated by T. gondii proteins or antibodies binding to those of the host (pathogen derived autoimmunity) and metabolite exchange, may contribute to this disruption. Susceptibility genes may thus be related to the causes and influencers of disease, rather than (and as well as) to the disease itself.
Carter, C. J.
Beclin 1 interactome controls the crosstalk between apoptosis, autophagy and inflammasome activation: impact on the aging process.
Autophagy and apoptosis are crucial cellular housekeeping and tissue survival mechanisms. There is emerging evidence of important crosstalk between apoptosis and autophagy which can be linked to inflammasome activation. Beclin 1 is a platform protein which assembles an interactome consisting of diverse proteins which control the initiation of autophagocytosis and distinct phases in endocytosis. Recent studies have demonstrated that the anti-apoptotic Bcl-2 family members can interact with Beclin 1 and inhibit autophagy. Consequently, impaired autophagy can trigger inflammasome activation. Interestingly, the hallmarks of the ageing process include a decline in autophagy, increased resistance to apoptosis and a low-grade inflammatory phenotype. Age-related stresses, e.g. genotoxic, metabolic and environmental insults, enhance the expression of NF-?B-driven anti-apoptotic Bcl-2 proteins which repress the Beclin 1-dependent autophagy. Suppression of autophagocytosis provokes inflammation including NF-?B activation which further potentiates anti-apoptotic defence. In a context-dependent manner, this feedback defence mechanism can enhance the aging process or provoke tumorigenesis or cellular senescence. We will review the role of Beclin 1 interactome in the crosstalk between apoptosis, autophagy and inflammasomes emphasizing that disturbances in Beclin 1-dependent autophagy can have a crucial impact on the aging process. PMID:23220384
Salminen, Antero; Kaarniranta, Kai; Kauppinen, Anu
Quantitative network mapping of the human kinome interactome reveals new clues for rational kinase inhibitor discovery and individualized cancer therapy.
The human kinome is gaining importance through its promising cancer therapeutic targets, yet no general model to address the kinase inhibitor resistance has emerged. Here, we constructed a systems biology-based framework to catalogue the human kinome, including 538 kinase genes, in the broader context of the human interactome. Specifically, we constructed three networks: a kinase-substrate interaction network containing 7,346 pairs connecting 379 kinases to 36,576 phosphorylation sites in 1,961 substrates, a protein-protein interaction network (PPIN) containing 92,699 pairs, and an atomic resolution PPIN containing 4,278 pairs. We identified the conserved regulatory phosphorylation motifs (e.g., Ser/Thr-Pro) using a sequence logo analysis. We found the typical anticancer target selection strategy that uses network hubs as drug targets, might lead to a high adverse drug reaction risk. Furthermore, we found the distinct network centrality of kinases creates a high anticancer drug resistance risk by feedback or crosstalk mechanisms within cellular networks. This notion is supported by the systematic network and pathway analyses that anticancer drug resistance genes are significantly enriched as hubs and heavily participate in multiple signaling pathways. Collectively, this comprehensive human kinome interactome map sheds light on anticancer drug resistance mechanisms and provides an innovative resource for rational kinase inhibitor design. PMID:25003367
Cheng, Feixiong; Jia, Peilin; Wang, Quan; Zhao, Zhongming
JAK2-Centered Interactome Hotspot Identified by an Integrative Network Algorithm in Acute Stanford Type A Aortic Dissection
The precise mechanisms underlying dissections, especially those without connective tissue diseases or congenital vascular diseases, are incompletely understood. This study attempted to identify both the expression profile of the dissected ascending aorta and the interactome hotspots associated with the disease, using microarray technology and gene regulatory network analysis. There were 2,737 genes differentially expressed between patients with acute Stanford type A aortic dissection and controls. Eight interactome hotspots significantly associated with aortic dissection were identified by an integrative network algorithm. In particular, we identified a JAK2-centered expression module, which was validated in an independent gene expression microarray data set, and which was characterized by over-expressed cytokines and receptors in acute aortic dissection cases, indicating that JAK2 may play a key role in the inflammatory process, which potentially contributes to the occurrence of acute aortic dissection. Overall, the analytical strategy used in this study offered the possibility to identify functional relevant network modules and subsequently facilitated the biological interpretation in the complicated disease.
Teschendorff, Andrew E.; Hong, Tao; Wang, Linyan; Qian, Mengjia; Wang, Chunsheng; Wang, Xiangdong
Interactome Profile of the Host Cellular Proteins and the Nonstructural Protein 2 of Porcine Reproductive and Respiratory Syndrome Virus
The nonstructural protein 2 (NSP2) is considered to be one of crucial viral proteins in the replication and pathogenesis of porcine reproductive and respiratory syndrome virus (PRRSV). In the present study, the host cellular proteins that interact with the NSP2 of PRRSV were immunoprecipitated with anti-Myc antibody from the MARC-145 cells infected by a recombinant PRRSV with 3xMyc tag insertion in its NSP2-coding region, and then 285 cellular proteins interacting with NSP2 were identified by LC-MS/MS. The Gene Ontology and enriched KEGG Pathway bioinformatics analyses indicated that the identified proteins could be assigned to different subcellular locations and functional classes. Functional analysis of the interactome profile highlighted cellular pathways associated with infectious disease, translation, immune system, nervous system and signal transduction. Two interested cellular proteins�BCL2-associated athanogene 6 (BAG6) and apoptosis-inducing factor 1 (AIF1) which may involve in transporting of NSP2 to Endoplasmic reticulum (ER) or PRRSV-driven apoptosis were validated by Western blot. The interactome data between PRRSV NSP2 and cellular proteins contribute to the understanding of the roles of NSP2 in the replication and pathogenesis of PRRSV, and also provide novel cellular target proteins for elucidating the associated molecular mechanisms of the interaction of host cellular proteins with viral proteins in regulating the viral replication.
Zhang, Han; Li, Yan; Ge, Xinna; Guo, Xin; Yu, Kangzhen; Yang, Hanchun
Determination of Protein Interactome of Transcription Factor Sox2 in Embryonic Stem Cells Engineered for Inducible Expression of Four Reprogramming Factors*
Unbiased proteomic screens provide a powerful tool for defining protein-protein interaction networks. Previous studies employed multidimensional protein identification technology to identify the Sox2-interactome in embryonic stem cells (ESC) undergoing differentiation in response to a small increase in the expression of epitope-tagged Sox2. Thus far the Sox2-interactome in ESC has not been determined. To identify the Sox2-interactome in ESC, we engineered ESC for inducible expression of different combinations of epitope-tagged Sox2 along with Oct4, Klf4, and c-Myc. Epitope-tagged Sox2 was used to circumvent the lack of suitable Sox2 antibodies needed to perform an unbiased proteomic screen of Sox2-associated proteins. Although i-OS-ESC differentiate when both Oct4 and Sox2 are elevated, i-OSKM-ESC do not differentiate even when the levels of the four transcription factors are coordinately elevated ?2�3-fold. Our findings with i-OS-ESC and i-OSKM-ESC provide new insights into the reasons why ESC undergo differentiation when Sox2 and Oct4 are elevated in ESC. Importantly, the use of i-OSKM-ESC enabled us to identify the Sox2-interactome in undifferentiated ESC. Using multidimensional protein identification technology, we identified >70 proteins that associate with Sox2 in ESC. We extended these findings by testing the function of the Sox2-assoicated protein Smarcd1 and demonstrate that knockdown of Smarcd1 disrupts the self-renewal of ESC and induces their differentiation. Together, our work provides the first description of the Sox2-interactome in ESC and indicates that Sox2 along with other master regulators is part of a highly integrated protein-protein interaction landscape in ESC.
Gao, Zhiguang; Cox, Jesse L.; Gilmore, Joshua M.; Ormsbee, Briana D.; Mallanna, Sunil K.; Washburn, Michael P.; Rizzino, Angie
Estimation of the trend function for spatio-temporal models
Spatiotemporal models have been applied in several scientific disciplines. A crucial problem is estimation of the trend function. Although nonparametric regression for spatial data has been studied in many papers, it is not the case for spatio-temporal data. In this article, we propose a local linear fitting method for spatio-temporal data and investigate the problem under what conditions the proposed
Hongxia Wang; Jinde Wang
Spatiotemporal electromagnetic soliton and spatial ring formation in nonlinear metamaterials
We present a systematic investigation of ultrashort electromagnetic pulse propagation in metamaterials (MMs) with simultaneous cubic electric and magnetic nonlinearity. We predict that spatiotemporal electromagnetic solitons may exist in the positive-index region of a MM with focusing nonlinearity and anomalous group velocity dispersion (GVD), as well as in the negative-index region of the MM with defocusing nonlinearity and normal GVD. The experimental circumstances for generating and manipulating spatiotemporal electromagnetic solitons can be created by elaborating appropriate MMs. In addition, we find that, in the negative-index region of a MM, a spatial ring may be formed as the electromagnetic pulse propagates for focusing nonlinearity and anomalous GVD; while the phenomenon of temporal splitting of the electromagnetic pulse may appear for the same case except for the defocusing nonlinearity. Finally, we demonstrate that the nonlinear magnetization makes the sign of effective electric nonlinear effect switchable due to the combined action of electric and magnetic nonlinearity, exerting a significant influence on the propagation of electromagnetic pulses.
Zhang Jinggui; Wen Shuangchun; Xiang Yuanjiang; Wang Youwen; Luo Hailu [Key Laboratory for Micro/Nano Optoelectronic Devices of Ministry of Education, School of Computer and Communication, Hunan University, Changsha 410082 (China)
Spatiotemporal chaos in Easter Island ecology.
This paper demonstrates that a recently proposed spatiotemporal model for the ecology of Easter Island admits periodic and chaotic attractors, not previously reported. Such behavior may more realistically depict the population dynamics of general ecosystems and illustrates the power of simple models to produce the kind of complex behavior that is ubiquitous in such systems. PMID:22980451
Sprott, J C
Spatiotemporal Segmentation Based on Region Merging
This paper proposes a technique for spatio-temporal segmentation to identify the objects present in the scene represented in a video sequence. This technique processes two consecutive frames at a time. A region-merging approach is used to identify the objects in the scene. Starting from an oversegmentation of the current frame, the objects are formed by iteratively merging regions together. Regions
Fabrice Moscheni; Sushil Bhattacharjee; Murat Kunt
On the Generation of Spatiotemporal Datasets
An efficient benchmarking environment for spatiotemporal access methods should at least include modules for generating synthetic datasets, storing datasets (real datasets included), collecting and running access structures, and visualizing experimental results. Focusing on the dataset repository module, a collection of synthetic data that would simulate a variety of real life scenarios is required. Several algorithms have been implemented in the
Yannis Theodoridis; Jefferson R. O. Silva; Mario A. Nascimento
Spatiotemporal Relational Probability Trees: An Introduction
We introduce spatiotemporal relational probability trees (SRPTs), probability estimation trees for relational data that can vary in both space and time. The SRPT algo- rithm addresses the exponential increase in search complex- ity through sampling. We validate the SRPT using a sim- ulated data set and we empirically demonstrate the SRPT algorithm on two real-world data sets.
Amy Mcgovern; Nathan C. Hiers; Matthew W. Collier; David J. Gagne II; Rodger A. Brown
Neural mechanisms of spatiotemporal signal processing
We have studied the synaptic, dendritic, and network mechanisms of spatiotemporal signal processing underlying the computation of visual motion in the avian tectum. Such mechanisms are critical for information processing in all vertebrates, but have been difficult to elucidate in mammals because of anatomical limitations. We have therefore developed a chick tectal slice preparation, which has features that help us
Spatiotemporal Coherent Control of Lattice Vibrational Waves
We achieved automated optical control over coherent lattice responses that were both time- and position-dependent across macroscopic length scales. In our experiments, spatiotemporal femtosecond pulse shaping was used to generate excitation light fields that were directed toward distinct regions of crystalline samples, producing terahertz-frequency lattice vibrational waves that emanated outward from their multiple origins at lightlike speeds. Interferences among the
T. Feurer; Joshua C. Vaughan; Keith A. Nelson
Working with Spatio-Temporal Data Type
NASA Astrophysics Data System (ADS)
Several aspects of spatiotemporal databases have been explored in past decades, ranging from basic data structure to query processing and indexing. But today, operational temporal GIS does not exist. The key impediments have been the complexity of integrating space and time and the lack of standards. OpenGIS standards for simple feature access (spatial type) do exist, but unlike the spatial type, standards for spatiotemporal data type do not exist. This paper explores a new approach to modeling space and time to provide the basis for implementing a temporal GIS. This approach is based on the concept of data types in databases. A data type provides constructors, accessors, and operators. Most commercial and open source databases provide data types to deal with the spatial component of a GIS, called spatial type. Oracle Spatial, DB2 Spatial Extender and Informix Spatial DataBlade, ST_Geometry for PostgreSQL and Oracle from Esri, PostGIS for PostgreSQL, etc., are some examples. This new spatiotemporal data type is called spatiotemporal type (STT). This STT is implemented in PostgreSQL, an open source relational database management system. The STT is an extension of Esri's ST_Geometry type for PostgreSQL, in which each spatial object has a life span. Constructors, accessors, and relational functions are provided to create STT and support spatial, spatiotemporal, and temporal queries. Some functions are extended based on OpenGIS standards for the spatial type. Examples are provided to demonstrate the application of these functions. The paper concludes with limitations and challenges of implementing STT.
Raza, A.
Annotating spatio-temporal datasets for meaningful analysis in the Web
NASA Astrophysics Data System (ADS)
More and more environmental datasets that vary in space and time are available in the Web. This comes along with an advantage of using the data for other purposes than originally foreseen, but also with the danger that users may apply inappropriate analysis procedures due to lack of important assumptions made during the data collection process. In order to guide towards a meaningful (statistical) analysis of spatio-temporal datasets available in the Web, we have developed a Higher-Order-Logic formalism that captures some relevant assumptions in our previous work [1]. It allows to proof on meaningful spatial prediction and aggregation in a semi-automated fashion. In this poster presentation, we will present a concept for annotating spatio-temporal datasets available in the Web with concepts defined in our formalism. Therefore, we have defined a subset of the formalism as a Web Ontology Language (OWL) pattern. It allows capturing the distinction between the different spatio-temporal variable types, i.e. point patterns, fields, lattices and trajectories, that in turn determine whether a particular dataset can be interpolated or aggregated in a meaningful way using a certain procedure. The actual annotations that link spatio-temporal datasets with the concepts in the ontology pattern are provided as Linked Data. In order to allow data producers to add the annotations to their datasets, we have implemented a Web portal that uses a triple store at the backend to store the annotations and to make them available in the Linked Data cloud. Furthermore, we have implemented functions in the statistical environment R to retrieve the RDF annotations and, based on these annotations, to support a stronger typing of spatio-temporal datatypes guiding towards a meaningful analysis in R. [1] Stasch, C., Scheider, S., Pebesma, E., Kuhn, W. (2014): "Meaningful spatial prediction and aggregation", Environmental Modelling & Software, 51, 149-165.
Stasch, Christoph; Pebesma, Edzer; Scheider, Simon
An Integrated Framework of Spatiotemporal Dynamics of Binocular Rivalry
Fluctuations in perceptual dominance during binocular rivalry exhibit several hallmark characteristics. First, dominance switches are not periodic but, instead, stochastic: perception changes unpredictably. Second, despite being stochastic, average durations of rivalry dominance vary dependent on the strength of the rival stimuli: variations in contrast, luminance, or spatial frequency produce predictable changes in average dominance durations and, hence, in alternation rate. Third, perceptual switches originate locally and spread globally over time, sometimes as traveling waves of dominance: rivalry transitions are spatiotemporal events. This essay (1) reviews recent advances in our understanding of the bases of these three hallmark characteristics of binocular rivalry dynamics and (2) provides an integrated framework to account for those dynamics using cooperative and competitive spatial interactions among local neural circuits distributed over the visual field's retinotopic map. We close with speculations about how that framework might incorporate top-down influences on rivalry dynamics.
Kang, Min-Suk; Blake, Randolph
The otoferlin interactome in neurosensory hair cells: significance for synaptic vesicle release and trans-Golgi network (Review).
Sound perception in terrestrial vertebrates relies on a structure in the inner ear consisting of the utriculus, sacculus and lagena. In mammals, the lagena has developed into the cochlea where mechanotransduction at ciliated cells leads to ion influx via regulated ion channels. To maintain proper Ca2+ concentration many cellular systems use a variety of functional proteins; the neurosensory systems use calcium-sensors like hippocalcin, visinin or recoverin. In cochlear hair cells the 230 kDa protein otoferlin has been suggested to play this role. While several observations support this hypothesis additional data argue for a more expanded functional profile of otoferlin. Evidence for otoferlin's multiple roles and newer results on otoferlin's interacting partners are presented and the existence of a protein complex as a functional unit ('interactome') in the cochlea and further tissues is suggested. PMID:21643623
Zak, Magdalena; Pfister, Markus; Blin, Nikolaus
Identification of the hypoxia-inducible factor 2? nuclear interactome in melanoma cells reveals master proteins involved in melanoma development.
Hypoxia-inducible factors (HIFs) are heterodimeric transcription factors that play a key role in cellular adaptation to hypoxia. HIF proteins are composed of an ? subunit regulated by oxygen pressure (essentially HIF1? or HIF2?) and a constitutively expressed ? subunit. These proteins are often overexpressed in cancer cells, and HIF overexpression frequently correlates with poor prognosis, making HIF proteins promising therapeutic targets. HIF proteins are involved in melanoma initiation and progression; however, the specific function of HIF2 in melanoma has not yet been studied comprehensively. Identifying protein complexes is a valuable way to uncover protein function, and affinity purification coupled with mass spectrometry and label-free quantification is a reliable method for this approach. We therefore applied quantitative interaction proteomics to identify exhaustively the nuclear complexes containing HIF2? in a human melanoma cell line, 501mel. We report, for the first time, a high-throughput analysis of the interactome of an HIF subunit. Seventy proteins were identified that interact with HIF2?, including some well-known HIF partners and some new interactors. The new HIF2? partners microphthalmia-associated transcription factor, SOX10, and AP2?, which are master actors of melanoma development, were confirmed via co-immunoprecipitation experiments. Their ability to bind to HIF1? was also tested: microphthalmia-associated transcription factor and SOX10 were confirmed as HIF1? partners, but the transcription factor AP2? was not. AP2? expression correlates with low invasive capacities. Interestingly, we demonstrated that when HIF2? was overexpressed, only cells expressing large amounts of AP2? exhibited decreased invasive capacities in hypoxia relative to normoxia. The simultaneous presence of both transcription factors therefore reduces cells' invasive properties. Knowledge of the HIF2? interactome is thus a useful resource for investigating the general mechanisms of HIF function and regulation, and here we reveal unexpected, distinct roles for the HIF1 and HIF2 isoforms in melanoma progression. PMID:23275444
Steunou, Anne-Lise; Ducoux-Petit, Manuelle; Lazar, Ikrame; Monsarrat, Bernard; Erard, Monique; Muller, Catherine; Clottes, Eric; Burlet-Schiltz, Odile; Nieto, Laurence
Systematic differences in signal emitting and receiving revealed by PageRank analysis of a human protein interactome.
Most protein PageRank studies do not use signal flow direction information in protein interactions because this information was not readily available in large protein databases until recently. Therefore, four questions have yet to be answered: A) What is the general difference between signal emitting and receiving in a protein interactome? B) Which proteins are among the top ranked in directional ranking? C) Are high ranked proteins more evolutionarily conserved than low ranked ones? D) Do proteins with similar ranking tend to have similar subcellular locations? In this study, we address these questions using the forward, reverse, and non-directional PageRank approaches to rank an information-directional network of human proteins and study their evolutionary conservation. The forward ranking gives credit to information receivers, reverse ranking to information emitters, and non-directional ranking mainly to the number of interactions. The protein lists generated by the forward and non-directional rankings are highly correlated, but those by the reverse and non-directional rankings are not. The results suggest that the signal emitting/receiving system is characterized by key-emittings and relatively even receivings in the human protein interactome. Signaling pathway proteins are frequent in top ranked ones. Eight proteins are both informational top emitters and top receivers. Top ranked proteins, except a few species-related novel-function ones, are evolutionarily well conserved. Protein-subunit ranking position reflects subunit function. These results demonstrate the usefulness of different PageRank approaches in characterizing protein networks and provide insights to protein interaction in the cell. PMID:23028653
Du, Donglei; Lee, Connie F; Li, Xiu-Qing
Identification of the Hypoxia-inducible Factor 2? Nuclear Interactome in Melanoma Cells Reveals Master Proteins Involved in Melanoma Development*
Hypoxia-inducible factors (HIFs) are heterodimeric transcription factors that play a key role in cellular adaptation to hypoxia. HIF proteins are composed of an ? subunit regulated by oxygen pressure (essentially HIF1? or HIF2?) and a constitutively expressed ? subunit. These proteins are often overexpressed in cancer cells, and HIF overexpression frequently correlates with poor prognosis, making HIF proteins promising therapeutic targets. HIF proteins are involved in melanoma initiation and progression; however, the specific function of HIF2 in melanoma has not yet been studied comprehensively. Identifying protein complexes is a valuable way to uncover protein function, and affinity purification coupled with mass spectrometry and label-free quantification is a reliable method for this approach. We therefore applied quantitative interaction proteomics to identify exhaustively the nuclear complexes containing HIF2? in a human melanoma cell line, 501mel. We report, for the first time, a high-throughput analysis of the interactome of an HIF subunit. Seventy proteins were identified that interact with HIF2?, including some well-known HIF partners and some new interactors. The new HIF2? partners microphthalmia-associated transcription factor, SOX10, and AP2?, which are master actors of melanoma development, were confirmed via co-immunoprecipitation experiments. Their ability to bind to HIF1? was also tested: microphthalmia-associated transcription factor and SOX10 were confirmed as HIF1? partners, but the transcription factor AP2? was not. AP2? expression correlates with low invasive capacities. Interestingly, we demonstrated that when HIF2? was overexpressed, only cells expressing large amounts of AP2? exhibited decreased invasive capacities in hypoxia relative to normoxia. The simultaneous presence of both transcription factors therefore reduces cells' invasive properties. Knowledge of the HIF2? interactome is thus a useful resource for investigating the general mechanisms of HIF function and regulation, and here we reveal unexpected, distinct roles for the HIF1 and HIF2 isoforms in melanoma progression.
Steunou, Anne-Lise; Ducoux-Petit, Manuelle; Lazar, Ikrame; Monsarrat, Bernard; Erard, Monique; Muller, Catherine; Clottes, Eric; Burlet-Schiltz, Odile; Nieto, Laurence
Spatiotemporal dynamics and optical vortices in a photorefractive phase-conjugate resonator
NASA Technical Reports Server (NTRS)
A truncated modal expansion approach is used to study the spatiotemporal dynamics of a phase-conjugate resonator as a function of Bragg detuning. The numerical results reveal a rich variety of behaviors. Emphasis is given to the spatial distribution of optical vortices, their trajectories and their relationship to the beam's spatial coherence. The limitations of the model are discussed and experimental results are presented for comparison with the model's predictions and assessment of its soundness.
Liu, Siuying Raymond; Indebetouw, Guy
Experimental observation of a skewed X-type spatiotemporal correlation of ultrabroadband twin beams.
This work presents the experimental observation of the nonfactorable near-field spatiotemporal correlation of ultrabroadband twin beams generated by parametric down-conversion, in an interferometric-type experiment using sum frequency generation, where both the temporal and the spatial degrees of freedom of parametric down-conversion light are controlled with high resolution. The revealed correlation is skewed in space-time in accordance with the X structure predicted by the theory. PMID:23368318
Jedrkiewicz, O; Gatti, A; Brambilla, E; Di Trapani, P
Spatio-temporal pattern formation on spherical surfaces: numerical simulation and application to solid tumour growth
.�� In this paper we examine spatio-temporal pattern formation in reaction-diffusion systems on the surface of the unit sphere\\u000a in 3D. We first generalise the usual linear stability analysis for a two-chemical system to this geometrical context. Noting\\u000a the limitations of this approach (in terms of rigorous prediction of spatially heterogeneous steady-states) leads us to develop,\\u000a as an alternative, a
M. A. J. Chaplain; M. Ganesh; I. G. Graham
What Is Spatio-Temporal Data Warehousing?
NASA Astrophysics Data System (ADS)
In the last years, extending OLAP (On-Line Analytical Processing) systems with spatial and temporal features has attracted the attention of the GIS (Geographic Information Systems) and database communities. However, there is no a commonly agreed definition of what is a spatio-temporal data warehouse and what functionality such a data warehouse should support. Further, the solutions proposed in the literature vary considerably in the kind of data that can be represented as well as the kind of queries that can be expressed. In this paper we present a conceptual framework for defining spatio-temporal data warehouses using an extensible data type system. We also define a taxonomy of different classes of queries of increasing expressive power, and show how to express such queries using an extension of the tuple relational calculus with aggregated functions.
Vaisman, Alejandro; Zim�nyi, Esteban
Spatiotemporal dynamics of skeins of wild geese
NASA Astrophysics Data System (ADS)
We performed a spatiotemporal analysis of a one-dimensional array of airborne geese. The one-dimensional structure of individuals exhibits large fluctuations with backward propagation. In field measurements, we measured properties such as the phase velocity, the dispersion relation, and effective interactions. We also proposed a simplified model for nonlinear wave propagation, which is identical to that for highway traffic flow. Our results strongly imply that fluctuations in the array originate from the excitation of collective modes in its intrinsic dynamics.
Hayakawa, Y.
Data assimilation using spatio-temporal descriptors
NASA Astrophysics Data System (ADS)
Data assimilation is the process by which numerical model output is fused with observations in order to provide consensus estimates. In a Bayesian framework, this typically consists of constructing a 'process model prior' centred on the numerical model output and an 'observation model' which describes the relationship between the observed variable and the process of interest. This approach, while straightforward and ubiquitous in the geophysical sciences, can lead to erroneous inferences when the numerical output is biased (both spatially and temporally) in an undefined way. Here we show an alternative way in which to carry out data assimilation, whereby only the spatial and temporal properties of the numerical model are fused with the data. The method, couched in a spatio-temporal Bayesian framework, follows a two-stage approach: (i) Spatio-temporal modelling of the numerical model outputs in order to extract spectral spatio-temporal characteristics which are deemed faithful to the processes of interest (e.g. length scales and marginal variances), and (ii) Spatio-temporal modelling of the processes of interest with informative priors (based on (i)) in order to provide updated estimates. We apply this method to estimating the mass balance of Antarctic ice-sheet processes from multiple observations sources: GRACE, ICESat, ENVISat and GPS data. We show that although this problem is under-determined due to lack of observation diversity, spectral characterisation using the two-stage approach allows us to tease out the individual processes and reduce confounding between the processes whilst concurrently providing inferences which are largely data-driven.
Zammit-Mangion, Andrew; Schoen, Nana; Rougier, Jonathan; Bamber, Jonathan
Spatiotemporal Visual Function in Tinted Lens Wearers
METHODS. Twenty children (13.1 6 0.9 years of age) were recruited who had successfully worn tinted lenses for at least 6 months and were compared with an age-matched control group (12.6 6 2.2 years of age) of 21 children who were not lens wearers. A range of psychophysical tasks was adapted to identify specific anomalous visual perceptions. Spatiotemporal contrast sensitivity
Anita J. Simmers; Peter J. Bex; Fiona K. H. Smith; Arnold J. Wilkins
Nearest matched filter classification of spatiotemporal patterns.
Recent advances in massively parallel optical and electronic neural network processing technology have made it plausible to consider the use of matched filter banks containing large numbers of individual filters as pattern classifiers for complex spatiotemporal pattern environments such as speech, sonar, radar, and advanced communications. This paper begins with an overview of how neural networks can be used to approximately implement such multidimensional matched filter banks. The nearest matched filter classifier is then formally defined. This definition is then reformulated to show that the classifier is equivalent to a nearest neighbor classifier in a separable infinite-dimensional metric space that specifies the local-in-time behavior of spatiotemporal patterns. The result of Cover and Hart is then applied to show that, given a statistically comprehensive set of filter templates, the nearest matched filter classifier will have near-Bayesian performance for spatiotemporal patterns. The combination of near-Bayesian classifier performance with the excellent performance of matched filtering in noise yields a powerful new classification technique. This result adds additional interest to Grossberg's hypothesis that the mammalian cerebral cortex carries out local-in-time nearest matched filter classification of both auditory and visual sensory inputs as an initial step in sensory pattern recognition-which may help explain the almost instantaneous pattern recognition capabilities of animals. PMID:20454418
Hecht-Nielsen, R
Spatio-temporal chaos: A solvable model
NASA Astrophysics Data System (ADS)
A solvable coupled map lattice model exhibiting spatio-temporal chaos is studied. Exact expressions are obtained for the spectra of Lyapunov exponents as a function of the model parameters. Although the model has spatio-temporal structure, the time series measured at a single lattice site are shown to consist of independent, identically distributed samples for several values of the model parameters. For these parameter values, the spatial series measured at a fixed time also consist of independent, identically distributed samples. In these cases, the information dimension density is 1, but the information entropy density depends on the model parameters. Thus, the model is an example where the information entropy density can be obtained neither from a time series measured at a single lattice site nor from a spatial series measured at a fixed time. We conclude that in studying only a time series or a spatial series without any knowledge of the system, one could be easily led into thinking that there is no spatio-temporal structure. For a full characterization of the system, structure in time and space will have to be considered simultaneously.
Diks, C.; Takens, F.; DeGeode, J.
Host-Mycobacterium avium subsp. paratuberculosis interactome reveals a novel iron assimilation mechanism linked to nitric oxide stress during early infection
Background The initial interaction between host cell and pathogen sets the stage for the ensuing infection and ultimately determine the course of disease. However, there is limited knowledge of the transcripts utilized by host and pathogen and how they may impact one another during this critical step. The purpose of this study was to create a host-Mycobacterium avium subsp. paratuberculosis (MAP) interactome for early infection in an epithelium-macrophage co-culture system using RNA-seq. Results Establishment of the host-MAP interactome revealed a novel iron assimilation system for carboxymycobactin. Iron assimilation is linked to nitric oxide synthase-2 production by the host and subsequent nitric oxide buildup. Iron limitation as well as nitric oxide is a prompt for MAP to enter into an iron sequestration program. This new iron sequestration program provides an explanation for mycobactin independence in some MAP strains grown in vitro as well as during infection within the host cell. Utilization of such a pathway is likely to aid MAP establishment and long-term survival within the host. Conclusions The host-MAP interactome identified a number of metabolic, DNA repair and virulence genes worthy for consideration as novel drug targets as well as future pathogenesis studies. Reported interactome data may also be utilized to conduct focused, hypothesis-driven research. Co-culture of uninfected bovine epithelial cells (MAC-T) and primary bovine macrophages creates a tolerant genotype as demonstrated by downregulation of inflammatory pathways. This co-culture system may serve as a model to investigate other bovine enteric pathogens.
A Hierarchical Bayesian Approach for Learning Sparse Spatio-Temporal Decomposition of Multichannel EEG
Multichannel electroencephalography (EEG) offers a non-invasive tool to explore spatio-temporal dynamics of brain activity. With EEG recordings consisting of multiple trials, traditional signal processing approaches that ignore inter-trial variability in the data may fail to accurately estimate the underlying spatio-temporal brain patterns. Moreover, precise characterization of such inter-trial variability per se can be of high scientific value in establishing the relationship between brain activity and behavior. In this paper, a statistical modeling framework is introduced for learning spatiotemporal decomposition of multiple-trial EEG data recorded under two contrasting experimental conditions. By modeling the variance of source signals as random variables varying across trials, the proposed two-stage hierarchical Bayesian model is able to capture inter-trial amplitude variability in the data in a sparse way where a parsimonious representation of the data can be obtained. A variational Bayesian (VB) algorithm is developed for statistical inference of the hierarchical model. The efficacy of the proposed modeling framework is validated with the analysis of both synthetic and real EEG data. In the simulation study we show that even at low signal-to-noise ratios our approach is able to recover with high precision the underlying spatiotemporal patterns and the evolution of source amplitude across trials; on two brain-computer interface (BCI) data sets we show that our VB algorithm can extract physiologically meaningful spatio-temporal patterns and make more accurate predictions than other two widely used algorithms: the common spatial patterns (CSP) algorithm and the Infomax algorithm for independent component analysis (ICA). The results demonstrate that our statistical modeling framework can serve as a powerful tool for extracting brain patterns, characterizing trial-to-trial brain dynamics, and decoding brain states by exploiting useful structures in the data.
Wu, Wei; Chen, Zhe; Gao, Shangkai; Brown, Emery N.
Next-generation sequencing coupled with a cell-free display technology for high-throughput production of reliable interactome data
Next-generation sequencing (NGS) has been applied to various kinds of omics studies, resulting in many biological and medical discoveries. However, high-throughput protein-protein interactome datasets derived from detection by sequencing are scarce, because protein-protein interaction analysis requires many cell manipulations to examine the interactions. The low reliability of the high-throughput data is also a problem. Here, we describe a cell-free display technology combined with NGS that can improve both the coverage and reliability of interactome datasets. The completely cell-free method gives a high-throughput and a large detection space, testing the interactions without using clones. The quantitative information provided by NGS reduces the number of false positives. The method is suitable for the in vitro detection of proteins that interact not only with the bait protein, but also with DNA, RNA and chemical compounds. Thus, it could become a universal approach for exploring the large space of protein sequences and interactome networks.
Fujimori, Shigeo; Hirai, Naoya; Ohashi, Hiroyuki; Masuoka, Kazuyo; Nishikimi, Akihiko; Fukui, Yoshinori; Washio, Takanori; Oshikubo, Tomohiro; Yamashita, Tatsuhiro; Miyamoto-Sato, Etsuko
Geostatistical Analysis of Spatio-Temporal Forest Fire Data
NASA Astrophysics Data System (ADS)
Forest fire is one of the major phenomena causing degradation of environment, landscape, natural ecosystems, human health and economy. One of the main topic in forest fire data studies deals with the detection, analysis and modelling of spatio-temporal patterns of clustering. Spatial patterns of forest fire locations, their sizes and their sequence in time are of great interest for fire prediction and for forest fire management planning and distribution in optimal way necessary resources. Currently, fires can be analyzed and monitored by using different statistical tools, for example, Ripley's k-function, fractals, Allan factor, scan statistics, etc. Some of them are adapted to temporal or spatial data and are either local or global. In the present study the main attention is paid to the application of geostatistical tools - variography and methods for the analysis of monitoring networks (MN) clustering techniques (topological, statistical and fractal measures), in order to detect and to characterize spatio-temporal forest fire patterns. The main studies performed include: a) analysis of forest fires temporal sequences; b) spatial clustering of forest fires; c) geostatistical spatial analysis of burnt areas. Variography was carried out both for temporal and spatial data. Real case study is based on the forest-fire event data from Canton of Ticino (Switzerland) for a period of 1969 to 2008. The results from temporal analysis show the presence of clustering and seasonal periodicities. Comprehensive analysis of the variograms shows an anisotropy in the direction 30� East-North where smooth changes are detected, while on the direction 30� North-West a greater variability was identified. The research was completed with an application of different MN analysis techniques including, analysis of distributions of distances between events, Morisita Index (MI), fractal dimensions (sandbox counting and box counting methods) and functional fractal dimensions, adapted and applied to characterize spatio-temporal events. The results are compared with the reference patterns (no spatial clustering) simulated within the natural validity domains (forests). The research was partly supported by SNSF projects IZAIZO-12777 and 200020-121835.
Vega Orozco, Carmen D.; Kanevski, Mikhail; Tonini, Marj; Conedera, Marc
Tuberculosis incidence in Portugal: spatiotemporal clustering
Background The statistics of disease clustering is one of the most important tools for epidemiologists to detect and monitor public health disease patterns. Nowadays, tuberculosis (TB) � an infectious disease caused by the Mycobacterium tuberculosis � presents different (development in populations and antibiotics resistance) patterns and specialists are very concerned with it and its association to several other diseases and factors. Each year, tuberculosis kills about three million people in the world. In particular, it is responsible for the death of more than one-third of HIV-infected people, who prove particularly susceptible due to a decline in their immune defences. The purpose of this study is to determine if there are spatiotemporal tuberculosis incidence clusters in continental Portugal. The presented case study is based on the notification of new tuberculosis cases (disease incidence), between 2000 and 2004. In methodological terms, the spatial scan statistic, used to identify spatiotemporal clusters, was improved by including two new approaches: definition of window sizes in the cluster scanning processes considering empirical mean spatial semivariograms and an independent and posterior validation of identified clusters (based on geostatistical simulations). Results Continental Portugal is organized in 18 districts with 278 sub-districts. For this case study, the number of new notified cases of TB, per sub-district and per year (2000�2004) was available. TB incidence presents clear spatial patterns: a semivariogram consistent with 40% of nugget effect and 60% of spatial contribution, following an exponential model with a range of 143 kilometres. Temporal semivariograms were not conclusive, as only 5 years of data were available. The spatial and temporal persistence of clusters were analyzed considering different models. Significant high incidence rate space-time clusters were identified in three areas of Portugal (between 2000 and 2004) and a purely temporal cluster was identified covering the whole country, during 2002. Conclusion In terms of spatiotemporal clustering of tuberculosis disease, the proposed methodology allowed the identification of critical spatiotemporal areas. In Portugal there were 3 critical districts (Porto, Set�bal and Lisbon) with high rates of notified incidences between 2000 and 2004. In methodological terms, semivariogram parameters were successfully applied to define spatiotemporal scan window sizes and shapes (ellipsoidal cylinders), showing very good results and performances in the case study. After defining the clusters, these were authenticated through a validation method, based on geostatistical simulations.
Nunes, Carla
Efficient Index Structures for Spatio-Temporal Objects
In this article we present a family of four tree-based access structures for indexing spatio-temporal objects. Our indexing methods support spatio-temporal, as well as purely spatial and purely temporal queries. In order to han-dle sets of extended spatio-temporal objects we propose to specialize generalized search trees by combining the advan-tages of the well-known spatial structures R*-tree ([1]) and SS-tree ([18]).
Carsten Kleiner; Udo W. Lipeck
Climate-mediated spatiotemporal variability in terrestrial productivity across Europe
NASA Astrophysics Data System (ADS)
Quantifying the interannual variability (IAV) of the terrestrial ecosystem productivity and its sensitivity to climate is crucial for improving carbon budget predictions. In this context it is necessary to disentangle the influence of climate from impacts of other mechanisms underlying the spatiotemporal patterns of IAV of the ecosystem productivity. In this study we investigated the spatiotemporal patterns of IAV of historical observations of European crop yields in tandem with a set of climate variables. We further evaluated if relevant remote-sensing retrievals of NDVI (normalized difference vegetation index) and FAPAR (fraction of absorbed photosynthetically active radiation) depict a similar behaviour. Our results reveal distinct spatial patterns in the IAV of the analysed proxies linked to terrestrial productivity. In particular, we find higher IAV in water-limited regions of Europe (Mediterranean and temperate continental Europe) compared to other regions in both crop yield and remote-sensing observations. Our results further indicate that variations in the water balance during the active growing season exert a more pronounced and direct effect than variations of temperature on explaining the spatial patterns in IAV of productivity-related variables in temperate Europe. Overall, we observe a temporally increasing trend in the IAV of terrestrial productivity and an increasing sensitivity of productivity to water availability in dry regions of Europe during the 1975-2009 period. In the same regions, a simultaneous increase in the IAV of water availability was detected. These findings suggest intricate responses of carbon fluxes to climate variability in Europe and that the IAV of terrestrial productivity has become potentially more sensitive to changes in water availability in the dry regions in Europe. The changing sensitivity of terrestrial productivity accompanied by the changing IAV of climate is expected to impact carbon stocks and the net carbon balance of European ecosystems.
Wu, X.; Babst, F.; Ciais, P.; Frank, D.; Reichstein, M.; Wattenbach, M.; Zang, C.; Mahecha, M. D.
Infinite-dimensional Bayesian filtering for detection of quasiperiodic phenomena in spatiotemporal data
NASA Astrophysics Data System (ADS)
This paper introduces a spatiotemporal resonator model and an inference method for detection and estimation of nearly periodic temporal phenomena in spatiotemporal data. The model is derived as a spatial extension of a stochastic harmonic resonator model, which can be formulated in terms of a stochastic differential equation. The spatial structure is included by introducing linear operators, which affect both the oscillations and damping, and by choosing the appropriate spatial covariance structure of the driving time-white noise process. With the choice of the linear operators as partial differential operators, the resonator model becomes a stochastic partial differential equation, which is compatible with infinite-dimensional Kalman filtering. The resulting infinite-dimensional Kalman filtering problem allows for a computationally efficient solution as the computational cost scales linearly with measurements in the temporal dimension. This framework is applied to weather prediction and to physiological noise elimination in functional magnetic resonance imaging brain data.
Solin, Arno; S�rkk�, Simo
Spatio-temporal population estimates for risk management
NASA Astrophysics Data System (ADS)
Accurate estimation of population at risk from hazards and effective emergency management of events require not just appropriate spatio-temporal modelling of hazards but also of population. While much recent effort has been focused on improving the modelling and predictions of hazards (both natural and anthropogenic), there has been little parallel advance in the measurement or modelling of population statistics. Different hazard types occur over diverse temporal cycles, are of varying duration and differ significantly in their spatial extent. Even events of the same hazard type, such as flood events, vary markedly in their spatial and temporal characteristics. Conceptually and pragmatically then, population estimates should also be available for similarly varying spatio-temporal scales. Routine population statistics derived from traditional censuses or surveys are usually static representations in both space and time, recording people at their place of usual residence on census/survey night and presenting data for administratively defined areas. Such representations effectively fix the scale of population estimates in both space and time, which is unhelpful for meaningful risk management. Over recent years, the Pop24/7 programme of research, based at the University of Southampton (UK), has developed a framework for spatio-temporal modelling of population, based on gridded population surfaces. Based on a data model which is fully flexible in terms of space and time, the framework allows population estimates to be produced for any time slice relevant to the data contained in the model. It is based around a set of origin and destination centroids, which have capacities, spatial extents and catchment areas, all of which can vary temporally, such as by time of day, day of week, season. A background layer, containing information on features such as transport networks and landuse, provides information on the likelihood of people being in certain places at specific times. Unusual patterns associated with special events can also be modelled and the framework is fully volume preserving. Outputs from the model are gridded population surfaces for the specified time slice, either for total population or by sub-groups (e.g. age). Software to implement the models (SurfaceBuilder247) has been developed and pre-processed layers for typical time slices for England and Wales in 2001 and 2006 are available for UK academic purposes. The outputs and modelling framework from the Pop24/7 programme provide significant opportunities for risk management applications. For estimates of mid- to long-term cumulative population exposure to hazards, such as in flood risk mapping, populations can be produced for numerous time slices and integrated with flood models. For applications in emergency response/ management, time-specific population models can be used as seeds for agent-based models or other response/behaviour models. Estimates for sub-groups of the population also permit exploration of vulnerability through space and time. This paper outlines the requirements for effective spatio-temporal population models for risk management. It then describes the Pop24/7 framework and illustrates its potential for risk management through presentation of examples from natural and anthropogenic hazard applications. The paper concludes by highlighting key challenges for future research in this area.
Cockings, Samantha; Martin, David; Smith, Alan; Martin, Rebecca
A provably efficient computational model for approximate spatiotemporal retrieval
The paper is concerned with the effective and efficient processing of spatiotemporal selection queries under varying degrees of approximation. Such queries may employ operators like overlaps, north, during, etc., and their result is a set of entities standing approximately in some spatiotemporal relation with respect to a query object X. The contribution of our work is twofold: i) First we
Delis Vasilis; Makris Christos; Sioutas Spiros
Spatio-Temporal Pattern Recognition Using Hidden Markov Models.
National Technical Information Service (NTIS)
A new spatio-temporal method for identifying 3D objects found in 2D image sequences is presented. The Hidden Markov Model technique is used as a spatio-temporal classification algorithm to identify 3D objects by the temporal changes in observed shape feat...
K. H. Fielding
Spatiotemporal Salient Points for Visual Recognition of Human Actions
This paper addresses the problem of human-action recogni- tion by introducing a sparse representation of image sequences as a collec- tion of spatiotemporal events that are localized at points that are salient both in space and time. The spatiotemporal salient points are detected by measuring the variations in the information content of pixel neighborhoods not only in space but also
Antonios Oikonomopoulos; Ioannis Patras; Maja Pantic
1. SUMMARY This paper concerns a methodology and its supporting prototype tool for database design of spatiotemporal applications. The methodology focuses on the main phases of conceptual and logical modeling with each phase being accompanied by models specifically constructed to handle spatiotemporal peculiarities. A database design tool that guides the designer through the conceptual and logical modeling as well as
Nectaria Tryfona; Christian S. Jensen; Fredrik Bajers Vej
Workload induced spatio-temporal distortions and safety of flight
A theoretical analysis of the relationship between cognitive complexity and the perception of time and distance is presented and experimentally verified. Complex tasks produce high rates of mental representation which affect the subjective sense of duration and, through the subjective time scale, the percept of distance derived from dynamic visual cues (i.e., visual cues requiring rate integration). The analysis of the interrelationship of subjective time and subjective distance yields the prediction that, as a function of cognitive complexity, distance estimates derived from dynamic visual cues will be longer than the actual distance whereas estimates based on perceived temporal duration will be shorter than the actual distance. This prediction was confirmed in an experiment in which subjects (both pilots and non-pilots) estimated distances using either temporal cues or dynamic visual cues. The distance estimation task was also combined with secondary loading tasks in order to vary the overall task complexity. The results indicated that distance estimates based on temporal cues were underestimated while estimates based on visual cues were overestimated. This spatio-temporal distortion effect increased with increases in overall task complexity. 30 refs., 6 figs., 1 tab.
Barrett, C.L.; Weisgerber, S.A. (Los Alamos National Lab., NM (USA); Naval Weapons Center, China Lake, CA (USA))
Noise-induced re-entrant spatio-temporal intermittency
NASA Astrophysics Data System (ADS)
We investigate the influence of noise on the spatio-temporal behavior of a simple model which has a subcritical bifurcation. We find that with increasing noise strength the spatio-temporal intermittency is first replaced by a low-amplitude noisy regime followed by spatio-temporal intermittency embedded into a noisy background. At sufficiently high noise intensity high-amplitude noise prevails. We point out that the transition from spatio-temporal intermittency to low-amplitude noise can be traced back to the conversion of a saddle point at zero amplitude for the deterministic system to a noise-stabilized fixed point. As the noise grows further, the noisy state around zero starts to communicate with a noisy limit cycle leading to noise-induced spatio-temporal intermittency. At high enough noise strength, high-amplitude noise is left over wiping out all details of the underlying deterministic dynamical system.
Hayase, Y.; Brand, H. R.
Bayesian spatiotemporal modelling for the assessment of short-term exposure to particle pollution in urban areas
This paper describes a Bayesian hierarchical approach to predict short-term concentrations of particle pollution in an urban environment, with application to inhalable particulate matter (PM10) in Greater London. We developed and compared several spatiotemporal models that differently accounted for factors affecting the spatiotemporal properties of particle concentrations. We considered two main source contributions to ambient measurements: (i) the long-range transport of the secondary fraction of particles, which temporal variability was described by a latent variable derived from rural concentrations; and (ii) the local primary component of particles (traffic- and non-traffic-related) captured by the output of the dispersion model ADMS-Urban, which site-specific effect was described by a Bayesian kriging. We also assessed the effect of spatiotemporal covariates, including type of site, daily temperature to describe the seasonal changes in chemical processes affecting local PM10 concentrations that are not considered in local-scale dispersion models and day of the week to account for time-varying emission rates not available in emissions inventories. The evaluation of the predictive ability of the models, obtained via a cross-validation approach, revealed that concentration estimates in urban areas benefit from combining the city-scale particle component and the long-range transport component with covariates that account for the residual spatiotemporal variation in the pollution process.
Pirani, Monica; Gulliver, John; Fuller, Gary W; Blangiardo, Marta
Comparative analysis of human tissue interactomes reveals factors leading to tissue-specific manifestation of hereditary diseases.
An open question in human genetics is what underlies the tissue-specific manifestation of hereditary diseases, which are caused by genomic aberrations that are present in cells across the human body. Here we analyzed this phenomenon for over 300 hereditary diseases by using comparative network analysis. We created an extensive resource of protein expression and interactions in 16 main human tissues, by integrating recent data of gene and protein expression across tissues with data of protein-protein interactions (PPIs). The resulting tissue interaction networks (interactomes) shared a large fraction of their proteins and PPIs, and only a small fraction of them were tissue-specific. Applying this resource to hereditary diseases, we first show that most of the disease-causing genes are widely expressed across tissues, yet, enigmatically, cause disease phenotypes in few tissues only. Upon testing for factors that could lead to tissue-specific vulnerability, we find that disease-causing genes tend to have elevated transcript levels and increased number of tissue-specific PPIs in their disease tissues compared to unaffected tissues. We demonstrate through several examples that these tissue-specific PPIs can highlight disease mechanisms, and thus, owing to their small number, provide a powerful filter for interrogating disease etiologies. As two thirds of the hereditary diseases are associated with these factors, comparative tissue analysis offers a meaningful and efficient framework for enhancing the understanding of the molecular basis of hereditary diseases. PMID:24921629
Barshir, Ruth; Shwartz, Omer; Smoly, Ilan Y; Yeger-Lotem, Esti
APC Is an RNA-Binding Protein, and Its Interactome Provides a Link to Neural Development and Microtubule Assembly.
Adenomatous polyposis coli (APC) is a microtubule plus-end scaffolding protein important in biology and disease. APC is implicated in RNA localization, although the mechanisms and functional significance remain unclear. We show APC is an RNA-binding protein and identify an RNA interactome by HITS-CLIP. Targets were highly enriched for APC-related functions, including microtubule organization, cell motility, cancer, and neurologic disease. Among the targets is ?2B-tubulin, known to be required in human neuron and axon migration. We show ?2B-tubulin is synthesized in axons and localizes preferentially to dynamic microtubules in the growth cone periphery. APC binds the ?2B-tubulin 3' UTR; experiments interfering with this interaction reduced ?2B-tubulin mRNA axonal localization and expression, depleted dynamic microtubules and the growth cone periphery, and impaired neuron migration. These results identify APC as a platform binding functionally related protein and RNA networks, and suggest a self-organizing model for the microtubule to localize synthesis of its own subunits. PMID:25036633
Preitner, Nicolas; Quan, Jie; Nowakowski, Dan W; Hancock, Melissa L; Shi, Jianhua; Tcherkezian, Joseph; Young-Pearse, Tracy L; Flanagan, John G
Differences in AMPA and kainate receptor interactomes facilitate identification of AMPA receptor auxiliary subunit GSG1L.
AMPA receptor (AMPA-R) complexes consist of channel-forming subunits, GluA1-4, and auxiliary proteins, including TARPs, CNIHs, synDIG1, and CKAMP44, which can modulate AMPA-R function in specific ways. The combinatorial effects of four GluA subunits binding to various auxiliary subunits amplify the functional diversity of AMPA-Rs. The significance and magnitude of molecular diversity, however, remain elusive. To gain insight into the molecular complexity of AMPA and kainate receptors, we compared the proteins that copurify with each receptor type in the rat brain. This interactome study identified the majority of known interacting proteins and, more importantly, provides candidates for additional studies. We validate the claudin homolog GSG1L as a newly identified binding protein�and unique modulator of AMPA-R gating, as�determined by detailed molecular, cellular, electrophysiological, and biochemical experiments. GSG1L extends the functional variety of AMPA-R complexes, and further investigation of other candidates may reveal additional complexity of ionotropic glutamate receptor function. PMID:22813734
Shanks, Natalie F; Savas, Jeffrey N; Maruo, Tomohiko; Cais, Ondrej; Hirao, Atsushi; Oe, Souichi; Ghosh, Anirvan; Noda, Yasuko; Greger, Ingo H; Yates, John R; Nakagawa, Terunaga
Differences of AMPA and kainate receptor interactomes identify a novel AMPA receptor auxiliary subunit, GSG1L
AMPA receptor (AMPA-R) complexes consist of channel forming subunits, GluA1�4 and auxiliary proteins including TARPs, CNIHs, synDIG1, and CKAMP44, which can modulate AMPA-R function in specific ways. Combinatorial effects of four GluA subunits binding to various auxiliary subunits amplify the functional diversity of AMPA-Rs. The significance and magnitude of molecular diversity, however, remain elusive. To gain insight into the molecular complexity of AMPA and kainate receptors (KA-Rs), we compared the proteins that co-purify with each receptor type in rat brain. This interactome study identified the majority of known interacting proteins and more importantly, provides novel candidates for further studies. We validate the claudin homologue GSG1L as a novel binding protein and unique modulator of AMPA-R gating, as determined by detailed molecular, cellular, electrophysiological, and biochemical experiments. GSG1L extends the functional variety of AMPA-R complexes and further investigation of other candidates may reveal additional complexity of ionotropic glutamate receptor function.
Shanks, Natalie F.; Savas, Jeffrey N.; Maruo, Tomohiko; Cais, Ondrej; Hirao, Atsushi; Oe, Souichi; Ghosh, Anirvan; Noda, Yasuko; Greger, Ingo H.; Yates, John R.; Nakagawa, Terunaga
Comparative Analysis of Human Tissue Interactomes Reveals Factors Leading to Tissue-Specific Manifestation of Hereditary Diseases
An open question in human genetics is what underlies the tissue-specific manifestation of hereditary diseases, which are caused by genomic aberrations that are present in cells across the human body. Here we analyzed this phenomenon for over 300 hereditary diseases by using comparative network analysis. We created an extensive resource of protein expression and interactions in 16 main human tissues, by integrating recent data of gene and protein expression across tissues with data of protein-protein interactions (PPIs). The resulting tissue interaction networks (interactomes) shared a large fraction of their proteins and PPIs, and only a small fraction of them were tissue-specific. Applying this resource to hereditary diseases, we first show that most of the disease-causing genes are widely expressed across tissues, yet, enigmatically, cause disease phenotypes in few tissues only. Upon testing for factors that could lead to tissue-specific vulnerability, we find that disease-causing genes tend to have elevated transcript levels and increased number of tissue-specific PPIs in their disease tissues compared to unaffected tissues. We demonstrate through several examples that these tissue-specific PPIs can highlight disease mechanisms, and thus, owing to their small number, provide a powerful filter for interrogating disease etiologies. As two thirds of the hereditary diseases are associated with these factors, comparative tissue analysis offers a meaningful and efficient framework for enhancing the understanding of the molecular basis of hereditary diseases.
Barshir, Ruth; Shwartz, Omer; Smoly, Ilan Y.; Yeger-Lotem, Esti
Spatio-temporal growth of disturbances in a boundary layer and energy based receptivity analysis
NASA Astrophysics Data System (ADS)
In fluid dynamical systems, it is not known a priori whether disturbances grow either in space or in time or as spatio-temporal structures. However, for boundary layers, it is customary to treat it as a spatial problem and some limited comparison between prediction and laboratory experiments exist. In the present work, the receptivity problem of a zero pressure gradient boundary layer excited by a localized harmonic source is investigated under the general spatio-temporal framework, using the Bromwich contour integral method. While this approach has been shown to be equivalent to the spatial study, for unstable systems excited by a single frequency source [T. K. Sengupta, M. Ballav, and S. Nijhawan, Phys. Fluids 6, 1213 (1994)], here we additionally show, how the boundary layer behaves when it is excited (i) at a single frequency that corresponds to a stable condition (given by spatial normal-mode analysis) and (ii) by wideband frequencies, that shows the possibility of flow transition due to a spatio-temporally growing forerunner or wave front. An energy based receptivity analysis tool is also developed as an alternative to traditional instability theory. Using this, we reinterpret the concept of critical layer that was originally postulated to explain the mathematical singularity of inviscid disturbance field in traditional instability theory of normal modes.
Sengupta, T. K.; Rao, A. Kameswara; Venkatasubbaiah, K.
Spatiotemporal multifractal characteristics of electromagnetic radiation in response to deep coal rock bursts
NASA Astrophysics Data System (ADS)
Dynamic collapses of deeply mined coal rocks are severe threats to miners, in order to predict the collapses more accurately using electromagnetic radiation (EMR), we investigate the spatiotemporal multifractal characteristics and formation mechanism of EMR induced by underground coal mining. Coal rock in the burst-prone zone often exchanges materials and energy with its environment and gradually transits from its original stable equilibrium structure to a non-equilibrium dissipative structure with implicit spatiotemporal complexity or multifractal structures, resulting in temporal variation in multifractal EMR. The inherent law of EMR time series during damage evolution was analyzed by using time-varying multifractal theory. Results show that the time-varying multifractal characteristics of EMR are determined by damage evolutions process, the dissipated energy caused by damage evolutions such as crack propagation, fractal sliding and shearing can be regarded as the fingerprint of various EMR micro-mechanics. Dynamic spatiotemporal multifractal spectrum of EMR considers both spatial (multiple fractures) and temporal (dynamic evolution) characteristics of coal rocks, and records the dynamic evolution processes of rock bursts. Thus, it can be used to evaluate the coal deformation and fracture process. The study is of significance for us to in-depth understand EMR mechanism and to increase the accuracy of applying the EMR method to forecast dynamic disasters.
Shaobin, H.; Enyuan, W.; Xiaofei, L.
Two bilateral sources of the late AEP as identified by a spatio-temporal dipole model.
A new spatio-temporal dipole model is presented, which enables prediction and analysis of scalp potential wave forms due to spatio-temporal overlap of multiple generators. Each generator is thought to represent a local neural subset, the electric activity of which can be modelled by an equivalent dipole with stationary location and orientation closely related to the spatial organization of the neural subset. The temporal course of dipole magnitude is assumed to depict the external far field due to the compound discharge processes of the generator. Simulations of uni- and bilateral dipoles within the temporal lobe, oriented vertically and horizontally, demonstrate how spatio-temporal overlap mag bring about the 'vertex response' of the late AEP and the wave form changes observed over temporal sites. Analyses of late AEPs reported for a coronal chain of electrodes by Peronnet et al. (1974) and Vaughan et al. (1980) revealed that the wave forms in the 60-250 msec range could be perfectly matched at all electrodes by model wave forms due to 2 bilateral sources within the temporal lobe. Their locations, orientations and their latency difference of about 30 msec suggest consistently that the sequential activation of primary and secondary auditory cortices is the predominant source to the late AEPs. PMID:2578376
Scherg, M; Von Cramon, D
Improved decoding of neural activity from fMRI signals using non-separable spatiotemporal deconvolutions.
The goal of most functional Magnetic Resonance Imaging (fMRI) analyses is to investigate neural activity. Many fMRI analysis methods assume that the temporal dynamics of the hemodynamic response function (HRF) to neural activation is separable from its spatial dynamics. Although there is empirical evidence that the HRF is more complex than suggested by space-time separable canonical HRF models, it is difficult to assess how much information about neural activity is lost when assuming space-time separability. In this study we directly test whether spatiotemporal variability in the HRF that is not captured by separable models contains information about neural signals. We predict intracranially measured neural activity from simultaneously recorded fMRI data using separable and non-separable spatiotemporal deconvolutions of voxel time series around the recording electrode. Our results show that abandoning the spatiotemporal separability assumption consistently improves the decoding accuracy of neural signals from fMRI data. We compare our findings with results from optical imaging and fMRI studies and discuss potential implications for classical fMRI analyses without invasive electrophysiological recordings. PMID:22537598
Biessmann, Felix; Murayama, Yusuke; Logothetis, Nikos K; M�ller, Klaus-Robert; Meinecke, Frank C
POINeT: protein interactome with sub-network analysis and hub prioritization
Background Protein-protein interactions (PPIs) are critical to every aspect of biological processes. Expansion of all PPIs from a set of given queries often results in a complex PPI network lacking spatiotemporal consideration. Moreover, the reliability of available PPI resources, which consist of low- and high-throughput data, for network construction remains a significant challenge. Even though a number of software tools are available to facilitate PPI network analysis, an integrated tool is crucial to alleviate the burden on querying across multiple web servers and software tools. Results We have constructed an integrated web service, POINeT, to simplify the process of PPI searching, analysis, and visualization. POINeT merges PPI and tissue-specific expression data from multiple resources. The tissue-specific PPIs and the numbers of research papers supporting the PPIs can be filtered with user-adjustable threshold values and are dynamically updated in the viewer. The network constructed in POINeT can be readily analyzed with, for example, the built-in centrality calculation module and an integrated network viewer. Nodes in global networks can also be ranked and filtered using various network analysis formulas, i.e., centralities. To prioritize the sub-network, we developed a ranking filtered method (S3) to uncover potential novel mediators in the midbody network. Several examples are provided to illustrate the functionality of POINeT. The network constructed from four schizophrenia risk markers suggests that EXOC4 might be a novel marker for this disease. Finally, a liver-specific PPI network has been filtered with adult and fetal liver expression profiles. Conclusion The functionalities provided by POINeT are highly improved compared to previous version of POINT. POINeT enables the identification and ranking of potential novel genes involved in a sub-network. Combining with tissue-specific gene expression profiles, PPIs specific to selected tissues can be revealed. The straightforward interface of POINeT makes PPI search and analysis just a few clicks away. The modular design permits further functional enhancement without hampering the simplicity. POINeT is available at .
Lee, Sheng-An; Chan, Chen-Hsiung; Chen, Tzu-Chi; Yang, Chia-Ying; Huang, Kuo-Chuan; Tsai, Chi-Hung; Lai, Jin-Mei; Wang, Feng-Sheng; Kao, Cheng-Yan; Huang, Chi-Ying F
Spatiotemporal Stochastic Resonance:Theory and Experiment
NASA Astrophysics Data System (ADS)
The amplification of weak periodic signals in bistable or excitable systems via stochastic resonance has been studied intensively over the last years. We are going one step further and ask: Can noise enhance spatiotemporal patterns in excitable media and can this effect be observed in nature? To this end, we are looking at large, two dimensional arrays of coupled excitable elements. Due to the coupling, excitation can propagate through the array in form of nonlinear waves. We observe target waves, rotating spiral waves and other wave forms. If the coupling between the elements is below a critical threshold, any excitational pattern will die out in the absence of noise. Below this threshold, large scale rotating spiral waves - as they are observed above threshold - can be maintained by a proper level of the noise[1]. Furthermore, their geometric features, such as the curvature can be controlled by the homogeneous noise level[2]. If the noise level is too large, break up of spiral waves and collisions with spontaneously nucleated waves yields spiral turbulence. Driving our array with a spatiotemporal pattern, e.g. a rotating spiral wave, we show that for weak coupling the excitational response of the array shows stochastic resonance - an effect we have termed spatiotemporal stochastic resonance. In the last part of the talk I'll make contact with calcium waves, observed in astrocyte cultures and hippocampus slices[3]. A. Cornell-Bell and collaborators[3] have pointed out the role of calcium waves for long-range glial signaling. We demonstrate the similarity of calcium waves with nonlinear waves in noisy excitable media. The noise level in the tissue is characterized by spontaneous activity and can be controlled by applying neuro-transmitter substances[3]. Noise effects in our model are compared with the effect of neuro-transmitters on calcium waves. [1]P. Jung and G. Mayer-Kress, CHAOS 5, 458 (1995). [2]P. Jung and G. Mayer-Kress, Phys. Rev. Lett.62, 2682 (1995). [3] A. Cornell-Bell, Steven M. Finkbeiner, Mark.S. Cooper and Stephen J. Smith, SCIENCE, 247, 373 (1990).
Peter, Jung
Assessment of groundwater utilization for irrigating park trees under the spatiotemporal uncertainty condition of water quality
NASA Astrophysics Data System (ADS)
Parks have a variety of functions for residents and are important for urban landscape planning. The healthy growth of urban park trees requires regular irrigation. To reduce the pressure of high groundwater levels and to avoid wasting groundwater resources, proper groundwater extraction for irrigating park trees in the Taipei Basin is regarded as a reciprocal solution of sustainable groundwater management and preserving excellent urban landscapes. Therefore, this study determines pristine groundwater use for irrigating park trees in the metropolitan Taipei Basin under the spatiotemporal uncertainty condition of water quality. First, six hydrochemical parameters in groundwater associated with an irrigation water quality standard were collected from a 12-year survey. Upper, median and lower quartiles of the six hydrochemical parameters were obtained to establish three thresholds. According to the irrigation water quality standard, multivariate indicator kriging (MVIK) was adopted to probabilistically evaluate the integration of the six hydrochemical parameters. Entropy was then applied to quantify the spatiotemporal uncertainty of the hydrochemical parameters. Finally, locations, which have high estimated probabilities for the median-quartile threshold and low local uncertainty, are suitable for pumping groundwater for irrigating park trees. The study results demonstrate that MVIK and entropy are capable of characterizing the spatiotemporal uncertainty of groundwater quality parameters and determining suitable parks of groundwater utilization for irrigation. Moreover, the upper, median and lower quartiles of hydrochemical parameters are served as three estimated thresholds in MVIK, which is robust to assessment predictions. Therefore, this study significantly improves the methodological application and limitation of MVIK for spatiotemporally analyzing environmental quality compared with the previous related works. Furthermore, the analyzed results indicate that 64 parks in this basin are recommended to use groundwater for irrigating trees. Most of the recommended parks are located in the central and northern foothill regions. The result findings can help government administrators establish a reciprocal plan of sustainable groundwater utilization and preserving excellent park landscapes in the Taipei Basin. Keywords: Park; Irrigation; Groundwater; Uncertainty; Multivariate indicator kriging (MVIK); Entropy
Jang, Cheng-Shin; Kuo, Yi-Ming
Characterizing configurations of fire ignition points through spatiotemporal point processes
NASA Astrophysics Data System (ADS)
Human-caused forest fires are usually regarded as unpredictable but often exhibit trends towards clustering in certain locations and periods. Characterizing such configurations is crucial for understanding spatiotemporal fire dynamics and implementing preventive actions. Our objectives were to analyse the spatiotemporal point configuration and to test for spatiotemporal interaction. We characterized the spatiotemporal structure of 984 fire ignition points in a study area of Galicia, Spain, during 2007-2011 by the K-Ripley's function. Our results suggest the presence of spatiotemporal structures for time lags of less than two years and ignition point distances in the range 0-12 km. Ignition centre points at time lags of less than 100 days are aggregated for any inter-event distance. This cluster structure loses strength as the time lag increases, and at time lags of more than 365 days this cluster structure is not significant for any lag distance. Our results also suggest spatiotemporal interdependencies at time lags of less than 100 days and inter-event distances of less than 10 km. At time lags of up to 365 days spatiotemporal components are independent for any point distance. These results suggest that risk conditions occur locally and are short-lived in this study area.
Comas, C.; Costafreda-Aumedes, S.; Vega-Garcia, C.
Chimera states as chaotic spatiotemporal patterns.
Chimera states are a recently new discovered dynamical phenomenon that appears in arrays of nonlocally coupled oscillators and displays a spatial pattern of coherent and incoherent regions. We report here an additional feature of this dynamical regime: an irregular motion of the position of the coherent and incoherent regions, i.e., we reveal the nature of the chimera as a spatiotemporal pattern with a regular macroscopic pattern in space, and an irregular motion in time. This motion is a finite-size effect that is not observed in the thermodynamic limit. We show that on a large time scale, it can be described as a Brownian motion. We provide a detailed study of its dependence on the number of oscillators N and the parameters of the system. PMID:20866466
Omel'chenko, Oleh E; Wolfrum, Matthias; Maistrenko, Yuri L
Emergence of spatiotemporal dislocation chains in drifting patterns.
One-dimensional patterns subjected to counter-propagative flows or speed jumps exhibit a rich and complex spatiotemporal dynamics, which is characterized by the perpetual emergence of spatiotemporal dislocation chains. Using a universal amplitude equation of drifting patterns, we show that this behavior is a result of a combination of a phase instability and an advection process caused by an inhomogeneous drift force. The emergence of spatiotemporal dislocation chains is verified in numerical simulations on an optical feedback system with a non-uniform intensity pump. Experimentally this phenomenon is also observed in a tilted quasi-one-dimensional fluidized shallow granular bed mechanically driven by a harmonic vertical vibration. PMID:24985447
Clerc, M G; Falc�n, C; Garc�a-�ustes, M A; Odent, V; Ortega, I
Imaging cross-correlation FROG: measuring ultrashort, complex, spatiotemporal fields.
We present imaging cross-correlation frequency-resolved optical gating (ImXFROG), a new method for the spatiotemporal phase retrieval of ultrashort pulses. It is demonstrated that ImXFROG can measure phase and intensity of arbitrary, spatiotemporally distorted pulses with femtosecond resolution and up to 10(7) independent variables. ImXFROG is implemented as a plug-in upgrade to an existing correlator and used to demonstrate the reconstruction of highly complex, optical pulses with femtosecond features and massive spatiotemporal distortion. PMID:24216822
Eilenberger, Falk; Brown, Alexander; Minardi, Stefano; Pertsch, Thomas
Emergence of spatiotemporal dislocation chains in drifting patterns
NASA Astrophysics Data System (ADS)
One-dimensional patterns subjected to counter-propagative flows or speed jumps exhibit a rich and complex spatiotemporal dynamics, which is characterized by the perpetual emergence of spatiotemporal dislocation chains. Using a universal amplitude equation of drifting patterns, we show that this behavior is a result of a combination of a phase instability and an advection process caused by an inhomogeneous drift force. The emergence of spatiotemporal dislocation chains is verified in numerical simulations on an optical feedback system with a non-uniform intensity pump. Experimentally this phenomenon is also observed in a tilted quasi-one-dimensional fluidized shallow granular bed mechanically driven by a harmonic vertical vibration.
Clerc, M. G.; Falc�n, C.; Garc�a-�ustes, M. A.; Odent, V.; Ortega, I.
Freezing in Parkinson's disease: a spatiotemporal motor disorder beyond gait.
Freezing of gait (FOG) is an incapacitating problem in Parkinson's disease that is difficult to manage therapeutically. We tested the hypothesis that impaired rhythm and amplitude control is a common mechanism of freezing which is also present during other rhythmic tasks. Therefore, we compared the occurrence and spatiotemporal profiles of freezing episodes during upper limb motion, lower limb motion, and FOG. Eleven freezers, 12 non-freezers, and 11 controls performed a rhythmic bilateral finger movement task. The triggering effect of movement speed, amplitude, and coordination pattern was evaluated. Regression slopes and spectral analysis addressed the spatial and temporal kinematic changes inherent to freezing episodes. The FOG Questionnaire score significantly predicted severity of upper limb freezing, present in 9 freezers, and of foot freezing, present in 8 freezers. Similar to gait, small-amplitude movements tended to trigger upper limb freezing, which was preceded by hastened movement and a strong amplitude breakdown. Upper limb freezing power spectra were broadband, including increased energy in the "freeze band" (3-8 Hz). Contrary to FOG, unilateral upper limb freezing was common and occurred mainly on the disease-dominant side. The findings emphasize that a core motor problem underlies freezing which can affect various movement effectors. This deficit may originate on the disease-dominant body side and interfere with amplitude and timing regulation during repetitive limb movements. These results may shift current thinking on the origins of freezing as being not exclusively a gait failure. PMID:22020744
Vercruysse, Sarah; Spildooren, Joke; Heremans, Elke; Vandenbossche, Jochen; Levin, Oron; Wenderoth, Nicole; Swinnen, Stephan P; Janssens, Luc; Vandenberghe, Wim; Nieuwboer, Alice
Systemic risk and spatiotemporal dynamics of the US housing market
Housing markets play a crucial role in economies and the collapse of a real-estate bubble usually destabilizes the financial system and causes economic recessions. We investigate the systemic risk and spatiotemporal dynamics of the US housing market (1975�2011) at the state level based on the Random Matrix Theory (RMT). We identify richer economic information in the largest eigenvalues deviating from RMT predictions for the housing market than for stock markets and find that the component signs of the eigenvectors contain either geographical information or the extent of differences in house price growth rates or both. By looking at the evolution of different quantities such as eigenvalues and eigenvectors, we find that the US housing market experienced six different regimes, which is consistent with the evolution of state clusters identified by the box clustering algorithm and the consensus clustering algorithm on the partial correlation matrices. We find that dramatic increases in the systemic risk are usually accompanied by regime shifts, which provide a means of early detection of housing bubbles.
Meng, Hao; Xie, Wen-Jie; Jiang, Zhi-Qiang; Podobnik, Boris; Zhou, Wei-Xing; Stanley, H. Eugene
Initial spatio-temporal domain expansion of the Modelfest database
NASA Astrophysics Data System (ADS)
The first Modelfest group publication appeared in the SPIE Human Vision and Electronic Imaging conference proceedings in 1999. "One of the group's goals is to develop a public database of test images with threshold data from multiple laboratories for designing and testing HVS (Human Vision Models)." After extended discussions the group selected a set of 45 static images thought to best meet that goal and collected psychophysical detection data which is available on the WEB and presented in the 2000 SPIE conference proceedings. Several groups have used these datasets to test spatial modeling ideas. Further discussions led to the preliminary stimulus specification for extending the database into the temporal domain which was published in the 2002 conference proceeding. After a hiatus of 12 years, some of us have collected spatio-temporal thresholds on an expanded stimulus set of 41 video clips; the original specification included 35 clips. The principal change involved adding one additional spatial pattern beyond the three originally specified. The stimuli consisted of 4 spatial patterns, Gaussian Blob, 4 c/d Gabor patch, 11.3 c/d Gabor patch and a 2D white noise patch. Across conditions the patterns were temporally modulated over a range of approximately 0-25 Hz as well as temporal edge and pulse modulation conditions. The display and data collection specifications were as specified by the Modelfest groups in the 2002 conference proceedings. To date seven subjects have participated in this phase of the data collection effort, one of which also participated in the first phase of Modelfest. Three of the spatio-temporal stimuli were identical to conditions in the original static dataset. Small differences in the thresholds were evident and may point to a stimulus limitation. The temporal CSF peaked between 4 and 8 Hz for the 0 c/d (Gaussian blob) and 4 c/d patterns. The 4 c/d and 11.3 c/d Gabor temporal CSF was low pass while the 0 c/d pattern was band pass. This preliminary expansion of the Modelfest dataset needs the participation of additional laboratories to evaluate the impact of different methods on threshold estimates and increase the subject base. We eagerly await the addition of new data from interested researchers. It remains to be seen how accurately general HVS models will predict thresholds across both Modelfest datasets.
Carney, Thom; Mozaffari, Sahar; Sun, Sean; Johnson, Ryan; Shirvastava, Sharona; Shen, Priscilla; Ly, Emma
Assessing the impact of a movement network on the spatiotemporal spread of infectious diseases.
Linking information on a movement network with space-time data on disease incidence is one of the key challenges in infectious disease epidemiology. In this article, we propose and compare two statistical frameworks for this purpose, namely, parameter-driven (PD) and observation-driven (OD) models. Bayesian inference in PD models is done using integrated nested Laplace approximations, while OD models can be easily fitted with existing software using maximum likelihood. The predictive performance of both formulations is assessed using proper scoring rules. As a case study, the impact of cattle trade on the spatiotemporal spread of Coxiellosis in Swiss cows, 2004-2009, is finally investigated. PMID:22171626
Schr�dle, Birgit; Held, Leonhard; Rue, H�vard
Spatio-temporal measurements of Trichel corona discharge using capacitive probe diagnostic
NASA Astrophysics Data System (ADS)
A nonintrusive capacitive probe diagnostic is developed to estimate the spatio-temporal charge density variation of corona discharge. Tikhonov regularization is used to calculate the charge density from measured potential. A good time resolution and restricted space resolution in charge density is achieved. The axial electric field due to space charge is also estimated by considering the discharge to be of finite radius and with uniformly distributed charge density along the radial direction. Space charge wave front movement, as predicted by existing theories, is noticed. Constraints of present technique and scope for further improvements are discussed.
Gupta, Deepak K.; Ramachandran, H.; John, P. I.
Linking Proteomic and Transcriptional Data through the Interactome and Epigenome Reveals a Map of Oncogene-induced Signaling
Cellular signal transduction generally involves cascades of post-translational protein modifications that rapidly catalyze changes in protein-DNA interactions and gene expression. High-throughput measurements are improving our ability to study each of these stages individually, but do not capture the connections between them. Here we present an approach for building a network of physical links among these data that can be used to prioritize targets for pharmacological intervention. Our method recovers the critical missing links between proteomic and transcriptional data by relating changes in chromatin accessibility to changes in expression and then uses these links to connect proteomic and transcriptome data. We applied our approach to integrate epigenomic, phosphoproteomic and transcriptome changes induced by the variant III mutation of the epidermal growth factor receptor (EGFRvIII) in a cell line model of glioblastoma multiforme (GBM). To test the relevance of the network, we used small molecules to target highly connected nodes implicated by the network model that were not detected by the experimental data in isolation and we found that a large fraction of these agents alter cell viability. Among these are two compounds, ICG-001, targeting CREB binding protein (CREBBP), and PKF118�310, targeting ?-catenin (CTNNB1), which have not been tested previously for effectiveness against GBM. At the level of transcriptional regulation, we used chromatin immunoprecipitation sequencing (ChIP-Seq) to experimentally determine the genome-wide binding locations of p300, a transcriptional co-regulator highly connected in the network. Analysis of p300 target genes suggested its role in tumorigenesis. We propose that this general method, in which experimental measurements are used as constraints for building regulatory networks from the interactome while taking into account noise and missing data, should be applicable to a wide range of high-throughput datasets.
Huang, Shao-shan Carol; Clarke, David C.; Gosline, Sara J. C.; Labadorf, Adam; Chouinard, Candace R.; Gordon, William; Lauffenburger, Douglas A.; Fraenkel, Ernest
Novel Applications of Stochastic Resonance and Spatiotemporal Chaos Control.
National Technical Information Service (NTIS)
This research project studied the spatiotemporal and stochastic coupled arrays of nonlinear elements using an dynamics of systems comprised of experimental method employing analog VLSI hardware. This is part of an ongoing initiative in our nonlinear dynam...
K. Wiesenfeld
Creating an effective environment for collaborative spatio-temporal model development will require computational systems that provide support for the user in three key areas: (1) Support for modular, hierarchical model construction and archiving/linking of simulation modules; (2)...
Pragmatic estimation of a spatio-temporal air quality model with irregular monitoring data
NASA Astrophysics Data System (ADS)
Statistical analyses of health effects of air pollution have increasingly used GIS-based covariates for prediction of ambient air quality in "land use" regression models. More recently these spatial regression models have accounted for spatial correlation structure in combining monitoring data with land use covariates. We present a flexible spatio-temporal modeling framework and pragmatic, multi-step estimation procedure that accommodates essentially arbitrary patterns of missing data with respect to an ideally complete space by time matrix of observations on a network of monitoring sites. The methodology incorporates a model for smooth temporal trends with coefficients varying in space according to Partial Least Squares regressions on a large set of geographic covariates and nonstationary modeling of spatio-temporal residuals from these regressions. This work was developed to provide spatial point predictions of PM 2.5 concentrations for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) using irregular monitoring data derived from the AQS regulatory monitoring network and supplemental short-time scale monitoring campaigns conducted to better predict intra-urban variation in air quality. We demonstrate the interpretation and accuracy of this methodology in modeling data from 2000 through 2006 in six U.S. metropolitan areas and establish a basis for likelihood-based estimation.
Sampson, Paul D.; Szpiro, Adam A.; Sheppard, Lianne; Lindstr�m, Johan; Kaufman, Joel D.
Spatiotemporal binary interaction and designer quasi-particle condensates
NASA Astrophysics Data System (ADS)
We introduce a new integrable model to investigate the dynamics of two component quasi-particle condensates with spatiotemporal interaction strengths. We derive the associated Lax pair of the coupled Gross�Pitaevskii (GP) equation and construct matter wave solitons. We show that the spatiotemporal binary interaction strengths not only facilitate the stabilization of the condensates, but also enables one to fabricate condensates with desirable densities, geometries, and properties, leading to the so-called �designer quasi-particle condensates�.
Ramaswamy, Radha; Pattu Sakthi, Vinayagam; Hyun Jong, Shin; Kuppuswamy, Porsezian
In this paper, we discuss geovisualization techniques to explore spatio-temporal patterns formed by people traveling through public transport system (PTS). The Spatio-temporal reasoning by PTS operators\\/policy makers to extract patterns from public transport system data is studied. The resulting questions are related to basic visual tasks like locate, identify, associate and compare. These visual tasks are incorporated in a set
Menno-Jan Kraak
Energy model for contrast detection: spatiotemporal characteristics of threshold vision
.�� A model for contrast detection of spatiotemporal stimuli is proposed which consists of a spatiotemporal linear filter, an\\u000a energy device and a threshold device. Assuming the existence of independent intrinsic noise, the probability of stimulus detection\\u000a was approximated by a Weibull function of the response energy. With this assumption, the stimulus energy is a constant at\\u000a fixed detection probability.
V. Manahilov; W. Simpson
Different routes from a matter wavepacket to spatiotemporal chaos.
We investigate the dynamics of a quasi-one-dimensional Bose-Einstein condensate confined in a double-well potential with spatiotemporally modulated interaction. A variety of phenomena is identified in different frequency regimes, including the self-compression, splitting, breathing-like, and near-fidelity of the matter wavepacket, which are associated with different routes for the onset of spatiotemporal chaos. The results also reveal that chaos can retain space-inversion symmetry of the system. PMID:23020448
Rong, Shiguang; Hai, Wenhua; Xie, Qiongtao; Zhong, Honghua
Spatiotemporal information systems in soil and environmental sciences
This work is concerned with spatiotemporal information systems and their application in soil and environmental sciences. Issues investigated in this work include developments in the space\\/time modelling of natural variations, composite spatiotemporal mapping, and the incorporation of various sources of information into space\\/time analysis. Theoretical models, simulation examples, as well as real-world case studies are discussed. The models can process
George Christakos
Discovering RNA-Protein Interactome by Using Chemical Context Profiling of the RNA-Protein Interface
SUMMARY RNA-protein (RNP) interactions generally are required for RNA function. At least 5% of human genes code for RNA-binding proteins. Whereas many approaches can identify the RNA partners for a specific protein, finding the protein partners for a specific RNA is difficult. We present a machine-learning method that scores a protein�s binding potential for an RNA structure by utilizing the chemical context profiles of the interface from known RNP structures. Our approach is applicable even when only a single RNP structure is available. We examined 801 mammalian proteins and find that 37 (4.6%) potentially bind transfer RNA (tRNA). Most are enzymes involved in cellular processes unrelated to translation and were not known to interact with RNA. We experimentally tested six positive and three negative predictions for tRNA binding in vivo, and all nine predictions were correct. Our computational approach provides a powerful complement to experiments in discovering new RNPs.
Parisien, Marc; Wang, Xiaoyun; Perdrizet, George; Lamphear, Corissa; Fierke, Carol A.; Maheshwari, Ketan C.; Wilde, Michael J.; Sosnick, Tobin R.; Pan, Tao
A comprehensive protein-protein interactome for yeast PAS kinase 1 reveals direct inhibition of respiration through the phosphorylation of Cbf1.
Per-Arnt-Sim (PAS) kinase is a sensory protein kinase required for glucose homeostasis in yeast, mice, and humans, yet little is known about the molecular mechanisms of its function. Using both yeast two-hybrid and copurification approaches, we identified the protein-protein interactome for yeast PAS kinase 1 (Psk1), revealing 93 novel putative protein binding partners. Several of the Psk1 binding partners expand the role of PAS kinase in glucose homeostasis, including new pathways involved in mitochondrial metabolism. In addition, the interactome suggests novel roles for PAS kinase in cell growth (gene/protein expression, replication/cell division, and protein modification and degradation), vacuole function, and stress tolerance. In vitro kinase studies using a subset of 25 of these binding partners identified Mot3, Zds1, Utr1, and Cbf1 as substrates. Further evidence is provided for the in vivo phosphorylation of Cbf1 at T211/T212 and for the subsequent inhibition of respiration. This respiratory role of PAS kinase is consistent with the reported hypermetabolism of PAS kinase-deficient mice, identifying a possible molecular mechanism and solidifying the evolutionary importance of PAS kinase in the regulation of glucose homeostasis. PMID:24850888
DeMille, Desiree; Bikman, Benjamin T; Mathis, Andrew D; Prince, John T; Mackay, Jordan T; Sowa, Steven W; Hall, Tacie D; Grose, Julianne H
Global identification of O-GlcNAc transferase (OGT) interactors by a human proteome microarray and the construction of an OGT interactome.
O-Linked ?-N-acetylglucosamine (O-GlcNAcylation) is an important protein PTM, which is very abundant in mammalian cells. O-GlcNAcylation is catalyzed by O-GlcNAc transferase (OGT), whose substrate specificity is believed to be regulated through interactions with other proteins. There are a handful of known human OGT interactors, which is far from enough for fully elucidating the substrate specificity of OGT. To address this challenge, we used a human proteome microarray containing ~17,000 affinity-purified human proteins to globally identify OGT interactors and identified 25 OGT-binding proteins. Bioinformatics analysis showed that these interacting proteins play a variety of roles in a wide range of cellular functions and are highly enriched in intra-Golgi vesicle-mediated transport and vitamin biosynthetic processes. Combining newly identified OGT interactors with the interactors identified prior to this study, we have constructed the first OGT interactome. Bioinformatics analysis suggests that the OGT interactome plays important roles in protein transportation/localization and transcriptional regulation. The novel OGT interactors that we identified in this study could serve as a starting point for further functional analysis. Because of its high-throughput and parallel analysis capability, we strongly believe that protein microarrays could be easily applied for the global identification of regulators for other key enzymes. PMID:24536041
Deng, Rui-Ping; He, Xiang; Guo, Shu-Juan; Liu, Wei-Feng; Tao, Yong; Tao, Sheng-Ce
Interactome Analyses of Mature ?-Secretase Complexes Reveal Distinct Molecular Environments of Presenilin (PS) Paralogs and Preferential Binding of Signal Peptide Peptidase to PS2*
?-Secretase plays a pivotal role in the production of neurotoxic amyloid ?-peptides (A?) in Alzheimer disease (AD) and consists of a heterotetrameric core complex that includes the aspartyl intramembrane protease presenilin (PS). The human genome codes for two presenilin paralogs. To understand the causes for distinct phenotypes of PS paralog-deficient mice and elucidate whether PS mutations associated with early-onset AD affect the molecular environment of mature ?-secretase complexes, quantitative interactome comparisons were undertaken. Brains of mice engineered to express wild-type or mutant PS1, or HEK293 cells stably expressing PS paralogs with N-terminal tandem-affinity purification tags served as biological source materials. The analyses revealed novel interactions of the ?-secretase core complex with a molecular machinery that targets and fuses synaptic vesicles to cellular membranes and with the H+-transporting lysosomal ATPase macrocomplex but uncovered no differences in the interactomes of wild-type and mutant PS1. The catenin/cadherin network was almost exclusively found associated with PS1. Another intramembrane protease, signal peptide peptidase, predominantly co-purified with PS2-containing ?-secretase complexes and was observed to influence A? production.
Jeon, Amy Hye Won; Bohm, Christopher; Chen, Fusheng; Huo, Hairu; Ruan, Xueying; Ren, Carl He; Ho, Keith; Qamar, Seema; Mathews, Paul M.; Fraser, Paul E.; Mount, Howard T. J.; St George-Hyslop, Peter; Schmitt-Ulms, Gerold
Spatio-temporal availability of soft mast in clearcuts in the Southern Appalachians
Soft mast is an important resource for many wild populations in the Southern Appalachians, yet the way clear-cutting affects availability of soft mast though time is not fully understood. We tested a theoretical model of temporal availability of soft mast in clearcuts using empirical data on percent cover and berry production of Gaylussacia, Vaccinium, and Rubus spp. plants in 100 stands that were clearcut (0-122 years old) in the Southern Appalachian Mountains. We modeled the relationship between soft mast availability and stand age, evaluated the effects of topography and forest type on soft mast, developed statistical models for predicting the spatio-temporal distribution of soft mast, and tested the hypothesis that percent cover of berry plants and berry production provided similar information about soft mast availability. We found temporal dynamics explained berry production better than it predicted percent plant cover, whereas topographic variables influenced percent plant cover more than they influenced berry production. Berry production and percent plant cover were highest in ???2-9-year-old stands. Percent plant cover was lowest in 10-69-year-old stands and intermediate in 70+-year-old stands. Three of our spatio-temporal models performed well during model testing and they were not biased by the training data, indicating the inferences about spatio-temporal availability of soft mast extended beyond our sample data. The methods we used to estimate the distribution of soft mast may be useful for modeling distributions of other resources. ?? 2006 Elsevier B.V. All rights reserved.
Reynolds-Hogland, M. J.; Mitchell, M. S.; Powell, R. A.
Spatiotemporal wavelet resampling for functional neuroimaging data.
The study of dynamic interdependences between brain regions is currently a very active research field. For any connectivity study, it is important to determine whether correlations between two selected brain regions are statistically significant or only chance effects due to non-specific correlations present throughout the data. In this report, we present a wavelet-based non-parametric technique for testing the null hypothesis that the correlations are typical of the data set and not unique to the regions of interest. This is achieved through spatiotemporal resampling of the data in the wavelet domain. Two functional MRI data sets were analysed: (1) Data from 8 healthy human subjects viewing a checkerboard image, and (2) "Null" data obtained from 3 healthy human subjects, resting with eyes closed. It was demonstrated that constrained resampling of the data in the wavelet domain allows construction of bootstrapped data with four essential properties: (1) Spatial and temporal correlations within and between slices are preserved, (2) The irregular geometry of the intracranial images is maintained, (3) There is adequate type I error control, and (4) Expected experiment-induced correlations are identified. The limitations and possible extensions of the proposed technique are discussed. PMID:15281138
Breakspear, Michael; Brammer, Michael J; Bullmore, Ed T; Das, Pritha; Williams, Leanne M
Spatiotemporal pattern of bacterioplankton in Donghu Lake
NASA Astrophysics Data System (ADS)
Bacterioplankton play key roles in the biogeochemical cycle and in organic contaminant degradation. The species richness and abundance of bacterial subgroups are generally distinct from each other, and this is attributed to their different functions in aquatic ecosystems. The spatiotemporal variations of eight phylogenetic subgroups (Actinobacteria, Bacteroidetes, Cyanobacteria, Firmicutes, Planctomycetes, alpha-, beta-, and gamma-Proteobacteria) derived from Donghu Lake were investigated using PCR-DGGE fingerprinting, to explore their responses to environmental factors. Results indicate that Actinobacteria and beta-Proteobacteria were the two largest bacterial subgroups detected. These two groups and Bacteroidetes showed clear seasonal patterns in composition of the operational taxonomic unit. Results also suggest that the bacterioplankton subgroups in Donghu Lake were significantly correlated with different environmental factors. In brief, the total nitrogen was one of the major factors regulating all the bacterioplankton except for Actinobacteria. However, total phosphorus, another important eutrophication factor, contributed to the two largest bacterial groups (Actinobacteria and beta-Proteobacteria), as well as to the Cyanobacteria and Firmicutes. Therefore, the responses of bacterioplankton subgroups to environmental factors were different, and this should be attributed to the differences in the functions of different groups.
Zhang, Xiang; Yan, Qingyun; Yu, Yuhe; Dai, Lili
Volumetric Correlates of Spatiotemporal Working and Recognition Memory Impairment in Aged Rhesus Monkeys
Spatiotemporal and recognition memory are affected by aging in humans and macaque monkeys. To investigate whether these deficits are coupled with atrophy of memory-related brain regions, T1-weighted magnetic resonance images were acquired and volumes of the cerebrum, ventricles, prefrontal cortex (PFC), calcarine cortex, hippocampus, and striatum were quantified in young and aged rhesus monkeys. Subjects were tested on a spatiotemporal memory procedure (delayed response [DR]) that requires the integrity of the PFC and a medial temporal lobe-dependent recognition memory task (delayed nonmatching to sample [DNMS]). Region of interest analyses revealed that age inversely correlated with striatal, dorsolateral prefrontal cortex (dlPFC), and anterior cingulate cortex volumes. Hippocampal volume predicted acquisition of the DR task. Striatal volume correlated with DNMS acquisition, whereas total prefrontal gray matter, prefrontal white matter, and dlPFC volumes each predicted DNMS accuracy. A regional covariance analysis revealed that age-related volumetric changes could be captured in a distributed network that was coupled with declining performance across delays on the DNMS task. This volumetric analysis adds to growing evidence that cognitive aging in primates arises from region-specific morphometric alterations distributed across multiple memory-related brain systems, including subdivisions of the PFC.
Shamy, Jul Lea; Habeck, Christian; Hof, Patrick R.; Amaral, David G.; Fong, Sania G.; Buonocore, Michael H.; Stern, Yaakov; Barnes, Carol A.
targetTB: A target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis
Background Tuberculosis still remains one of the largest killer infectious diseases, warranting the identification of newer targets and drugs. Identification and validation of appropriate targets for designing drugs are critical steps in drug discovery, which are at present major bottle-necks. A majority of drugs in current clinical use for many diseases have been designed without the knowledge of the targets, perhaps because standard methodologies to identify such targets in a high-throughput fashion do not really exist. With different kinds of 'omics' data that are now available, computational approaches can be powerful means of obtaining short-lists of possible targets for further experimental validation. Results We report a comprehensive in silico target identification pipeline, targetTB, for Mycobacterium tuberculosis. The pipeline incorporates a network analysis of the protein-protein interactome, a flux balance analysis of the reactome, experimentally derived phenotype essentiality data, sequence analyses and a structural assessment of targetability, using novel algorithms recently developed by us. Using flux balance analysis and network analysis, proteins critical for survival of M. tuberculosis are first identified, followed by comparative genomics with the host, finally incorporating a novel structural analysis of the binding sites to assess the feasibility of a protein as a target. Further analyses include correlation with expression data and non-similarity to gut flora proteins as well as 'anti-targets' in the host, leading to the identification of 451 high-confidence targets. Through phylogenetic profiling against 228 pathogen genomes, shortlisted targets have been further explored to identify broad-spectrum antibiotic targets, while also identifying those specific to tuberculosis. Targets that address mycobacterial persistence and drug resistance mechanisms are also analysed. Conclusion The pipeline developed provides rational schema for drug target identification that are likely to have high rates of success, which is expected to save enormous amounts of money, resources and time in the drug discovery process. A thorough comparison with previously suggested targets in the literature demonstrates the usefulness of the integrated approach used in our study, highlighting the importance of systems-level analyses in particular. The method has the potential to be used as a general strategy for target identification and validation and hence significantly impact most drug discovery programmes.
Raman, Karthik; Yeturu, Kalidas; Chandra, Nagasuma
Modeling sediment transport as a spatio-temporal Markov process.
NASA Astrophysics Data System (ADS)
Despite a century of research about sediment transport by bedload occuring in rivers, its constitutive laws remain largely unknown. The proof being that our ability to predict mid-to-long term transported volumes within reasonable confidence interval is almost null. The intrinsic fluctuating nature of bedload transport may be one of the most important reasons why classical approaches fail. Microscopic probabilistic framework has the advantage of taking into account these fluctuations at the particle scale, to understand their effect on the macroscopic variables such as sediment flux. In this framework, bedload transport is seen as the random motion of particles (sand, gravel, pebbles...) over a two-dimensional surface (the river bed). The number of particles in motion, as well as their velocities, are random variables. In this talk, we show how a simple birth-death Markov model governing particle motion on a regular lattice accurately reproduces the spatio-temporal correlations observed at the macroscopic level. Entrainment, deposition and transport of particles by the turbulent fluid (air or water) are supposed to be independent and memoryless processes that modify the number of particles in motion. By means of the Poisson representation, we obtained a Fokker-Planck equation that is exactly equivalent to the master equation and thus valid for all cell sizes. The analysis shows that the number of moving particles evolves locally far from thermodynamic equilibrium. Several analytical results are presented and compared to experimental data. The index of dispersion (or variance over mean ratio) is proved to grow from unity at small scales to larger values at larger scales confirming the non Poisonnian behavior of bedload transport. Also, we study the one and two dimensional K-function, which gives the average number of moving particles located in a ball centered at a particle centroid function of the ball's radius.
Heyman, Joris; Ancey, Christophe
Spatiotemporal averaging of in-stream solute removal dynamics
NASA Astrophysics Data System (ADS)
The scale dependence of nutrient loads exported from a catchment is a function of complex interactions between hydrologic and biogeochemical processes that modulate the input signals from the hillslope by aggregation and attenuation in a converging river network. Observational data support an empirical inverse relation between the biogeochemical cycling rate constant for nitrate k (T-1) and the stream stage h (L), k = vf/h, with vf, the uptake velocity (LT-1), being constant in space under steady flow conditions. Here we offer a physical explanation for the persistence of this pattern across scales and then extend the analysis to spatiotemporal scaling of k under transient-flow conditions. Inverse k-h dependence arose as an emergent pattern by coupling the mechanistic Transient Storage Model with a network model. Analytical modeling indicated that (1) nitrate processing efficiency increases with increasing variability in the discharge Q and (2) temporal averaging had no effect on the exponent a of the k-h relationship (k = vf/ha) in catchments with low Q variability, but strong dependence arose in catchments with high variability in Q. Network modeling in domains with low Q variability confirmed that the exponent a was independent of temporal averaging, but vf was a function of the averaging timescale. The probability distribution functions for k could be adequately predicted using analytical approaches. Understanding the k-h scaling relationships enables the direct estimation of the variability in nutrient losses due to in-stream reactions without requiring explicit information for spatially distributed network modeling.
Basu, Nandita B.; Rao, P. Suresh C.; Thompson, Sally E.; Loukinova, Natalia V.; Donner, Simon D.; Ye, Sheng; Sivapalan, Murugesu
Spatio-temporal coupling of EEG signals in epilepsy
NASA Astrophysics Data System (ADS)
Approximately 1% of the world's population suffer from epileptic seizures throughout their lives that mostly come without sign or warning. Thus, epilepsy is the most common chronical disorder of the neurological system. In the past decades, the problem of detecting a pre-seizure state in epilepsy using EEG signals has been addressed in many contributions by various authors over the past two decades. Up to now, the goal of identifying an impending epileptic seizure with sufficient specificity and reliability has not yet been achieved. Cellular Nonlinear Networks (CNN) are characterized by local couplings of dynamical systems of comparably low complexity. Thus, they are well suited for an implementation as highly parallel analogue processors. Programmable sensor-processor realizations of CNN combine high computational power comparable to tera ops of digital processors with low power consumption. An algorithm allowing an automated and reliable detection of epileptic seizure precursors would be a"huge step" towards the vision of an implantable seizure warning device that could provide information to patients and for a time/event specific treatment directly in the brain. Recent contributions have shown that modeling of brain electrical activity by solutions of Reaction-Diffusion-CNN as well as the application of a CNN predictor taking into account values of neighboring electrodes may contribute to the realization of a seizure warning device. In this paper, a CNN based predictor corresponding to a spatio-temporal filter is applied to multi channel EEG data in order to identify mutual couplings for different channels which lead to a enhanced prediction quality. Long term EEG recordings of different patients are considered. Results calculated for these recordings with inter-ictal phases as well as phases with seizures will be discussed in detail.
Senger, Vanessa; M�ller, Jens; Tetzlaff, Ronald
A model of spatiotemporal tactile sensitivity linking psychophysics to tissue mechanics.
Sensitivities were measured for tangible spatiotemporal sinusoids applied to the index fingertip. The sinusoids had temporal frequencies of 8 and 128 Hz, in order to selectively activate the non-Pacinian I (NP I) and Pacinian (P) cutaneous mechanoreceptor systems, respectively, and had spatial frequencies from 0.00-1.03 cycles/mm. The sensitivity of the NP I system increased as the spatial frequency increased, whereas the sensitivity of the P system generally decreased as the spatial frequency increased. A mechanical model of the fingertip was used to calculate the normal and shear strains in the tissue, and a psychophysical linking hypothesis was introduced to predict tactile sensitivities based on the calculated strains. Specifically, the fingertip was modeled as a slab of a linear, isotropic, homogeneous, viscoelastic material. The boundary conditions were imposed by the spatiotemporal sinusoid at the top of the slab and the rigidly attached bone at the bottom of the slab. It was then assumed that the detection threshold was equal to the stimulus amplitude, which produced a constant, criterion strain at the location of the receptor. For both the P and NP I responses, the agreement between the predicted and measured sensitivities was best for calculations based on the normal strain, and for spatial frequencies below 0.5 cycles/mm. At higher spatial frequencies, the measured sensitivities were higher than predicted. The model also predicted the location of the P and NP I receptors in the tissue, the thickness of the tissue, and the value of the threshold strain for both receptor types. The predicted values agreed reasonably well with independent anatomical and physiological measurements. PMID:2732387
Van Doren, C L
Deformation localization in orogens: Spatiotemporal expression and thermodynamic constraint
NASA Astrophysics Data System (ADS)
Orogens are spatiotemporal expressions of instabilities in materials under load, constrained by thermodynamics, and preserved in the cold outer shell of the planet. Their pressure-temperature-time histories are consistent with the predictions of differential grade-2 (DG-2) materials in pure shear. We place the statistically invariant shear localization mechanism of these materials in a coherent thermodynamic context using an analysis of strained elastic materials. This prototype system exhibits non-classical thermodynamic symmetry-breaking, where the potentials are all functions of a single variable and the distinction between heat and work fades from view. Consequently, internal energy must be described by a monotonically decreasing function of the entropy in order for heat capacity and absolute temperature to be positive. The entropy itself exhibits an inverse dependence on length. These constraints are satisfied by the overall shape and slope of the distributed deformation threshold ?D for DG-2 materials, and its noted 1/length correlation with naturally observed folds as a function of thermomechanical competence ?/?. We predict that temperature in this non-linear elastic material will vary in proportion to the slope of ?D, being high at low competence, and low at high competence. Similar constraints apply to a self-gravitating body, where the energy function varies inversely with radius. Assigning zero pressure at the surface of the body, we also predict that pressure, the tensor trace of its stress-energy density, will vary inversely with radius. Thus, the body force of gravity will be expressed in this elastic self-gravitating system through the interplay of elastic and thermal lengths. Deformation localization in DG-2 materials arises due to the dynamic rescaling of lengths in response to a spike in the intrinsic energy ?I at ?/? = �. While the intrinsic ?I and localization ?L thresholds are monotonically decreasing for ?/? > �, they exhibit positive slopes at lower competence, signaling a return to classical thermodynamics and Joule heating in this transitional domain. Numerous structural and tectonic observations can be correlated using this remarkably simple model, beginning with the thickness and mechanical character of the brittle crust and oceanic lithosphere. In effect, this model projects the kinematic theory of plate tectonics into four-dimensional spacetime.
Patton, R. L.; Watkinson, A. John
Fluorescence advantages with microscopic spatiotemporal control
We present a clever design concept of using femtosecond laser pulses in microscopy by selective excitation or de-excitation of one fluorophore over the other overlapping one. Using either a simple pair of femtosecond pulses with variable delay or using a train of laser pulses at 20-50 Giga-Hertz excitation, we show controlled fluorescence excitation or suppression of one of the fluorophores with respect to the other through wave-packet interference, an effect that prevails even after the fluorophore coherence timescale. Such an approach can be used both under the single-photon excitation as well as in the multi-photon excitation conditions resulting in effective higher spatial resolution. Such high spatial resolution advantage with broadband-pulsed excitation is of immense benefit to multi-photon microscopy and can also be an effective detection scheme for trapped nanoparticles with near-infrared light. Such sub-diffraction limit trapping of nanoparticles is challenging and a two-photon fluorescence diagnostics allows a direct observation of a single nanoparticle in a femtosecond high-repetition rate laser trap, which promises new directions to spectroscopy at the single molecule level in solution. The gigantic peak power of femtosecond laser pulses at high repetition rate, even at low average powers, provide huge instantaneous gradient force that most effectively result in a stable optical trap for spatial control at sub-diffraction limit. Such studies have also enabled us to explore simultaneous control of internal and external degrees of freedom that require coupling of various control parameters to result in spatiotemporal control, which promises to be a versatile tool for the microscopic world.
Goswami, Debabrata; Roy, Debjit; De, Arijit K.
A multi-state spatio-temporal Markov model for categorized incidence of meningitis in sub-Saharan Africa.
Meningococcal meningitis is a major public health problem in the African Belt. Despite the obvious seasonality of epidemics, the factors driving them are still poorly understood. Here, we provide a first attempt to predict epidemics at the spatio-temporal scale required for in-year response, using a purely empirical approach. District-level weekly incidence rates for Niger (1986-2007) were discretized into latent, alert and epidemic states according to pre-specified epidemiological thresholds. We modelled the probabilities of transition between states, accounting for seasonality and spatio-temporal dependence. One-week-ahead predictions for entering the epidemic state were generated with specificity and negative predictive value >99%, sensitivity and positive predictive value >72%. On the annual scale, we predict the first entry of a district into the epidemic state with sensitivity 65?0%, positive predictive value 49?0%, and an average time gained of 4?6 weeks. These results could inform decisions on preparatory actions. PMID:22995184
Agier, L; Stanton, M; Soga, G; Diggle, P J
Genes involved in TGF?1-driven epithelial-mesenchymal transition of renal epithelial cells are topologically related in the human interactome map
Background Understanding how mesenchymal cells arise from epithelial cells could have a strong impact in unveiling mechanisms of epithelial cell plasticity underlying kidney regeneration and repair. In primary human tubular epithelial cells (HUTEC) under different TGF?1 concentrations we had observed epithelial-to-mesenchymal transition (EMT) but not epithelial-myofibroblast transdifferentiation. We hypothesized that the process triggered by TGF?1 could be a dedifferentiation event. The purpose of this study is to comprehensively delineate genetic programs associated with TGF?1-driven EMT in our in vitro model using gene expression profile on large-scale oligonucleotide microarrays. Results In HUTEC under TGF?1 stimulus, 977 genes were found differentially expressed. Thirty genes were identified whose expression depended directly on TGF?1 concentration. By mapping the differentially expressed genes in the Human Interactome Map using Cytoscape software, we identified a single scale-free network consisting of 2630 interacting proteins and containing 449 differentially expressed proteins. We identified 27 hub proteins in the interactome with more than 29 edges incident on them and encoded by differentially expressed genes. The Gene Ontology analysis showed an excess of up-regulated proteins involved in biological processes, such as "morphogenesis", "cell fate determination" and "regulation of development", and the most up-regulated genes belonged to these categories. In addition, 267 genes were mapped to the KEGG pathways and 14 pathways with more than nine differentially expressed genes were identified. In our model, Smad signaling was not the TGF?1 action effector; instead, the engagement of RAS/MAPK signaling pathway seems mainly to regulate genes involved in the cell cycle and proliferation/apoptosis. Conclusion Our present findings support the hypothesis that context-dependent EMT generated in our model by TGF?1 might be the outcome of a dedifferentiation. In fact: 1) the principal biological categories involved in the process concern morphogenesis and development; 2) the most up-regulated genes belong to these categories; and, finally, 3) some intracellular pathways are involved, whose engagement during kidney development and nephrogenesis is well known. These long-term effects of TGF?1 in HUTEC involve genes that are highly interconnected, thereby generating a scale-free network that we named the "TGF?1 interactome", whose hubs represent proteins that may have a crucial role for HUTEC in response to TGF?1.
Campanaro, Stefano; Picelli, Simone; Torregrossa, Rossella; Colluto, Laura; Ceol, Monica; Del Prete, Dorella; D'Angelo, Angela; Valle, Giorgio; Anglani, Franca
Targeted interactomics reveals a complex core cell cycle machinery in Arabidopsis thaliana
Cell proliferation is the main driving force for plant growth. Although genome sequence analysis revealed a high number of cell cycle genes in plants, little is known about the molecular complexes steering cell division. In a targeted proteomics approach, we mapped the core complex machinery at the heart of the Arabidopsis thaliana cell cycle control. Besides a central regulatory network of core complexes, we distinguished a peripheral network that links the core machinery to up- and downstream pathways. Over 100 new candidate cell cycle proteins were predicted and an in-depth biological interpretation demonstrated the hypothesis-generating power of the interaction data. The data set provided a comprehensive view on heterodimeric cyclin-dependent kinase (CDK)�cyclin complexes in plants. For the first time, inhibitory proteins of plant-specific B-type CDKs were discovered and the anaphase-promoting complex was characterized and extended. Important conclusions were that mitotic A- and B-type cyclins form complexes with the plant-specific B-type CDKs and not with CDKA;1, and that D-type cyclins and S-phase-specific A-type cyclins seem to be associated exclusively with CDKA;1. Furthermore, we could show that plants have evolved a combinatorial toolkit consisting of at least 92 different CDK�cyclin complex variants, which strongly underscores the functional diversification among the large family of cyclins and reflects the pivotal role of cell cycle regulation in the developmental plasticity of plants.
Van Leene, Jelle; Hollunder, Jens; Eeckhout, Dominique; Persiau, Geert; Van De Slijke, Eveline; Stals, Hilde; Van Isterdael, Gert; Verkest, Aurine; Neirynck, Sandy; Buffel, Yelle; De Bodt, Stefanie; Maere, Steven; Laukens, Kris; Pharazyn, Anne; Ferreira, Paulo C G; Eloy, Nubia; Renne, Charlotte; Meyer, Christian; Faure, Jean-Denis; Steinbrenner, Jens; Beynon, Jim; Larkin, John C; Van de Peer, Yves; Hilson, Pierre; Kuiper, Martin; De Veylder, Lieven; Van Onckelen, Harry; Inze, Dirk; Witters, Erwin; De Jaeger, Geert
Targeted interactomics reveals a complex core cell cycle machinery in Arabidopsis thaliana.
Cell proliferation is the main driving force for plant growth. Although genome sequence analysis revealed a high number of cell cycle genes in plants, little is known about the molecular complexes steering cell division. In a targeted proteomics approach, we mapped the core complex machinery at the heart of the Arabidopsis thaliana cell cycle control. Besides a central regulatory network of core complexes, we distinguished a peripheral network that links the core machinery to up- and downstream pathways. Over 100 new candidate cell cycle proteins were predicted and an in-depth biological interpretation demonstrated the hypothesis-generating power of the interaction data. The data set provided a comprehensive view on heterodimeric cyclin-dependent kinase (CDK)-cyclin complexes in plants. For the first time, inhibitory proteins of plant-specific B-type CDKs were discovered and the anaphase-promoting complex was characterized and extended. Important conclusions were that mitotic A- and B-type cyclins form complexes with the plant-specific B-type CDKs and not with CDKA;1, and that D-type cyclins and S-phase-specific A-type cyclins seem to be associated exclusively with CDKA;1. Furthermore, we could show that plants have evolved a combinatorial toolkit consisting of at least 92 different CDK-cyclin complex variants, which strongly underscores the functional diversification among the large family of cyclins and reflects the pivotal role of cell cycle regulation in the developmental plasticity of plants. PMID:20706207
Van Leene, Jelle; Hollunder, Jens; Eeckhout, Dominique; Persiau, Geert; Van De Slijke, Eveline; Stals, Hilde; Van Isterdael, Gert; Verkest, Aurine; Neirynck, Sandy; Buffel, Yelle; De Bodt, Stefanie; Maere, Steven; Laukens, Kris; Pharazyn, Anne; Ferreira, Paulo C G; Eloy, Nubia; Renne, Charlotte; Meyer, Christian; Faure, Jean-Denis; Steinbrenner, Jens; Beynon, Jim; Larkin, John C; Van de Peer, Yves; Hilson, Pierre; Kuiper, Martin; De Veylder, Lieven; Van Onckelen, Harry; Inz�, Dirk; Witters, Erwin; De Jaeger, Geert
The TOPLESS Interactome: A Framework for Gene Repression in Arabidopsis1[W][OA
Transcription factors activate or repress target gene expression or switch between activation and repression. In animals and yeast, Groucho/Tup1 corepressor proteins are recruited by diverse transcription factors to induce context-specific transcriptional repression. Two groups of Groucho/Tup1-like corepressors have been described in plants. LEUNIG and LEUNIG_HOMOLOG constitute one group and TOPLESS (TPL) and the four TPL-related (TPR) corepressors form the other. To discover the processes in which TPL and the TPR corepressors operate, high-throughput yeast two-hybrid approaches were used to identify interacting proteins. We found that TPL/TPR corepressors predominantly interact directly with specific transcription factors, many of which were previously implicated in transcriptional repression. The interacting transcription factors reveal that the TPL/TPR family has been coopted multiple times to modulate gene expression in diverse processes, including hormone signaling, stress responses, and the control of flowering time, for which we also show biological validation. The interaction data suggest novel mechanisms for the involvement of TPL/TPR corepressors in auxin and jasmonic acid signaling. A number of short repression domain (RD) sequences have previously been identified in Arabidopsis (Arabidopsis thaliana) transcription factors. All known RD sequences were enriched among the TPL/TPR interactors, and novel TPL-RD interactions were identified. We show that the presence of RD sequences is essential for TPL/TPR recruitment. These data provide a framework for TPL/TPR-dependent transcriptional repression. They allow for predictions about new repressive transcription factors, corepressor interactions, and repression mechanisms and identify a wide range of plant processes that utilize TPL/TPR-mediated gene repression.
Causier, Barry; Ashworth, Mary; Guo, Wenjia; Davies, Brendan
Spatio-temporal MODIS EVI gap filling under cloud cover: An example in Scotland
NASA Astrophysics Data System (ADS)
Time series of satellite data have an important role in the monitoring of regional and global ecosystem properties. Satellite images often present missing data due to atmospheric aerosol, clouds or other atmospheric conditions. Most methods proposed to minimise the effects of degradation and to restore signal values do not take into account the spatial and temporal correlation of the values in the pixels. The aim of this study was to propose and test a spatio-temporal interpolation method to reconstruct pixel values in MODIS data time series that are missing due to cloud cover or other image noise. The method presented and tested is an example of a hybrid Generalised Additive Model (GAM)-geostatistical space-time model, including the fitting of a smoother spatio-temporal trend and a spatial component to account for local details supported by information in covariates. The method is not limited by the type of noise or degradation of pixels values, latitude, vegetation dynamics and land uses. The application of cloud masks on the target image provided the data for a quantitative validation through the comparison between the modelled EVI values and those from the MODIS product. The method was able to restore data providing very good to adequate responses in series of simulations of missing data. The comparison of distributions showed good agreement and predictive capabilities. The spatio-temporal method always performed better and the use of kriged residuals was helpful for situations with high percentages of missing data. The spatial pattern and the local features were well preserved for cloud coverage ?20%. For higher percentages of missing data, the results were smoother with less local detail retained, but still showing the general spatial pattern of the variable. The method has proved to be flexible and able to provide reconstructed images reproducing spatial patterns and local features of the measured product, even with substantial amounts of missing pixels.
Poggio, Laura; Gimona, Alessandro; Brown, Iain
Novel semantic similarity measure improves an integrative approach to predicting gene functional associations
Background Elucidation of the direct/indirect protein interactions and gene associations is required to fully understand the workings of the cell. This can be achieved through the use of both low- and high-throughput biological experiments and in silico methods. We present GAP (Gene functional Association Predictor), an integrative method for predicting and characterizing gene functional associations. GAP integrates different biological features using a novel taxonomy-based semantic similarity measure in predicting and prioritizing high-quality putative gene associations. The proposed similarity measure increases information gain from the available gene annotations. The annotation information is incorporated from several public pathway databases, Gene Ontology annotations as well as drug and disease associations from the scientific literature. Results We evaluated GAP by comparing its prediction performance with several other well-known functional interaction prediction tools over a comprehensive dataset of known direct and indirect interactions, and observed significantly better prediction performance. We also selected a small set of GAP�s highly-scored novel predicted pairs (i.e., currently not found in any known database or dataset), and by manually searching the literature for experimental evidence accessible in the public domain, we confirmed different categories of predicted functional associations with available evidence of interaction. We also provided extra supporting evidence for subset of the predicted functionally-associated pairs using an expert curated database of genes associated to autism spectrum disorders. Conclusions GAP�s predicted �functional interactome� contains ?1M highly-scored predicted functional associations out of which about 90% are novel (i.e., not experimentally validated). GAP�s novel predictions connect disconnected components and singletons to the main connected component of the known interactome. It can, therefore, be a valuable resource for biologists by providing corroborating evidence for and facilitating the prioritization of potential direct or indirect interactions for experimental validation. GAP is freely accessible through a web portal: http://ophid.utoronto.ca/gap.
Spiking neural network for recognizing spatiotemporal sequences of spikes.
Sensory neurons in many brain areas spike with precise timing to stimuli with temporal structures, and encode temporally complex stimuli into spatiotemporal spikes. How the downstream neurons read out such neural code is an important unsolved problem. In this paper, we describe a decoding scheme using a spiking recurrent neural network. The network consists of excitatory neurons that form a synfire chain, and two globally inhibitory interneurons of different types that provide delayed feedforward and fast feedback inhibition, respectively. The network signals recognition of a specific spatiotemporal sequence when the last excitatory neuron down the synfire chain spikes, which happens if and only if that sequence was present in the input spike stream. The recognition scheme is invariant to variations in the intervals between input spikes within some range. The computation of the network can be mapped into that of a finite state machine. Our network provides a simple way to decode spatiotemporal spikes with diverse types of neurons. PMID:14995489
Jin, Dezhe Z
Spiking neural network for recognizing spatiotemporal sequences of spikes
NASA Astrophysics Data System (ADS)
Sensory neurons in many brain areas spike with precise timing to stimuli with temporal structures, and encode temporally complex stimuli into spatiotemporal spikes. How the downstream neurons read out such neural code is an important unsolved problem. In this paper, we describe a decoding scheme using a spiking recurrent neural network. The network consists of excitatory neurons that form a synfire chain, and two globally inhibitory interneurons of different types that provide delayed feedforward and fast feedback inhibition, respectively. The network signals recognition of a specific spatiotemporal sequence when the last excitatory neuron down the synfire chain spikes, which happens if and only if that sequence was present in the input spike stream. The recognition scheme is invariant to variations in the intervals between input spikes within some range. The computation of the network can be mapped into that of a finite state machine. Our network provides a simple way to decode spatiotemporal spikes with diverse types of neurons.
Jin, Dezhe Z.
Spatio-temporal behavior of spiral vortex flow
NASA Astrophysics Data System (ADS)
Experimental realizations of Taylor-Couette flow often include rigid end plates at bottom and top of the system. As a consequence of such end plates the bifurcation behavior of the basic laminar flow as well as the spatio-temporal properties of the emerging pattern, such as e.g. spiral vortex flow, can change. The latter point is in the focus of our present experimental study. The spatio-temporal behavior of spiral vortex flow in a Taylor-Couette system with rigid end plates is analyzed by a measurement technique based on Doppler-shift. This enables us to determine the spatial amplitude profile of up- and downward propagating spiral vortices within oscillatory flow states. Our study confirms experimentally recent numerical results of Hoffmann et al. [1] on the spatio-temporal properties of the spiral vortex state in finite systems with rigid end plates.
Heise, M.; K�lter, D.; Abshagen, J.; Pfister, G.
Experimental study of spatiotemporally localized surface gravity water waves.
We present experimental results on the study of spatiotemporally localized surface wave events on deep water that can be modeled using the Peregrine breather solution of the nonlinear Schr�dinger equation. These are often considered as prototypes of oceanic rogue waves that can focus wave energy into a single wave packet. For small steepness values of the carrier gravity waves the Peregrine breathers are relatively wide, thus providing an excellent agreement between the theory and experimental results. For larger steepnesses the focusing leads to temporally and spatially shorter events. Nevertheless, agreement between measurements and the Peregrine breather theory remains reasonably good, with discrepancies of modulation gradients and spatiotemporal symmetries being tolerable. Lifetimes and travel distances of the spatiotemporally localized wave events determined from the experiment are in good agreement with the theory. PMID:23005529
Chabchoub, A; Akhmediev, N; Hoffmann, N P
GEDS: GPU Execution of Continuous Queries on Spatio-Temporal Data Streams
Much research exists for the efficient processing of spatio-temporal data streams. However, all methods ultimately rely on an ill-equipped processor, namely a CPU, to evaluate concurrent, continuous spatio-temporal queries over these data streams. This paper presents GEDS, a scalable, Graphics Processing Unit (GPU)-based framework for the evaluation of continuous spatio-temporal queries over spatio-temporal data streams. GEDS employs the computation sharing
Jonathan Cazalas; Ratan Guha
Searching for Cellular Partners of Hantaviral Nonstructural Protein NSs: Y2H Screening of Mouse cDNA Library and Analysis of Cellular Interactome
Hantaviruses (Bunyaviridae) are negative-strand RNA viruses with a tripartite genome. The small (S) segment encodes the nucleocapsid protein and, in some hantaviruses, also the nonstructural protein (NSs). The aim of this study was to find potential cellular partners for the hantaviral NSs protein. Toward this aim, yeast two-hybrid (Y2H) screening of mouse cDNA library was performed followed by a search for potential NSs protein counterparts via analyzing a cellular interactome. The resulting interaction network was shown to form logical, clustered structures. Furthermore, several potential binding partners for the NSs protein, for instance ACBD3, were identified and, to prove the principle, interaction between NSs and ACBD3 proteins was demonstrated biochemically.
Parviainen, Ville; Vaheri, Antti; Renkonen, Risto; Bouloy, Michele; Plyusnin, Alexander
The nuclear basket proteins Mlp1p and Mlp2p are part of a dynamic interactome including Esc1p and the proteasome.
The basket of the nuclear pore complex (NPC) is generally depicted as a discrete structure of eight protein filaments that protrude into the nucleoplasm and converge in a ring distal to the NPC. We show that the yeast proteins Mlp1p and Mlp2p are necessary components of the nuclear basket and that they also embed the NPC within a dynamic protein network, whose extended interactome includes the spindle organizer, silencing factors, the proteasome, and key components of messenger ribonucleoproteins (mRNPs). Ultrastructural observations indicate that the basket reduces chromatin crowding around the central transporter of the NPC and might function as a docking site for mRNP during nuclear export. In addition, we show that the Mlps contribute to NPC positioning, nuclear stability, and nuclear envelope morphology. Our results suggest that the Mlps are multifunctional proteins linking the nuclear transport channel to multiple macromolecular complexes involved in the regulation of gene expression and chromatin maintenance. PMID:24152732
Niepel, Mario; Molloy, Kelly R; Williams, Rosemary; Farr, Julia C; Meinema, Anne C; Vecchietti, Nicholas; Cristea, Ileana M; Chait, Brian T; Rout, Michael P; Strambio-De-Castillia, Caterina
The nuclear basket proteins Mlp1p and Mlp2p are part of a dynamic interactome including Esc1p and the proteasome
The basket of the nuclear pore complex (NPC) is generally depicted as a discrete structure of eight protein filaments that protrude into the nucleoplasm and converge in a ring distal to the NPC. We show that the yeast proteins Mlp1p and Mlp2p are necessary components of the nuclear basket and that they also embed the NPC within a dynamic protein network, whose extended interactome includes the spindle organizer, silencing factors, the proteasome, and key components of messenger ribonucleoproteins (mRNPs). Ultrastructural observations indicate that the basket reduces chromatin crowding around the central transporter of the NPC and might function as a docking site for mRNP during nuclear export. In addition, we show that the Mlps contribute to NPC positioning, nuclear stability, and nuclear envelope morphology. Our results suggest that the Mlps are multifunctional proteins linking the nuclear transport channel to multiple macromolecular complexes involved in the regulation of gene expression and chromatin maintenance.
Niepel, Mario; Molloy, Kelly R.; Williams, Rosemary; Farr, Julia C.; Meinema, Anne C.; Vecchietti, Nicholas; Cristea, Ileana M.; Chait, Brian T.; Rout, Michael P.; Strambio-De-Castillia, Caterina
Male reproductive strategy explains spatiotemporal segregation in brown bears
1. Spatiotemporal segregation is often explained by the risk for offspring predation or by differences in physiology, predation risk vulnerability or competitive abilities related to size dimorphism. 2. Most large carnivores are size dimorphic and offspring predation is often intraspecific and related to nonparental infanticide (NPI). NPI can be a foraging strategy, a strategy to reduce competition, or a male reproductive strategy. Spatiotemporal segregation is widespread among large carnivores, but its nature remains poorly understood. 3. We evaluated three hypotheses to explain spatiotemporal segregation in the brown bear, a size-dimorphic large carnivore in which NPI is common; the �NPI � foraging/competition hypothesis', i.e. NPI as a foraging strategy or a strategy to reduce competition, the �NPI � sexual selection hypothesis�, i.e. infanticide as a male reproductive strategy and the �body size hypothesis�, i.e. body-size-related differences in physiology, predation risk vulnerability or competitive ability causes spatiotemporal segregation. To test these hypotheses, we quantified spatiotemporal segregation among adult males, lone adult females and females with cubs-of-the-year, based on GPS-relocation data (2006�2010) and resource selection functions in a Scandinavian population. 4. We found that spatiotemporal segregation was strongest between females with cubs-of-the-year and adult males during the mating season. During the mating season, females with cubs-of-the-year selected their resources, in contrast to adult males, in less rugged landscapes in relative close proximity to certain human-related variables, and in more open habitat types. After the mating season, females with cubs-of-the-year markedly shifted their resource selection towards a pattern more similar to that of their conspecifics. No strong spatiotemporal segregation was apparent between females with cubs-of-the-year and conspecifics during the mating and the postmating season. 5. The �NPI � sexual selection hypothesis� best explained spatiotemporal segregation in our study system. We suggest that females with cubs-of-the-year alter their resource selection to avoid infanticidal males. In species exhibiting NPI as a male reproductive strategy, female avoidance of infanticidal males is probably more common than observed or reported, and may come with a fitness cost if females trade safety for optimal resources.
Steyaert, Sam MJG; Kindberg, Jonas; Swenson, Jon E; Zedrosser, Andreas
Spatiotemporal structure of femtosecond Bessel beams from spatial light modulators.
We numerically investigate the spatiotemporal structure of Bessel beams generated with spatial light modulators (SLMs). Grating-like phase masks enable the spatial filtering of undesired diffraction orders produced by SLMs. Pulse front tilt and temporal broadening effects are investigated. In addition, we explore the influence of phase wrapping and show that the spatiotemporal structure of SLM-generated femtosecond Bessel beams is similar to Bessel X-pulses at short propagation distance and to subluminal pulsed Bessel beams at long propagation distance. PMID:24695141
Froehly, L; Jacquot, M; Lacourt, P A; Dudley, J M; Courvoisier, F
Quasiperiodic transition to spatiotemporal chaos in weakly ionized magnetoplasmas
NASA Astrophysics Data System (ADS)
The transition to temporal and then spatiotemporal chaos in a weakly ionized magnetoplasma system which supports nonlinear flute-type ionization-drift waves was studied. The system follows a complex quasiperiodic route with strong nonlinear mode-mode competition, a narrow frequency-locking interval, and an unstable third independent frequency to temporal chaos. At the onset of the spatial chaos, the discrete spatiotemporal modes decrease down to the noise floor, the temporal correlation is reduced more than ten times, and the correlation dimension jumps from less than 8 to greater than 12.
Chu, J. H.; I, Lin
Male reproductive strategy explains spatiotemporal segregation in brown bears.
Spatiotemporal segregation is often explained by the risk for offspring predation or by differences in physiology, predation risk vulnerability or competitive abilities related to size dimorphism. Most large carnivores are size dimorphic and offspring predation is often intraspecific and related to nonparental infanticide (NPI). NPI can be a foraging strategy, a strategy to reduce competition, or a male reproductive strategy. Spatiotemporal segregation is widespread among large carnivores, but its nature remains poorly understood. We evaluated three hypotheses to explain spatiotemporal segregation in the brown bear, a size-dimorphic large carnivore in which NPI is common; the 'NPI - foraging/competition hypothesis', i.e. NPI as a foraging strategy or a strategy to reduce competition, the 'NPI - sexual selection hypothesis', i.e. infanticide as a male reproductive strategy and the 'body size hypothesis', i.e. body-size-related differences in physiology, predation risk vulnerability or competitive ability causes spatiotemporal segregation. To test these hypotheses, we quantified spatiotemporal segregation among adult males, lone adult females and females with cubs-of-the-year, based on GPS-relocation data (2006-2010) and resource selection functions in a Scandinavian population. We found that spatiotemporal segregation was strongest between females with cubs-of-the-year and adult males during the mating season. During the mating season, females with cubs-of-the-year selected their resources, in contrast to adult males, in less rugged landscapes in relative close proximity to certain human-related variables, and in more open habitat types. After the mating season, females with cubs-of-the-year markedly shifted their resource selection towards a pattern more similar to that of their conspecifics. No strong spatiotemporal segregation was apparent between females with cubs-of-the-year and conspecifics during the mating and the postmating season. The 'NPI - sexual selection hypothesis' best explained spatiotemporal segregation in our study system. We suggest that females with cubs-of-the-year alter their resource selection to avoid infanticidal males. In species exhibiting NPI as a male reproductive strategy, female avoidance of infanticidal males is probably more common than observed or reported, and may come with a fitness cost if females trade safety for optimal resources. PMID:23461483
Steyaert, Sam M J G; Kindberg, Jonas; Swenson, Jon E; Zedrosser, Andreas
Time reversal and the spatio-temporal matched filter
It is known that focusing of an acoustic field by a time-reversal mirror (TRM) is equivalent to a spatio-temporal matched filter under conditions where the Green's function of the field satisfies reciprocity and is time invariant, i.e. the Green's function is independent of the choice of time origin. In this letter, it is shown that both reciprocity and time invariance can be replaced by a more general constraint on the Green's function that allows a TRM to implement the spatio-temporal matched filter even when conditions are time varying.
Lehman, S K; Poggio, A J; Kallman, J S; Meyer, A W; Candy, J V
Spatiotemporal evolution of dielectric driven cogenerated dust density waves
An experimental observation of spatiotemporal evolution of dust density waves (DDWs) in cogenerated dusty plasma in the presence of modified field induced by glass plate is reported. Various DDWs, such as vertical, oblique, and stationary, were detected simultaneously for the first time. Evolution of spatiotemporal complexity like bifurcation in propagating wavefronts is also observed. As dust concentration reaches extremely high value, the DDW collapses. Also, the oblique and nonpropagating mode vanishes when we increase the number of glass plates, while dust particles were trapped above each glass plates showing only vertical DDWs.
Sarkar, Sanjib; Bose, M. [Department of Physics, Jadavpur University, Kolkata 700032 (India)] [Department of Physics, Jadavpur University, Kolkata 700032 (India); Mukherjee, S. [FCIPT, Institute for Plasma Research, Gandhinagar 382428 (India)] [FCIPT, Institute for Plasma Research, Gandhinagar 382428 (India); Pramanik, J. [Kharagpur College, Kharagpur 721305, West Bengal (India)] [Kharagpur College, Kharagpur 721305, West Bengal (India)
Optical Flow by Plane-based Spatio-Temporal Correlation
NASA Astrophysics Data System (ADS)
The approach of spatio-temporal correlaton (STC) in which the object's flow is supposed to be point-symmetry for the searching point(pixel) has been proposed to improve the flow estimation of moving object. However, in the STC the shape of object becomes complex as Tree, the flows causes many errors. In this paper, we propose a plane-based spatio-temporal correlaton (PSTC) to cope with this. We show that the PSTC effectively estimates the flows of blurred �Tree" with Gaussian function through the experiments.
Kim, Jinwoo; Funato, Kazuteru; Wang, Rong-Long; Okazaki, Kozo
Real-time spatio-temporal analysis of dynamic scenes
We propose a set of tools for spatio-temporal real-time analysis of dynamic scenes. It is designed to improve the grounding\\u000a situation of autonomous agents in (simulated) physical domains. We introduce a knowledge processing pipeline ranging from\\u000a relevance-driven compilation of a qualitative scene description to a knowledge-based detection of complex event and action\\u000a sequences, conceived as a spatio-temporal pattern-matching problem. A
Tobias Warden; Ubbo Visser
Spatiotemporal modelling of viral infection dynamics
NASA Astrophysics Data System (ADS)
Viral kinetics have been studied extensively in the past through the use of ordinary differential equations describing the time evolution of the diseased state in a spatially well-mixed medium. However, emerging spatial structures such as localized populations of dead cells might affect the spread of infection, similar to the manner in which a counter-fire can stop a forest fire from spreading. In the first phase of the project, a simple two-dimensional cellular automaton model of viral infections was developed. It was validated against clinical immunological data for uncomplicated influenza A infections and shown to be accurate enough to adequately model them. In the second phase of the project, the simple two-dimensional cellular automaton model was used to investigate the effects of relaxing the well-mixed assumption on viral infection dynamics. It was shown that grouping the initially infected cells into patches rather than distributing them uniformly on the grid reduced the infection rate as only cells on the perimeter of the patch have healthy neighbours to infect. Use of a local epithelial cell regeneration rule where dead cells are replaced by healthy cells when an immediate neighbour divides was found to result in more extensive damage of the epithelium and yielded a better fit to experimental influenza A infection data than a global regeneration rule based on division rate of healthy cell. Finally, the addition of immune cell at the site of infection was found to be a better strategy at low infection levels, while addition at random locations on the grid was the better strategy at high infection level. In the last project, the movement of T cells within lymph nodes in the absence of antigen, was investigated. Based on individual T cell track data captured by two-photon microscopy experiments in vivo, a simple model was proposed for the motion of T cells. This is the first step towards the implementation of a more realistic spatiotemporal model of HIV than those proposed thus far.
Beauchemin, Catherine
Proteomic analysis of the SH2 domain-containing leukocyte protein of 76 kDa (SLP76) interactome in resting and activated primary mast cells [corrected].
We report the first proteomic analysis of the SLP76 interactome in resting and activated primary mouse mast cells. This was made possible by a novel genetic approach used for the first time here. It consists in generating knock-in mice that express signaling molecules bearing a C-terminal tag that has a high affinity for a streptavidin analog. Tagged molecules can be used as molecular baits to affinity-purify the molecular complex in which they are engaged, which can then be studied by mass spectrometry. We examined first SLP76 because, although this cytosolic adapter is critical for both T cell and mast cell activation, its role is well known in T cells but not in mast cells. Tagged SLP76 was expressed in physiological amounts and fully functional in mast cells. We unexpectedly found that SLP76 is exquisitely sensitive to mast cell granular proteases, that Zn(2+)-dependent metalloproteases are especially abundant in mast cells and that they were responsible for SLP76 degradation. Adding a Zn(2+) chelator fully protected SLP76 in mast cell lysates, thereby enabling an efficient affinity-purification of this adapter with its partners. Label-free quantitative mass spectrometry analysis of affinity-purified SLP76 interactomes uncovered both partners already described in T cells and novel partners seen in mast cells only. Noticeably, molecules inducibly recruited in both cell types primarily concur to activation signals, whereas molecules recruited in activated mast cells only are mostly associated with inhibition signals. The transmembrane adapter LAT2, and the serine/threonine kinase with an exchange factor activity Bcr were the most recruited molecules. Biochemical and functional validations established the unexpected finding that Bcr is recruited by SLP76 and positively regulates antigen-induced mast cell activation. Knock-in mice expressing tagged molecules with a normal tissue distribution and expression therefore provide potent novel tools to investigate signalosomes and to uncover novel signaling molecules in mast cells. PMID:23820730
Bounab, Yacine; Hesse, Anne-Marie-; Iannascoli, Bruno; Grieco, Luca; Cout�, Yohann; Niarakis, Anna; Roncagalli, Romain; Lie, Eunkyung; Lam, Kong-Peng; Demangel, Caroline; Thieffry, Denis; Garin, J�r�me; Malissen, Bernard; Da�ron, Marc
Spatio-temporal topological relationships between land parcels in cadastral database
NASA Astrophysics Data System (ADS)
There are complex spatio-temporal relationships among cadastral entities. Cadastral spatio-temporal data model should not only describe the data structure of cadastral objects, but also express cadastral spatio-temporal relationships between cadastral objects. In the past, many experts and scholars have proposed a variety of cadastral spatio-temporal data models, but few of them concentrated on the representation of spatiotemporal relationships and few of them make systematic studies on spatiotemporal relationships between cadastral objects. The studies on spatio-temporal topological relationships are not abundant. In the paper, we initially review current approaches to the studies of spatio-temporal topological relationships, and argue that spatio-temporal topological relation is the combination of temporal topology on the time dimension and spatial topology on the spatial dimension. Subsequently, we discuss and develop an integrated representation of spatio-temporal topological relationships within a 3-dimensional temporal space. In the end, based on the semantics of spatiotemporal changes between land parcels, we conclude the possible spatio-temporal topological relations between land parcels, which provide the theoretical basis for creating, updating and maintaining of land parcels in the cadastral database.
Song, W.; Zhang, F.
Spatiotemporal monthly rainfall forecasting for south-eastern and eastern Australia using climatic indices
NASA Astrophysics Data System (ADS)
Knowledge about future rainfall would significantly benefit land, water resources and agriculture management, as this assists with planning and management decisions. Forecasting spatiotemporal monthly rainfall is difficult, especially in Australia where there is a complex interaction between topography and the effect of Indian and Pacific Ocean. This study describes a method for spatiotemporal monthly rainfall forecasting in south-eastern and eastern part of Australia using climatic and non-climatic variables. Rainfall data were obtained from Bureau of Meteorology (BoM) from 136 high quality weather stations from the south-eastern and eastern part of Australia with monthly rainfall records from 1879 to 2012. To reduce spatial complexity of the area and improve model accuracy, spatial classification (regionalization) was considered as first step. Significant predictors for each sub-region among lagged climatic input variables were selected using Fuzzy Ranking Algorithm (FRA). Climate classification: 1) discovered homogenous sub-regions with a similar rainfall patterns and investigated spatiotemporal rainfall variations in the area, 2) allowed selection of significant predictors with a fine resolution for each area, 3) improved the prediction model and increased model accuracy. PCA was used to reduce the dimensions of the dataset and to remove the rainfall time series correlation. K-means clustering was used on the loadings of PCs describing 93% of long-term monthly rainfall variations. The analysis was repeated for different numbers of sub-regions (3 - 8) to identify the best number of clusters to improve the forecast model performance. Subsequently, a Fuzzy Ranking Algorithm (FRA) was applied to the lagged climatic predictors and monthly rainfall in each sub-region to identify the best predictors. After these two stages of pre-processing, a Neural Network model was developed and optimized for each of the sub-regions as well as for the entire area. It is concluded from the result of this study that climate classification can improve the result of monthly spatiotemporal rainfall forecast models in South-eastern and eastern Australia. Also, the number of sub-regions is one of the important parameters in ranking predictors at the modeling stage, and allows elucidation of climate influences for different sub regions. Classification of stations helps FRA to capture variations in Australian rainfall in space without influence of the rainfall seasonal cycle and regimes.
Montazerolghaem, Maryam; Vervoort, Willem; Minasny, Budiman; McBratney, Alex
A general science-based framework for dynamical spatio-temporal models
Spatio-temporal statistical models are increasingly being used across a wide variety of scientific disciplines to describe and predict spatially-explicit processes that evolve over time. Correspondingly, in recent years there has been a significant amount of research on new statistical methodology for such models. Although descriptive models that approach the problem from the second-order (covariance) perspective are important, and innovative work is being done in this regard, many real-world processes are dynamic, and it can be more efficient in some cases to characterize the associated spatio-temporal dependence by the use of dynamical models. The chief challenge with the specification of such dynamical models has been related to the curse of dimensionality. Even in fairly simple linear, first-order Markovian, Gaussian error settings, statistical models are often over parameterized. Hierarchical models have proven invaluable in their ability to deal to some extent with this issue by allowing dependency among groups of parameters. In addition, this framework has allowed for the specification of science based parameterizations (and associated prior distributions) in which classes of deterministic dynamical models (e. g., partial differential equations (PDEs), integro-difference equations (IDEs), matrix models, and agent-based models) are used to guide specific parameterizations. Most of the focus for the application of such models in statistics has been in the linear case. The problems mentioned above with linear dynamic models are compounded in the case of nonlinear models. In this sense, the need for coherent and sensible model parameterizations is not only helpful, it is essential. Here, we present an overview of a framework for incorporating scientific information to motivate dynamical spatio-temporal models. First, we illustrate the methodology with the linear case. We then develop a general nonlinear spatio-temporal framework that we call general quadratic nonlinearity and demonstrate that it accommodates many different classes of scientific-based parameterizations as special cases. The model is presented in a hierarchical Bayesian framework and is illustrated with examples from ecology and oceanography. ?? 2010 Sociedad de Estad??stica e Investigaci??n Operativa.
Wikle, C. K.; Hooten, M. B.
Hierarchical Bayesian spatio-temporal modeling and entropy-based network design
NASA Astrophysics Data System (ADS)
Typical spatio-temporal data include temperature, precipitation, atmospheric pressure, ozone concentration, personal income, infection prevalence, mosquito populations, among others. To model such data in a given region by hierarchical Bayesian kriging is undertaken in this paper. In addition, an environmental network design problem is also explored. For demonstration, we consider the ozone concentrations in the Toronto region of Ontario, Canada. There are many missing observations in the data. To proceed, we first formulate the hierarchical spatio-temporal model in terms of observed data. We then fill in some missing observations such that the data has the staircase structure. Thus, in light of Le and Zidek (2006), we model the ozone concentrations in Toronto region by hierarchical Bayesian kriging and derive a conditional predictive distribution of the ozone concentrations over unknown locations. To decide if a new monitoring station needs to be added or an existing station can be closed down, we solve this environmental network design problem by using the principle of maximum entropy.
Wu, Y.; Jin, B.; Chan, E.
Quantitative macroscopic treatment of the spatiotemporal properties of spin crossover solids based on a reaction diffusion equation
NASA Astrophysics Data System (ADS)
We propose here a new theoretical treatment of the spatiotemporal properties in spin-crosser solids, based on the expansion of the free energy taking into account the spatial fluctuations of the high-spin (HS) fraction. This leads to an equation of motion on the HS fraction following a reaction diffusion equation (RDE), in which most of the parameters can be derived from the experiments. This equation involves the true temporal and spatial scales at variance from the previous stochastic microscopic models, which were based on a homogeneous treatment of the crystal's properties. We have illustrated this new treatment for a two-dimensional rectangularly shaped system with a square symmetry and we could reproduce quantitatively the process of nucleation, growth, and propagation of the HS fraction inside the thermal hysteresis loop, accompanying a first-order transition. The computed spatiotemporal evolution of the system allowed one to follow the propagation of a well-defined macroscopic HS:LS interface, which was found in excellent quantitative agreement with the experimental observations of optical microscopy on the switchable spin crossover crystal [{Fe(NCSe)(py)2}2(m-bpypz)]. The RDE treatment should generate predictive models for novel spatiotemporal effects in spin crossover solids and more generally for all kinds of switchable molecular solids.
Paez-Espejo, Miguel; Sy, Mouhamadou; Varret, Fran�ois; Boukheddaden, Kamel
Adaptive Spatio-Temporal Filtering for Movement Related Potentials in EEG-Based Brain-Computer Interfaces.
Movement related potentials (MRPs) are used as features in many brain-computer interfaces (BCIs) based on electroencephalogram (EEG). MRP feature extraction is challenging since EEG is noisy and varies between subjects. Previous studies used spatial and spatio-temporal filtering methods to deal with these problems. However, they did not optimize temporal information or may have been susceptible to overfitting when training data are limited and the feature space is of high dimension. Furthermore, most of these studies manually select data windows and low-pass frequencies. We propose an adaptive spatio-temporal (AST) filtering method to model MRPs more accurately in lower dimensional space. AST automatically optimizes all parameters by employing a Gaussian kernel to construct a low-pass time-frequency filter and a linear ridge regression (LRR) algorithm to compute a spatial filter. Optimal parameters are simultaneously sought by minimizing leave-one-out cross-validation error through gradient descent. Using four BCI datasets from 12 individuals, we compare the performances of AST filter to two popular methods: the discriminant spatial pattern filter and regularized spatio-temporal filter. The results demonstrate that our AST filter can make more accurate predictions and is computationally feasible. PMID:24723632
Lu, Jun; Xie, Kan; McFarland, Dennis J
Synchronization of spatiotemporal chaotic systems by feedback control
We demonstrate that two identical spatiotemporal chaotic systems can be synchronized by (1) linking one or a few of their dynamical variables, and (2) applying a small feedback control to one of the systems. Numerical examples using the diffusively coupled logistic map lattice are given. The effect of noise and the limitation of the technique are discussed.
Lai, Y.; Grebogi, C. (Institute for Plasma Research, University of Maryland, College Park, Maryland 20742 (United States) Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, Maryland 21205 (United States) Department of Mathematics and Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742 (United States))
Spatiotemporal Analysis of Face Profiles: Detection, Segmentation, and Registration
We use a two-image approach to construct a 3D human facial model for multimedia applications. The images used are those of faces at direct frontal and side views. The selection of the side view from a sequence of facial images is automatically done by applying a spatiotemporal approach to face profile analysis. The extracted side profile is then segmented based
Behzad Dariush; Sing Bang Kang; Keith Waters
Spatiotemporal Coupling of the Tongue in Amyotrophic Lateral Sclerosis
ERIC Educational Resources Information Center
Purpose: The primary aim of the investigation was to identify deficits in spatiotemporal coupling between tongue regions in amyotrophic lateral sclerosis (ALS). The relations between disease-related changes in tongue movement patterns and speech intelligibility were also determined. Methods: The authors recorded word productions from 11�
Kuruvilla, Mili S.; Green, Jordan R.; Yunusova, Yana; Hanford, Kathy
Noise-seeded spatiotemporal modulation instability in normal dispersion
NASA Astrophysics Data System (ADS)
In optical second-harmonic generation with normal dispersion, the virtually infinite bandwidth of the unbounded, hyperbolic, modulational instability leads to quenching of spatial multisoliton formation and to the occurrence of a catastrophic spatiotemporal breakup when an extended beam is left to interact with an extremely weak external noise with a coherence time much shorter than that of the pump.
Salerno, D.; Jedrkiewicz, O.; Trull, J.; Valiulis, G.; Picozzi, A.; di Trapani, P.
Multi-vehicle control and optimization for spatiotemporal sampling
In this paper we analyze the mapping accuracy of a sensor network using a quantitative measure of the mapping error as performance metric. We use optimal interpolation to calculate the estimation error of a map of a spatiotemporal field produced by assimilating observations collected by a group of vehicles. The vehicles travel in a closed trajectory in a steady, uniform
Nitin Sydney; Derek A. Paley
Spatio-temporal saliency perception via hypercomplex frequency spectral contrast.
Salient object perception is the process of sensing the salient information from the spatio-temporal visual scenes, which is a rapid pre-attention mechanism for the target location in a visual smart sensor. In recent decades, many successful models of visual saliency perception have been proposed to simulate the pre-attention behavior. Since most of the methods usually need some ad hoc parameters or high-cost preprocessing, they are difficult to rapidly detect salient object or be implemented by computing parallelism in a smart sensor. In this paper, we propose a novel spatio-temporal saliency perception method based on spatio-temporal hypercomplex spectral contrast (HSC). Firstly, the proposed HSC algorithm represent the features in the HSV (hue, saturation and value) color space and features of motion by a hypercomplex number. Secondly, the spatio-temporal salient objects are efficiently detected by hypercomplex Fourier spectral contrast in parallel. Finally, our saliency perception model also incorporates with the non-uniform sampling, which is a common phenomenon of human vision that directs visual attention to the logarithmic center of the image/video in natural scenes. The experimental results on the public saliency perception datasets demonstrate the effectiveness of the proposed approach compared to eleven state-of-the-art approaches. In addition, we extend the proposed model to moving object extraction in dynamic scenes, and the proposed algorithm is superior to the traditional algorithms. PMID:23482090
Li, Ce; Xue, Jianru; Zheng, Nanning; Lan, Xuguang; Tian, Zhiqiang
Efficient motion weighted spatio-temporal video SSIM index
NASA Astrophysics Data System (ADS)
Recently, Seshadrinathan and Bovik proposed the Motion-based Video Integrity Evaluation (MOVIE) index for VQA.1, 2 MOVIE utilized a multi-scale spatio-temporal Gabor filter bank to decompose the videos and to compute motion vectors. Apart from its psychovisual inspiration, MOVIE is an interesting option for VQA owing to its performance. However, the use of MOVIE in a practical setting may prove to be difficult owing to the presence of the multi-scale optical flow computation. In order to bridge the gap between the conceptual elegance of MOVIE and a practical VQA algorithm, we propose a new VQA algorithm - the spatio-temporal video SSIM based on the essence of MOVIE. Spatio-temporal video SSIM utilizes motion information computed from a block-based motion-estimation algorithm and quality measures using a localized set of oriented spatio-temporal filters. In this paper we explain the algorithm and demonstrate its conceptual similarity to MOVIE; we explore its computational complexity and evaluate its performance on the popular VQEG dataset. We show that the proposed algorithm allows for efficient FR VQA without compromising on the performance while retaining the conceptual elegance of MOVIE.
Moorthy, Anush K.; Bovik, Alan C.
Overlapping Linear Quadtrees and Spatio-Temporal Query Processing
In this paper, indexing in spatio-temporal databases by using the technique of overlapping is investigated. Overlapping has been previously applied in various access methods to combine consecutive structure instances into a single structure, without storing identical sub-structures. In this way, space is saved without sacrificing time performance. A new access method, overlapping linear quadtrees is introduced. This structure is able
Theodoros Tzouramanis; Michael Vassilakopoulos; Yannis Manolopoulos
Fast Spatio-Temporal Data Mining from Large Geophysical Datasets
NASA Technical Reports Server (NTRS)
Use of the UCLA CONQUEST (CONtent-based Querying in Space and Time) is reviewed for performance of automatic cyclone extraction and detection of spatio-temporal blocking conditions on MPP. CONQUEST is a data analysis environment for knowledge and data mining to aid in high-resolution modeling of climate modeling.
Stolorz, P.; Mesrobian, E.; Muntz, R.; Santos, J. R.; Shek, E.; Yi, J.; Mechoso, C.; Farrara, J.
Spatiotemporal analysis of complex signals: Theory and applications
We present a space-time description of regular and complex phenomena which consists of a decomposition of a spatiotemporal signal into orthogonal temporal modes that we call chronos and orthogonal spatial modes that we call topos. This permits the introduction of several characteristics of the signal, three characteristic energies and entropies (one temporal, one spatial, and one global), and a characteristic
Nadine Aubry; R�gis Guyonnet; Ricardo Lima
Finding Spatio-Temporal Patterns in Large Sensor Datasets
ERIC Educational Resources Information Center
Spatial or temporal data mining tasks are performed in the context of the relevant space, defined by a spatial neighborhood, and the relevant time period, defined by a specific time interval. Furthermore, when mining large spatio-temporal datasets, interesting patterns typically emerge where the dataset is most dynamic. This dissertation is�
McGuire, Michael Patrick
Kernel Averaged Predictors for Spatio-Temporal Regression Models
In applications where covariates and responses are observed across space and time, a common goal is to quantify the effect of a change in the covariates on the response while adequately accounting for the spatio-temporal structure of the observations. The most common approach for building such a model is to confine the relationship between a covariate and response variable to a single spatio-temporal location. However, oftentimes the relationship between the response and predictors may extend across space and time. In other words, the response may be affected by levels of predictors in spatio-temporal proximity to the response location. Here, a flexible modeling framework is proposed to capture such spatial and temporal lagged effects between a predictor and a response. Specifically, kernel functions are used to weight a spatio-temporal covariate surface in a regression model for the response. The kernels are assumed to be parametric and non-stationary with the data informing the parameter values of the kernel. The methodology is illustrated on simulated data as well as a physical data set of ozone concentrations to be explained by temperature.
Gelfand, Alan E.
Spiking neural network for recognizing spatiotemporal sequences of spikes
Sensory neurons in many brain areas spike with precise timing to stimuli with temporal structures, and encode temporally complex stimuli into spatiotemporal spikes. How the downstream neurons read out such neural code is an important unsolved problem. In this paper, we describe a decoding scheme using a spiking recurrent neural network. The network consists of excitatory neurons that form a
Dezhe Z. Jin
Supporting user-defined granularities in a spatiotemporal conceptual model
Granularities are integral to spatial and temporal data. A large number of applications require storage of facts along with their temporal and spatial context, which needs to be expressed in terms of appropriate granularities. For many real-world applications, a single granularity in the database is insufficient. In order to support any type of spatial or temporal reasoning, the semantics related to granularities needs to be embedded in the database. Specifying granularities related to facts is an important part of conceptual database design because under-specifying the granularity can restrict an application, affect the relative ordering of events and impact the topological relationships. Closely related to granularities is indeterminacy, i.e., an occurrence time or location associated with a fact that is not known exactly. In this paper, we present an ontology for spatial granularities that is a natural analog of temporal granularities. We propose an upward-compatible, annotation-based spatiotemporal conceptual model that can comprehensively capture the semantics related to spatial and temporal granularities, and indeterminacy without requiring new spatiotemporal constructs. We specify the formal semantics of this spatiotemporal conceptual model via translation to a conventional conceptual model. To underscore the practical focus of our approach, we describe an on-going case study. We apply our approach to a hydrogeologic application at the United States Geologic Survey and demonstrate that our proposed granularity-based spatiotemporal conceptual model is straightforward to use and is comprehensive.
Khatri, V.; Ram, S.; Snodgrass, R. T.; O'Brien, G. M.
Spatiotemporal modeling of irregularly spaced Aerosol Optical Depth data
Many advancements have been introduced to tackle spatial and temporal structures in data. When the spatial and/or temporal domains are relatively large, assumptions must be made to account for the sheer size of the data. The large data size, coupled with realities that come with observational data, make it difficult for all of these assumptions to be met. In particular, air quality data are very sparse across geographic space and time, due to a limited air pollution monitoring network. These �missing� values make it diffcult to incorporate most dimension reduction techniques developed for high-dimensional spatiotemporal data. This article examines aerosol optical depth (AOD), an indirect measure of radiative forcing, and air quality. The spatiotemporal distribution of AOD can be influenced by both natural (e.g., meteorological conditions) and anthropogenic factors (e.g., emission from industries and transport). After accounting for natural factors influencing AOD, we examine the spatiotemporal relationship in the remaining human influenced portion of AOD. The presented data cover a portion of India surrounding New Delhi from 2000 � 2006. The proposed method is demonstrated showing how it can handle the large spatiotemporal structure containing so much missing data for both meteorologic conditions and AOD over time and space.
Oleson, Jacob J.; Kumar, Naresh; Smith, Brian J.
Spatiotemporal Spike Encoding of a Continuous External Signal
Interspike intervals of spikes emitted from an integrator neuron model of sensory neurons can encode input information represented as a continuous signal from a deterministic system. If a real brain uses spike timing as a means of information processing, other neurons receiving spatiotemporal spikes from such sensory neurons must also be capable of treating information included in deterministic interspike intervals.
Naoki Masuda; Kazuyuki Aihara
Controlling Spatiotemporal Chaos Using Nonlinear Feedback Functional Method
NASA Astrophysics Data System (ADS)
Nonlinear feedback functional method for controlling spatiotemporal chaos is presented. As a key point, some typical kinds of nonlinear feedback functions are given. The efficiency and robustness of the method based on the flexibility of choices of nonlinear feedback functions are discussed.
Fang, Jin-qing; M, K. Ali; Fang, Qing
KNN-kernel based clustering for spatio-temporal database
Extracting and analyzing the interesting patterns from spatio-temporal databases, have drawn a great interest in various fields of research. Recently, a number of experiments have explored the problem of spatial or temporal data mining, and some clustering algorithms have been proposed. However, not many studies have been dealing with the integration of spatial data mining and temporal data mining. Moreover,
A. Musdholifah; S. Z. B. M. Hashim; Ito Wasito
Spatio-temporal laplacian pyramid coding for action recognition.
We present a novel descriptor, called spatio-temporal Laplacian pyramid coding (STLPC), for holistic representation of human actions. In contrast to sparse representations based on detected local interest points, STLPC regards a video sequence as a whole with spatio-temporal features directly extracted from it, which prevents the loss of information in sparse representations. Through decomposing each sequence into a set of band-pass-filtered components, the proposed pyramid model localizes features residing at different scales, and therefore is able to effectively encode the motion information of actions. To make features further invariant and resistant to distortions as well as noise, a bank of 3-D Gabor filters is applied to each level of the Laplacian pyramid, followed by max pooling within filter bands and over spatio-temporal neighborhoods. Since the convolving and pooling are performed spatio-temporally, the coding model can capture structural and motion information simultaneously and provide an informative representation of actions. The proposed method achieves superb recognition rates on the KTH, the multiview IXMAS, the challenging UCF Sports, and the newly released HMDB51 datasets. It outperforms state of the art methods showing its great potential on action recognition. PMID:23912503
Shao, Ling; Zhen, Xiantong; Tao, Dacheng; Li, Xuelong
A New Spatio-Temporal Fast Motion Estimation Algorithm
A new spatio-temporal approach is proposed for fast block motion estimation in video coding. The approach exploits the existing correlation of the spatio- temporal block neighborhood by utilizing the frequency of appearance of the neighborhood's motion vectors. Extensive simulations show that the proposed algorithm performs close to the full search algorithm (in terms of quality) with a significant computational gain.
V. Fotopoulos; A. N. Skodras
Cubic map algebra functions for spatio-temporal analysis
We propose an extension of map algebra to three dimensions for spatio-temporal data handling. This approach yields a new class of map algebra functions that we call "cube functions." Whereas conventional map algebra functions operate on data layers representing two-dimensional space, cube functions operate on data cubes representing two-dimensional space over a third-dimensional period of time. We describe the prototype implementation of a spatio-temporal data structure and selected cube function versions of conventional local, focal, and zonal map algebra functions. The utility of cube functions is demonstrated through a case study analyzing the spatio-temporal variability of remotely sensed, southeastern U.S. vegetation character over various land covers and during different El Nin??o/Southern Oscillation (ENSO) phases. Like conventional map algebra, the application of cube functions may demand significant data preprocessing when integrating diverse data sets, and are subject to limitations related to data storage and algorithm performance. Solutions to these issues include extending data compression and computing strategies for calculations on very large data volumes to spatio-temporal data handling.
Mennis, J.; Viger, R.; Tomlin, C. D.
Perturbation of the heparin/heparin-sulfate interactome of human breast cancer cells modulates pro-tumourigenic effects associated with PI3K/Akt and MAPK/ERK signalling.
Heparansulfate-proteoglycans (HSPGs) interact via their polyanionic heparansulfate (HS) side chains with a variety of proteins on the cell surface or within the extracellular matrix membrane. The large number of heparin/HS binding proteins form a highly interconnected functional network, which has been termed as the heparin/HS interactome and is functionally linked to physiological and pathological processes. The aim of this study was to investigate the global effect of these protein-HSPG interactions on the tumourigenicity of two breast cancer cell lines (MCF-7 and MDA-MB-231). Cancer cells were cultured in serum-free medium and treated with a concentration of heparin which was capable of modulating HS/ligand interaction. Microarray analysis of MCF-7 cells cultured under these conditions showed that expression of 105 of 1,357 genes potentially related to the pathogenesis of breast neoplasm was significantly altered by heparin treatment. The changes in gene expression correlated with a less tumourigenic phenotype, including reduction of cell adhesive, invasive and migratory properties. These effects were associated with an inhibition of the PI3K/Akt and Raf/MEK/ERK signalling pathways. The modulatory effect of heparin on HS-associated activity was confirmed with one example of heparin/HS interactomes, transforming growth factor ? (TGF?). The innate TGF? activity of MCF-7 cells was reduced by heparin treatment, with specific interruption of the TGF?-Smad signalling pathway. The pro-tumourigenic contribution of the heparin/HS interactomes was verified in cells in which HSPG synthesis was blocked using ?-xyloside. In conclusion, the interaction between cell surface HPSGs and innate heparin/HS interactomes makes a significant contribution to the tumourigenicity. PMID:23571852
Chen, Yunliang; Scully, Michael; Dawson, Gloria; Goodwin, Christopher; Xia, Min; Lu, Xinjie; Kakkar, Ajay
Using atmospheric observations to evaluate the spatiotemporal variability of CO2 fluxes simulated by terrestrial biospheric models
NASA Astrophysics Data System (ADS)
Terrestrial biospheric models (TBMs) are used to extrapolate local observations and process-level understanding of land-atmosphere carbon exchange to larger regions, and serve as a predictive tool for examining carbon-climate interactions. Understanding the performance of TBMs is thus crucial to the carbon cycle and climate science. In this study, we propose a statistical model selection approach for evaluating the spatiotemporal patterns of net ecosystem exchange (NEE) simulated by TBMs using atmospheric CO2 measurements. We find that current atmospheric observations are sensitive to the underlying spatiotemporal flux variability at sub-biome scales for a large portion of the North American continent, and that atmospheric observations can therefore be used to evaluate simulated spatiotemporal flux patterns, rather than focusing solely on flux magnitudes at aggregated scales. Results show that the proposed approach can be used to assess whether a TBM represents a substantial portion of the underlying flux variability as well as to differentiate among multiple competing TBMs. When applying the proposed approach to four prototypical TBMs, we find that the performance of TBMs varies substantially across seasons, with best performance during the growing season and limited skill during transition seasons. This seasonal difference in the ability of TBMs to represent the spatiotemporal flux variability may reflect the models' capability to represent the seasonally-varying influence of environmental drivers on fluxes. While none of the TBMs consistently outperforms the others, differences among the examined models are at least partially attributable to their internal structures. Overall, the proposed approach provides a new avenue for evaluating TBM performance based on sub-biome scale flux patterns, presenting an opportunity for assessing and informing model development using atmospheric observations.
Fang, Y.; Michalak, A. M.; Shiga, Y. P.; Yadav, V.
Spatiotemporal Analysis of Normal and Pathological Human Vocal Fold Vibrations
Purpose In order for spatiotemporal analysis to become a relevant clinical tool, it must be applied to human vocal fold vibration. Receiver operating characteristic (ROC) analysis will help assess the ability of spatiotemporal parameters to detect pathological vibration. Materials and Methods Spatiotemporal parameters of correlation length and entropy were extracted from high speed videos of 124 subjects, 67 without vocal fold pathology and 57 with either vocal fold polyps or nodules. Mann-Whitney rank sum tests were performed to compare normal vocal fold vibrations to pathological vibrations, and ROC analysis was used to assess the diagnostic value of spatiotemporal analysis. Results A statistically significant difference was found between the normal and pathological groups in both correlation length (P < 0.001) and entropy (P < 0.001). ROC analysis showed area under the curve (AUC) of 0.85 for correlation length, 0.87 for entropy, and 0.92 when the two parameters were combined. A statistically significant difference was not found between the nodules and polyps groups in either correlation length (P = 0.227) or entropy (P = 0.943). ROC analysis showed AUC of 0.63 for correlation length and 0.51 for entropy. Conclusions Although they could not effectively distinguish vibration of vocal folds with nodules from those with polyps, the spatiotemporal parameters correlation length and entropy exhibit the ability to differentiate normal and pathological vocal fold vibration, and may represent a diagnostic tool for objectively detecting abnormal vibration in the future, especially in neurological voice disorders and vocal folds without a visible lesion.
Krausert, Christopher R.; Liang, Yufang; Zhang, Yu; Rieves, Adam L.; Geurink, Kyle R.; Jiang, Jack J.
Autophagy-related gene 12 (ATG12) is a novel determinant of primary resistance to HER2-targeted therapies: Utility of transcriptome analysis of the autophagy interactome to guide breast cancer treatment
The autophagic process, which can facilitate breast cancer resistance to endocrine, cytotoxic, and molecularly targeted agents, is mainly regulated at the post-translational level. Although recent studies have suggested a possible transcriptome regulation of the autophagic genes, little is known about either the analysis tools that can be applied or the functional importance of putative candidate genes emerging from autophagy-dedicated transcriptome studies. In this context, we evaluated whether the constitutive activation of the autophagy machinery, as revealed by a transcriptome analysis using an autophagy-focused polymerase chain reaction (PCR) array, might allow for the identification of novel autophagy-specific biomarkers for intrinsic (primary) resistance to HER2-targeted therapies. Quantitative real-time PCR (qRT-PCR)-based profiling of 84 genes involved in autophagy revealed that, when compared to trastuzumab-sensitive SKBR3 cells, the positive regulator of autophagic vesicle formation ATG12 (autophagy-related gene 12) was the most differentially up-regulated gene in JIMT1 cells, a model of intrinsic cross-resistance to trastuzumab and other HER1/2-targeting drugs. An analysis of the transcriptional status of ATG12 in > 50 breast cancer cell lines suggested that the ATG12 transcript is commonly upregulated in trastuzumab-unresponsive HER2-overexpressing breast cancer cells. A lentiviral-delivered small hairpin RNA stable knockdown of the ATG12 gene fully suppressed the refractoriness of JIMT1 cells to trastuzumab, erlotinib, gefitinib, and lapatinib in vitro. ATG12 silencing significantly reduced JIMT1 tumor growth induced by subcutaneous injection in nude mice. Remarkably, the outgrowth of trastuzumab-unresponsive tumors was prevented completely when trastuzumab treatment was administered in an ATG12-silenced genetic background. We demonstrate for the first time the usefulness of low-density, autophagy-dedicated qRT-PCR-based platforms for monitoring primary resistance to HER2-targeted therapies by transcriptionally screening the autophagy interactome. The degree of predictive accuracy warrants further investigation in the clinical situation.
Corominas-Faja, Bruna; Urruticoechea, Ander; Martin-Castillo, Begona; Menendez, Javier A.
Asymmetric spatiotemporal chaos induced by a polypoid mass in the excised larynx
NASA Astrophysics Data System (ADS)
In this paper, asymmetric spatiotemporal chaos induced by a polypoid mass simulating the laryngeal pathology of a vocal polyp is experimentally observed using high-speed imaging in an excised larynx. Spatiotemporal analysis reveals that the normal vocal folds show spatiotemporal correlation and symmetry. Normal vocal fold vibrations are dominated mainly by the first vibratory eigenmode. However, pathological vocal folds with a polypoid mass show broken symmetry and spatiotemporal irregularity. The spatial correlation is decreased. The pathological vocal folds spread vibratory energy across a large number of eigenmodes and induce asymmetric spatiotemporal chaos. High-order eigenmodes show complicated dynamics. Spatiotemporal analysis provides a valuable biomedical application for investigating the spatiotemporal chaotic dynamics of pathological vocal fold systems with a polypoid mass and may represent a valuable clinical tool for the detection of laryngeal mass lesion using high-speed imaging.
Zhang, Yu; Jiang, Jack J.
Breast Cancer DNA Interactome.
National Technical Information Service (NTIS)
With the development of new methodologies has come a greater appreciation of how genes are able to interact with distal regulatory elements. Gene transcription may be regulated by remote enhancer regions through chromosome looping. The role that higher or...
A. R. Hoffman
Breast Cancer DNA Interactome.
National Technical Information Service (NTIS)
With the development of new methodologies has come a greater appreciation of how genes are able to interact with distal regulatory elements. Gene transcription may be regulated by remote enhancer regions through chromosome looping. The role that higher or...
A. R. Hoffman
A psychophysical and computational analysis of the spatio-temporal mechanisms underlying the flash-lag effect.
Several accounts put forth to explain the flash-lag effect (FLE) rely mainly on either spatial or temporal mechanisms. Here we investigated the relationship between these mechanisms by psychophysical and theoretical approaches. In a first experiment we assessed the magnitudes of the FLE and temporal-order judgments performed under identical visual stimulation. The results were interpreted by means of simulations of an artificial neural network, that was also employed to make predictions concerning the FLE. The model predicted that a spatio-temporal mislocalisation would emerge from two, continuous and abrupt-onset, moving stimuli. Additionally, a straightforward prediction of the model revealed that the magnitude of this mislocalisation should be task-dependent, increasing when the use of the abrupt-onset moving stimulus switches from a temporal marker only to both temporal and spatial markers. Our findings confirmed the model's predictions and point to an indissoluble interplay between spatial facilitation and processing delays in the FLE. PMID:19227376
Cravo, Andr� M; Baldo, Marcus V C
Spatio-temporal development of the long and short-wave vortex-pair instabilities
NASA Astrophysics Data System (ADS)
We consider the spatio-temporal development of the long-wave and short-wave instabilities in a pair of counter-rotating vortices in the presence of a uniform axial advection velocity. The stability properties depend upon the aspect ratio a/b of the vortex pair, where a is the core radius of the vortices and b their separation, and upon W0/U0 the ratio between the self-induced velocity of the pair and the axial advection velocity. For sufficiently small W0/U0, the instabilities are convective, but an increase of W0/U0 may lead to an absolute instability. Near the absolute instability threshold, spatial growth rates are larger than those predicted by temporal stability theory. Considering aeronautical applications, it is shown that instabilities of the type considered in this communication cannot become absolute in farfield wakes of high aspect ratio wings.
Fabre, David; Cossu, Carlo; Jacquin, Laurent
Spatio-Temporally Restricted Expression of Cell Adhesion Molecules during Chicken Embryonic Development
Differential cell adhesive properties are known to regulate important developmental events like cell sorting and cell migration. Cadherins and protocadherins are known to mediate these cellular properties. Though a large number of such molecules have been predicted, their characterization in terms of interactive properties and cellular roles is far from being comprehensive. To narrow down the tissue context and collect correlative evidence for tissue specific roles of these molecules, we have carried out whole-mount in situ hybridization based RNA expression study for seven cadherins and four protocadherins. In developing chicken embryos (HH stages 18, 22, 26 and 28) cadherins and protocadherins are expressed in tissue restricted manner. This expression study elucidates precise expression domains of cell adhesion molecules in the context of developing embryos. These expression domains provide spatio-temporal context in which the function of these genes can be further explored.
Roy, Priti; Bandyopadhyay, Amitabha
Challenges for modelling spatio-temporal variations of malaria risk in Malawi
NASA Astrophysics Data System (ADS)
Malaria is the leading cause of morbidity and mortality in Malawi with more than 6 million episodes reported each year. Malaria poses a huge economic burden to Malawi in terms of the direct cost of treating malaria patients and also indirect costs resulting from workdays lost in agriculture and industry and absenteeism from school. Malawi implements malaria control activities within the Roll Back Malaria framework, with the objective to provide those most at risk (i.e. children under five years, pregnant woman and individuals with suppressed immune systems) access to personal and community protective measures. However, at present there is no mechanism by which to target the most 'at risk' populations ahead of an impending epidemic. Malaria transmission is influenced by variations in meteorological conditions, which impact the biology of the mosquito and the availability of breeding sites, but also socio-economic conditions such as levels of urbanisation, poverty and education, which influence human vulnerability and vector habitat. The many potential drivers of malaria, both extrinsic, such as climate, and intrinsic, such as population immunity are often difficult to disentangle. This presents a challenge for modelling of malaria risk in space and time. Using an age-stratified spatio-temporal dataset of malaria cases at the district level from July 2004 - June 2011, we use a spatio-temporal modelling framework to model variations in malaria risk in Malawi. Climatic and topographic variations are accounted for using an interpolation method to relate gridded products to administrative districts. District level data is tested in the model to account for confounding factors, including the proportion of the population living in urban areas; residing in traditional housing; with no toilet facilities; who do not attend school, etc, the number of health facilities per population and yearly estimates of insecticide-treated mosquito net distribution. In order to account for the unobserved confounding factors that influence malaria, which are not accounted for using measured covariates, a negative binomial generalised linear mixed model (GLMM) is adopted, which includes structured and unstructured spatial and temporal random effects. The parameters in this spatio-temporal Bayesian hierarchical model are estimated using Markov Chain Monte Carlo (MCMC). This allows posterior predictive distributions for disease risk to be derived for each spatial location and time period. A novel visualisation technique is then used to display seasonal probabilistic forecasts of malaria risk, derived from the developed model using pre-defined risk category thresholds, on a map. This technique allows decision makers to identify areas where the model predicts with certainty a particular malaria risk category (high, medium or low); in order to effectively target limited resources to those districts most at risk for a given season.
Lowe, R.; Chirombo, J.; Tompkins, A. M.
Spatiotemporal variability in surface rupturing behavior of thrust fault: Insights from paleoseismology for the 2008 Iwate-Miyagi Nairiku, Japan, earthquake
The 14 June 2008 Mw 6.9 Iwate-Miyagi Nairiku earthquake struck the mountainous region in northern Honshu and was accompanied by 20-km-long thrust-faulting surface rupture with ~50 cm vertical offset. Since these breaks occurred where no active fault trace had been mapped, we Japanese have been pursuing its predictability retrospectively for improving seismic hazard assessment. Here we argue the spatio-temporal variability
T. Maruyama; S. Toda; M. Omata; Y. Kohriya; Y. Mori
A knowledge based real-time travel time prediction system for urban network
Many approaches had been proposed for travel time prediction in these decades; most of them focus on the predicting the travel time on freeway or simple arterial network. Travel time prediction for urban network in real time is hard to achieve for several reasons: complexity and path routing problem in urban network, unavailability of real-time sensor data, spatiotemporal data coverage
Wei-hsun Lee; Shian-shyong Tseng; Sheng-han Tsai
Network architecture and spatio-temporally symmetric dynamics
NASA Astrophysics Data System (ADS)
We examine the relation between the structure of a network and the spatio-temporally symmetric periodic dynamics it can support. For solutions in which no cell is stationary, we show that only networks in which all cells interact with each other, or which contain a single group of interacting cells which drive the remainder of the network can exhibit such dynamics robustly. These characteristics of network architecture are not captured by the typical statistical quantities used to describe network structure. We illustrate the existence of spatio-temporally periodic solutions through a direct construction using ideas from coupled cell theory and the theory of weakly coupled oscillators, and show that these solutions can be stable in a very large region of parameter space. While we consider only a special type of network behavior, these ideas extend to more general architectures and dynamics.
Josi?, Kre�imir; T�r�k, Andrei
Spatiotemporal patterns and symmetry breaking on a ring electrode.
A series of experiments on a ring electrode with changes in a parameter, the applied potential, are described. Spatiotemporal patterns are investigated in a region of parameter space in which relaxation oscillations occur. The simplest state is a period 2Pi oscillation that has full O(2) symmetry so that at each instant the pattern is unchanged by rotations or reflections of the ring. With change in parameter a spatiotemporal period doubling occurs to period 4Pi. This is followed by a symmetry breaking to another state with period 4Pi and subsequently by a second period doubling to period 8Pi. Proper orthogonal decomposition is used as an aid in elucidating the nature of the transitions. PMID:11308565
Green, B J; Hudson, J L
Spatiotemporal analysis of ERP data in emotional processing
NASA Astrophysics Data System (ADS)
The aim of this paper is to analyze spatiotemporal patterns of Event-related potential (ERP) in emotional processing by using fuzzy k-means clustering method to segment ERP data into microstates.108 pictures (categorized as positive, negative and neutral) were presented to 24 healthy, right-handed subjects while 128-channel EEG data were recorded. For each subject, 3 artifact-free ERPs were computed under each condition. A modified fuzzy k-mean clustering method based on shape similarity is applied to the grand mean ERPs and the statistical analysis is performed to define the significance of each segmentation map. In the results, positive and negative conditions showed different spatiotemporal patterns of ERP. The results were in accord with other emotional study by fMRI or PET.
Hu, Jin; Tian, Jie; Yang, Lei; Pan, Xiaohong; Liu, Jianggang
Nature of Spatiotemporal Light Bullets in Bulk Kerr Media
NASA Astrophysics Data System (ADS)
We present a detailed experimental investigation which uncovers the nature of light bullets generated from self-focusing in a bulk dielectric medium with Kerr nonlinearity in the anomalous group velocity dispersion regime. By high dynamic range measurements of three-dimensional intensity profiles, we demonstrate that the light bullets consist of a sharply localized high-intensity core, which carries the self-compressed pulse and contains approximately 25% of the total energy, and a ring-shaped spatiotemporal periphery. Subdiffractive propagation along with dispersive broadening of the light bullets in free space after they exit the nonlinear medium indicate a strong space-time coupling within the bullet. This finding is confirmed by measurements of a spatiotemporal energy density flux that exhibits the same features as a stationary, polychromatic Bessel beam, thus highlighting the nature of the light bullets.
Majus, D.; Tamo�auskas, G.; Gra�ulevi?iūt�--, I.; Garejev, N.; Lotti, A.; Couairon, A.; Faccio, D.; Dubietis, A.
Suppression of spatiotemporal chaos under a constant electric potential signal
NASA Astrophysics Data System (ADS)
Suppression of spatiotemporal chaos in a one-dimensional nonlinear drift-wave equation driven by a sinusoidal wave is considered. Using a constant electric potential signal we demonstrate numerically that the spatiotemporal chaos can be effectively suppressed if the control parameters are properly chosen. The threshold and the controllable range of the control parameters are given. By establishing the kinetic equation of the system energy we find theoretically that an additional driving term in the energy equation is produced by the control signal and it can lead up to the frequency entrainment. Moreover, when the regular state is reached under the control, the system energy oscillates quasi-periodically, while the additional driving term decays to zero.
Yang, Chao-Yu; Tang, Guo-Ning; Liu, Jun-Xian
A Spatiotemporal Communication Protocol for Wireless Sensor Networks
In this paper, we present a spatiotemporal communication protocol for sensor networks, called SPEED. SPEED is specifically tailored to be a localized algorithm with minimal control overhead. End-to-end soft real-time communication is achieved by maintaining a desired delivery speed across the sensor network through a novel combination of feedback control and nondeterministic geographic forwarding. SPEED is a highly efficient and
Tian He; John A. Stankovic; Chenyang Lu; Tarek F. Abdelzaher
Cell Population Tracking and Lineage Construction with Spatiotemporal Context
Automated visual-tracking of cell populations in vitro using time-lapse phase contrast microscopy enables quantitative, systematic, and high-throughput measurements of cell behaviors. These measure- ments include the spatiotemporal quantification of cell migration, mitosis, apoptosis, and the reconstruc- tion of cell lineages. The combination of low signal-to-noise ratio of phase contrast microscopy images, high and varying densities of the cell cultures, topological
Kang Li; Mei Chen; Takeo Kanade
Spatio-temporal dynamics in a Turing model
NASA Astrophysics Data System (ADS)
In this paper we study numerically two-dimensional spatio-temporal pattern formation in a generic Turing model, by investigating the dynamical behavior of a monostable system in the presence of Turing-Hopf bifurcation. In addition, we study the interaction of instabilities in a tristable system. We speculate that the interaction of spatial and temporal instabilities in Turing systems might bring some insight to a recent biological finding of temporal patterns on animal skin.
Lepp�nen, T.; Karttunen, M.; Barrio, R. A.; Kaski, K.
Spatio-Temporal Tracking of Faces by Stereo Vision
This report contributes a new approach for the robust tracking of humans� heads and faces based on a spatio-temporal scene\\u000a analysis. The framework comprises aspects of structure and motion problems, as there are feature extraction, spatial and temporal\\u000a matching, re-calibration, tracking, and reconstruction. The scene is acquired through a calibrated stereo sensor. A cue processor\\u000a extracts invariant features in both
Markus Steffens; Werner Krybus; Christine Kohring; Danny Morton
Spatiotemporal energy models for the perception of motion
A motion sequence may be represented as a single pattern in x-y-t space; a velocity of motion corresponds to a three-dimensional orientation in this space. Motion information can be extracted by a system that responds to the oriented spatiotemporal energy. We discuss a class of models for human motion mechanisms in which the first stage consists of linear filters that
E. H. Adelson; J. R. Bergen
Spatiotemporal analytical model for coupled-cavity soliton fiber lasers
A closed-form analytical model for the spatiotemporal processes giving rise to soliton pulse formation and compression in an additive-pulse mode-locked coupled-cavity fiber laser systems is presented. The model is based on a modified nonlinear Schr�dinger equation for solitons. Perturbation techniques coupled with Fourier transform methods are employed to simplify the solution to the nonlinear Schr�dinger equation. The contributions of Gaussian
Eric J. Donkor; Mohammad N. Noman; Patrick D. Kumavor
Time-Aggregated Graphs for Modeling Spatio-temporal Networks
Given applications such as location based services and the spatio-temporal queries they may pose on a spatial network (e.g.,\\u000a road networks), the goal is to develop a simple and expressive model that honors the time dependence of the road network.\\u000a The model must support the design of efficient algorithms for computing the frequent queries on the network. This problem\\u000a is
Betsy George; Shashi Shekhar
Spatiotemporally Adaptive Estimation and Segmenation of OF-Fields
A grayvalue structure tensor provides knowledge about a local grayvalue variation. This knowledge can be used to devise a spatiotemporally adaptive optic flow estimation process. Such an adaptive estimation lowers the level at which the resulting optic flow (OF) field\\u000a is disturbed by noise and estimation artefacts. This in turn substantially simplifies the analysis of remaining � often subtle\\u000a �
Hans-hellmut Nagel; A. Gehrke
Idea-map: A spatiotemporal view of research ideas
in this paper, we describe idea-map, a mash-up application building on top of Linked Data Cloud. It reads in user's keywords (about research ideas) and executes a SPARQL query against DBLP endpoint. Spatial and temporal information is extracted and parsed from the query results and is further transformed to SIMILE\\/EXHIBIT to show a spatiotemporal map for the research ideas. Idea-map
He Hu; Xiaoyong Du
Reflection-antisymmetric spatiotemporal chaos under field-translational invariance.
We propose a route to spatiotemporal chaos, in which the system is assumed to have spatial reflection antisymmetry and field-translation symmetry. The lowest-order nonlinear equation that satisfies these symmetries is explored with the weak nonlinear analysis around the bifurcation point. We conclude that the nonlinear term ?(x)(2)u?(x)(3)u is important to make a nontrivial dynamics, and show that the nonlinear dynamical equation having this term produces a turbulent dynamics. PMID:22587169
Matsuo, Miki Y; Sano, Masaki
Spatiotemporal energy models for the perception of motion
A motion sequence may be represented as a single pattern in x-y-t space; a velocity of motion corresponds to a three-dimensional orientation in this space. Motion sinformation can be extracted by a system that responds to the oriented spatiotemporal energy. We discuss a class of models for human motion mechanisms in which the first stage consists of linear filters that
Edward H. Adelson; James R. Bergen
Spatiotemporal Hopfield neural cube for diagnosing recurrent nasal papilloma
Gadolinium-enhanced magnetic resonance imaging (MRI) is widely used to detect recurrent nasal tumours. A specifically designed\\u000a two-layer Hopfield neural network, called the spatiotemporal Hopfield neural cube (SHNC), is presented, to be used for detecting\\u000a recurrent nasal papilloma. Differing from conventional, two-dimensional Hopfield neural networks, the SHNC extends the one-layer,\\u000a two-dimensional Hopfield network in the original image plane into a two-layer,
C.-Y. Chang
Home Video Visual Quality Assessment With Spatiotemporal Factors
Compared with the video programs taken by professionals, home videos are always with low quality content resulted from non-professional capture skills. In this paper, we present a novel spatiotemporal quality assessment scheme in terms of low-level content features for home videos. In contrast to existing frame-level-based quality assessment approaches, a type of temporal segment of video, subshot, is selected as
Tao Mei; Xian-sheng Hua; Cai-zhi Zhu; He-qin Zhou; Shipeng Li
Spatiotemporally Resolved Acoustics in a Photoelastic Granular Material
NASA Astrophysics Data System (ADS)
The effect of the force chain network on sound propagation in a granular material is poorly understood. To quantitatively study these effects, we perform acoustics experiments in a two dimensional photoelastic granular material in which force chains are visible. We send acoustic pulses into the material from a point source and measure the effects of this pulse via two methods: accelerometers within individual grains and movies which produce spatiotemporally resolved measurements of the acoustic propagation.
Owens, Eli T.; Couvreur, St�phanie; Daniels, Karen E.
Free-carrier-driven spatiotemporal dynamics in amplifying silicon waveguides
NASA Astrophysics Data System (ADS)
We theoretically investigate the free-carrier-induced spatiotemporal dynamics of continuous waves in silicon waveguides embedded in an amplifying medium. Optical propagation is governed by a cubic Ginzburg-Landau equation coupled with an ordinary differential equation accounting for the free-carrier dynamics. We find that, owing to free-carrier dispersion, continuous waves are modulationally unstable in both anomalous and normal dispersion regimes and chaotically generate unstable accelerating pulses.
Roy, Samudra; Marini, Andrea; Biancalana, Fabio
Considering Correlation between Variables to Improve Spatiotemporal Forecasting
The importance of forecasting cannot be overemphasized in modern environment surveillance applications, including flood control,\\u000a rainfall analysis, pollution study, nuclear leakage prevention and so on. That is why we proposed STIFF (SpatioTemporal Integrated\\u000a Forecasting Framework) in previous work [11], trying to answer such a challenging problem of doing forecasting in natural environment with both spatial and temporal\\u000a characteristics involved. However,
Zhigang Li; Liangang Liu; Margaret H. Dunham
Measuring the spatiotemporal field of ultrashort Bessel-X pulses.
We present direct measurements of the spatiotemporal electric field of an ultrashort Bessel-X pulse generated using a conical lens (axicon). These measurements were made using the linear-optical interferometric technique SEA TADPOLE, which has micrometer spatial resolution and femtosecond temporal resolution. From our measurements, both the superluminal velocity of the Bessel pulse and the propagation invariance of the central spot are apparent. We verified our measurements with simulations. PMID:19649069
Bowlan, Pamela; Valtna-Lukner, Heli; L�hmus, Madis; Piksarv, Peeter; Saari, Peeter; Trebino, Rick
Spatiotemporal characteristics of interannual temperature variations in the Tsushima Strait
Spatiotemporal characteristics of interannual temperature variations in the Tsushima Strait are investigated on the basis\\u000a of historical hydrographic data applying the same procedures as Senjyu et al. (2006). Empirical orthogonal function (EOF) analysis revealed that the most energetic mode of variation (the EOF first mode),\\u000a which accounts for about 31.5% of the total variance, is the in-phase temperature change for
Tomoharu Senjyu; Sigeaki Matsui; In-Seong Han
A spatiotemporal decomposition of a fully inhomogeneous turbulent flow field
NASA Astrophysics Data System (ADS)
The three-dimensional orthogonal spatial modes and their temporal counterparts have been extracted from a large-eddy simulation of turbulent flow over a surface-mounted cube, using a space-time symmetric version of proper orthogonal decomposition (POD), proposed by Aubry et al. (1991). A relatively small domain of interest, located immediately above the top face of the flow obstacle, has been selected for the application of POD. Within that volume of interest, time records of the velocity field have been sampled at 6000 locations simultaneously. The space-time duality of POD can be demonstrated by deriving two alternative eigenvalue problems for either the orthogonal spatial modes or the orthogonal temporal modes. For a particular case, the choice between the two alternatives can be done on the basis of computational convenience and of data-storage requirements. The results show that the first spatiotemporal mode can be identified with the mean flow. The second spatiotemporal mode is dominated by the alternating vortex shedding from the side edges of the flow obstacle. A Fourier analysis of the second temporal mode leads to a Strouhal number of S=0.125 which corresponds to the measured Strouhal number for the vortex shedding (Martinuzzi, 1992). The third and the fourth spatiotemporal modes are connected with the rolls created at the horizontal leading edge of the cube. For the flow field investigated, the dual space-time point of view of POD is rather realistic in the sense that the first four spatiotemporal modes can actually be observed in the flow.
Manhart, M.; Wengle, H.
Spatiotemporal control over growth factor signaling for therapeutic neovascularization ?
Many of the qualitative roles of growth factors involved in neovascularization have been delineated, but it is unclear yet from an engineering perspective how to use these factors as therapies. We propose that an approach that integrates quantitative spatiotemporal measurements of growth factor signaling using 3-D in vitro and in vivo models, mathematic modeling of factor tissue distribution, and new delivery technologies may provide an opportunity to engineer neovascularization on demand.
Cao, Lan; Mooney, David J.
Spatiotemporal chaos from a continuous Na/sub 2/ laser
We report on the observation of spatiotemporal chaos in a multimode continuous Na/sub 2/ ring laser optically pumped by a single-mode Ar/sup +/ laser. Measurements of the intensity of the emitted beam at two different points of the beam cross section reveal different temporal behavior of those two signals. Power spectra, fractal dimensions, and correlations indicate that both signals are chaotic and are generated by a common attractor.
Klische, W.; Weiss, C.O.; Wellegehausen, B.
Spatiotemporal Fusion Framework for Multi-camera Face Orientation Analysis
In this paper, we propose a collaborative technique for face orientation estimation in smart camera networks. The proposed\\u000a spatiotemporal feature fusion analysis is based on active collaboration between the cameras in data fusion and decision making\\u000a using features extracted by each camera. First, a head strip mapping method is proposed based on a Markov model and a Viterbi-like\\u000a algorithm to
Chung-ching Chang; Hamid K. Aghajan
Spatiotemporal cGMP dynamics in living mouse rods.
Signaling of single photons in rod photoreceptors decreases the concentration of the second messenger, cyclic GMP (cGMP), causing closure of cGMP-sensitive channels located in the plasma membrane. Whether the spatiotemporal profiles of the fall in cGMP are narrow and deep, or broad and shallow, has important consequences for the amplification and the fidelity of signaling. The factors that determine the cGMP profiles include the diffusion coefficient for cGMP, the spontaneous rate of cGMP hydrolysis, and the rate of cGMP synthesis, which is powerfully regulated by calcium feedback mechanisms. Here, using suction electrodes to record light-dependent changes in cGMP-activated current in living mouse rods lacking calcium feedback, we have determined the rate constant of spontaneous cGMP hydrolysis and the longitudinal cGMP diffusion coefficient. These measurements result in a fully constrained spatiotemporal model of phototransduction, which we used to determine the effect of feedback to cGMP synthesis in spatially constricting the fall of cGMP during the single-photon response of normal rods. We find that the spatiotemporal cGMP profiles during the single-photon response are optimized for maximal amplification and preservation of signal linearity, effectively operating within an axial signaling domain of ~2 ?m. PMID:22768933
Gross, Owen P; Pugh, Edward N; Burns, Marie E
Video event detection: from subvolume localization to spatiotemporal path search.
Although sliding window-based approaches have been quite successful in detecting objects in images, it is not a trivial problem to extend them to detecting events in videos. We propose to search for spatiotemporal paths for video event detection. This new formulation can accurately detect and locate video events in cluttered and crowded scenes, and is robust to camera motions. It can also well handle the scale, shape, and intraclass variations of the event. Compared to event detection using spatiotemporal sliding windows, the spatiotemporal paths correspond to the event trajectories in the video space, thus can better handle events composed by moving objects. We prove that the proposed search algorithm can achieve the global optimal solution with the lowest complexity. Experiments are conducted on realistic video data sets with different event detection tasks, such as anomaly event detection, walking person detection, and running detection. Our proposed method is compatible with different types of video features or object detectors and robust to false and missed local detections. It significantly improves the overall detection and localization accuracy over the state-of-the-art methods. PMID:24356358
Tran, Du; Yuan, Junsong; Forsyth, David
Sniffing and Spatiotemporal Coding in Olfaction
The act of sniffing increases the air velocity and changes the duration of air flow in the nose. It is not yet clear how these changes interact with the intrinsic timing within the olfactory bulb, but this is a matter of current research activity. An action of sniffing in generating a high velocity that alters the sorption of odorants onto the lining of the nasal cavity is expected from the established work on odorant properties and sorption in the frog nose. Recent work indicates that the receptor properties in the olfactory epithelium and olfactory bulb are correlated with the receptor gene expression zones. The responses in both the epithelium and the olfactory bulb are predictable to a considerable extent by the hydrophobicity of odorants. Furthermore receptor expression in both that rodent and salamander nose interacts with the shapes of the nasal cavity to place the receptor sensitivity to odorants in optimal places according to the aerodynamic properties of the nose.
Scott, John W.
Clifford algebra-based spatio-temporal modelling and analysis for complex geo-simulation data
NASA Astrophysics Data System (ADS)
The spatio-temporal data simulating Ice-Land-Ocean interaction of Antarctic are used to demonstrate the Clifford algebra-based data model construction, spatio-temporal query and data analysis. The results suggest that Clifford algebra provides a powerful mathematical tool for the whole modelling and analysis chains for complex geo-simulation data. It can also help implement spatio-temporal analysis algorithms more clearly and simply.
Luo, Wen; Yu, Zhaoyuan; Hu, Yong; Yuan, Linwang
State Space Neural Networks for Freeway Travel Time Prediction
The highly non-linear characteristics of the freeway travel time prediction problem require a modeling approach that is capable\\u000a of dealing with complex non-linear spatio-temporal relationships between the observable traffic quantities. Based on a state-space\\u000a formulation of the travel time prediction problem, we derived a recurrent state-space neural network (SSNN) topology. The\\u000a SSNN model is capable of accurately predicting experienced travel
Hans Van Lint; Serge P. Hoogendoorn; Henk J. Van Zuylen
Forecasting Hotspots - A Predictive Analytics Approach.
In spatiotemporal data, analysts are searching for regions of space and time with unusually high incidences of events (hotspots). In the cases that hotspots are found, analysts would like to predict how these regions may grow in order to plan resource allocation and preventative measures. Furthermore, analysts would also like to predict where future hotspots may occur. To facilitate such forecasting, we have created a predictive visual analytics toolkit that provides analysts with linked spatiotemporal and statistical analytic views. Our system models spatiotemporal events through the combination of kernel density estimation for event distribution and seasonal trend decomposition by loess smoothing for temporal predictions. We provide analysts with estimates of error in our modeling, along with spatial and temporal alerts to indicate the occurrence of statistically significant hotspots. Spatial data is distributed based on a modeling of previous event locations, thereby maintaining a temporal coherence with past events. Such tools allow analysts to perform real-time hypothesis testing, plan intervention strategies, and allocate resources to correspond to perceived threats. PMID:20498509
Maciejewski, Ross; Hafen, Ryan; Rudolph, Stephen; Larew, Stephen G; Mitchell, Michael A; Cleveland, William S; Ebert, David S
Bayesian spatio-temporal discard model in a demersal trawl fishery
NASA Astrophysics Data System (ADS)
Spatial management of discards has recently been proposed as a useful tool for the protection of juveniles, by reducing discard rates and can be used as a buffer against management errors and recruitment failure. In this study Bayesian hierarchical spatial models have been used to analyze about 440 trawl fishing operations of two different metiers, sampled between 2009 and 2012, in order to improve our understanding of factors that influence the quantity of discards and to identify their spatio-temporal distribution in the study area. Our analysis showed that the relative importance of each variable was different for each metier, with a few similarities. In particular, the random vessel effect and seasonal variability were identified as main driving variables for both metiers. Predictive maps of the abundance of discards and maps of the posterior mean of the spatial component show several hot spots with high discard concentration for each metier. We argue how the seasonal/spatial effects, and the knowledge about the factors influential to discarding, could potentially be exploited as potential mitigation measures for future fisheries management strategies. However, misidentification of hotspots and uncertain predictions can culminate in inappropriate mitigation practices which can sometimes be irreversible. The proposed Bayesian spatial method overcomes these issues, since it offers a unified approach which allows the incorporation of spatial random-effect terms, spatial correlation of the variables and the uncertainty of the parameters in the modeling process, resulting in a better quantification of the uncertainty and accurate predictions.
Grazia Pennino, M.; Mu�oz, Facundo; Conesa, David; L�pez-Qu�lez, Antonio; Bellido, Jos� M.
Exploring off-targets and off-systems for adverse drug reactions via chemical-protein interactome--clozapine-induced agranulocytosis as a case study.
In the era of personalized medical practice, understanding the genetic basis of patient-specific adverse drug reaction (ADR) is a major challenge. Clozapine provides effective treatments for schizophrenia but its usage is limited because of life-threatening agranulocytosis. A recent high impact study showed the necessity of moving clozapine to a first line drug, thus identifying the biomarkers for drug-induced agranulocytosis has become important. Here we report a methodology termed as antithesis chemical-protein interactome (CPI), which utilizes the docking method to mimic the differences in the drug-protein interactions across a panel of human proteins. Using this method, we identified HSPA1A, a known susceptibility gene for CIA, to be the off-target of clozapine. Furthermore, the mRNA expression of HSPA1A-related genes (off-target associated systems) was also found to be differentially expressed in clozapine treated leukemia cell line. Apart from identifying the CIA causal genes we identified several novel candidate genes which could be responsible for agranulocytosis. Proteins related to reactive oxygen clearance system, such as oxidoreductases and glutathione metabolite enzymes, were significantly enriched in the antithesis CPI. This methodology conducted a multi-dimensional analysis of drugs' perturbation to the biological system, investigating both the off-targets and the associated off-systems to explore the molecular basis of an adverse event or the new uses for old drugs. PMID:21483481
Yang, Lun; Wang, Kejian; Chen, Jian; Jegga, Anil G; Luo, Heng; Shi, Leming; Wan, Chunling; Guo, Xizhi; Qin, Shengying; He, Guang; Feng, Guoyin; He, Lin
Interactome mapping of the phosphatidylinositol 3-kinase-mammalian target of rapamycin pathway identifies deformed epidermal autoregulatory factor-1 as a new glycogen synthase kinase-3 interactor.
The phosphatidylinositol 3-kinase-mammalian target of rapamycin (PI3K-mTOR) pathway plays pivotal roles in cell survival, growth, and proliferation downstream of growth factors. Its perturbations are associated with cancer progression, type 2 diabetes, and neurological disorders. To better understand the mechanisms of action and regulation of this pathway, we initiated a large scale yeast two-hybrid screen for 33 components of the PI3K-mTOR pathway. Identification of 67 new interactions was followed by validation by co-affinity purification and exhaustive literature curation of existing information. We provide a nearly complete, functionally annotated interactome of 802 interactions for the PI3K-mTOR pathway. Our screen revealed a predominant place for glycogen synthase kinase-3 (GSK3) A and B and the AMP-activated protein kinase. In particular, we identified the deformed epidermal autoregulatory factor-1 (DEAF1) transcription factor as an interactor and in vitro substrate of GSK3A and GSK3B. Moreover, GSK3 inhibitors increased DEAF1 transcriptional activity on the 5-HT1A serotonin receptor promoter. We propose that DEAF1 may represent a therapeutic target of lithium and other GSK3 inhibitors used in bipolar disease and depression. PMID:20368287
Pilot-Storck, Fanny; Chopin, Emilie; Rual, Jean-Fran�ois; Baudot, Anais; Dobrokhotov, Pavel; Robinson-Rechavi, Marc; Brun, Christine; Cusick, Michael E; Hill, David E; Schaeffer, Laurent; Vidal, Marc; Goillot, Evelyne
Interactome Mapping of the Phosphatidylinositol 3-Kinase-Mammalian Target of Rapamycin Pathway Identifies Deformed Epidermal Autoregulatory Factor-1 as a New Glycogen Synthase Kinase-3 Interactor*
The phosphatidylinositol 3-kinase-mammalian target of rapamycin (PI3K-mTOR) pathway plays pivotal roles in cell survival, growth, and proliferation downstream of growth factors. Its perturbations are associated with cancer progression, type 2 diabetes, and neurological disorders. To better understand the mechanisms of action and regulation of this pathway, we initiated a large scale yeast two-hybrid screen for 33 components of the PI3K-mTOR pathway. Identification of 67 new interactions was followed by validation by co-affinity purification and exhaustive literature curation of existing information. We provide a nearly complete, functionally annotated interactome of 802 interactions for the PI3K-mTOR pathway. Our screen revealed a predominant place for glycogen synthase kinase-3 (GSK3) A and B and the AMP-activated protein kinase. In particular, we identified the deformed epidermal autoregulatory factor-1 (DEAF1) transcription factor as an interactor and in vitro substrate of GSK3A and GSK3B. Moreover, GSK3 inhibitors increased DEAF1 transcriptional activity on the 5-HT1A serotonin receptor promoter. We propose that DEAF1 may represent a therapeutic target of lithium and other GSK3 inhibitors used in bipolar disease and depression.
Pilot-Storck, Fanny; Chopin, Emilie; Rual, Jean-Francois; Baudot, Anais; Dobrokhotov, Pavel; Robinson-Rechavi, Marc; Brun, Christine; Cusick, Michael E.; Hill, David E.; Schaeffer, Laurent; Vidal, Marc; Goillot, Evelyne
SRC Homology 2 Domain Binding Sites in Insulin, IGF-1 and FGF receptor mediated signaling networks reveal an extensive potential interactome
Specific peptide ligand recognition by modular interaction domains is essential for the fidelity of information flow through the signal transduction networks that control cell behavior in response to extrinsic and intrinsic stimuli. Src homology 2 (SH2) domains recognize distinct phosphotyrosine peptide motifs, but the specific sites that are phosphorylated and the complement of available SH2 domains varies considerably in individual cell types. Such differences are the basis for a wide range of available protein interaction microstates from which signaling can evolve in highly divergent ways. This underlying complexity suggests the need to broadly map the signaling potential of systems as a prerequisite for understanding signaling in specific cell types as well as various pathologies that involve signal transduction such as cancer, developmental defects and metabolic disorders. This report describes interactions between SH2 domains and potential binding partners that comprise initial signaling downstream of activated fibroblast growth factor (FGF), insulin (Ins), and insulin-like growth factor-1 (IGF-1) receptors. A panel of 50 SH2 domains screened against a set of 192 phosphotyrosine peptides defines an extensive potential interactome while demonstrating the selectivity of individual SH2 domains. The interactions described confirm virtually all previously reported associations while describing a large set of potential novel interactions that imply additional complexity in the signaling networks initiated from activated receptors. This study of pTyr ligand binding by SH2 domains provides valuable insight into the selectivity that underpins complex signaling networks that are assembled using modular protein interaction domains.