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Phoenix DeBuysere
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Fort Pierce
St. Lucie County
Florida, USA
Jan. 6, 2009
Rock Island County
Illinois, USA
We got Phoenix from the Animal Shelter in Fort Pierce as a 5 week old puppy. We had to have our dog Deeogee put to sleep & found her the next day. She had come in only the day before, since we had Deogee creamated, I named her Phoenix for rising from his ashes.My dad lived with us & always called her "FUZZY FOOT" as she was a Chow & they would get fuzzy in between clippings. She thought she was a SMALL LAP DOG. She gave us 16 WONDERFUL YEARS & we will miss her soooooo much. She was put to sleep with me holding her at 4pm January 6 2009. I know that Grandpa was there to greet her & she is probably sitting in his lap now. THANK YOU PHOENIX FOR BEING A GREAT FRIEND. I LOVE YOU & WILL NEVER STOP.
Thank You Bev This Poem meant ALOT
If It Should Be...
If it should be that I grow weak
And pain should keep me from my sleep,
Then you must do what must be done,
For this last battle cannot be won.
You will be sad, I understand.
Don't let your grief then stay your hand.
For this day, more than all the rest,
Your love for me must stand the test.
We've had so many happy years.
What is to come can hold no fears.
You'd not want me to suffer so;
The time has come -- please let me go.
Take me where my need they'll tend,
And please stay with me till the end.
Hold me firm and speak to me,
Until my eyes no longer see.
I know in time that you will see
The kindness that you did for me.
Although my tail its last has waved,
From pain and suffering I've been saved.
Please do not grieve -- it must be you
Who had this painful thing to do.
We've been so close, we two, these years;
Don't let your heart hold back its tears.
~ Anonymous ~
Cremated, Ashes given to family or friend.
Specifically: Ashes given to Mom & Daddy
Created by:
Charlotte Kramer-DeBuys...
Record added: Jan 06, 2009
Find A Grave Memorial# 32691630
Added by:
Charlotte Kramer-DeBuysere
Photos may be scaled.
Click on image for full size.
With Love For Good Dog Phoenix ♥.....
Added: Jan. 6, 2016
Added: Jan. 6, 2015
Always Loved And Remembered ♥.....
Added: Jan. 6, 2015
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Commercial trap fishermen are hoping a strong stone crab season will help make up for a thus-far lousy lobster season.
Fishermen are saying lobster season, which started in August, has been less than stellar so far this year, with low prices and a dismal catch. Trap fishermen began soaking their stone crab traps Friday, and will begin pulling them and harvesting claws on Monday.
"We are all hoping, because lobster season has been horrible," said Lower Keys stone crab fisherman George Nile. "We really need stone crab season to pick up and make up for a poor lobster season. Last year was a really good year [for stone crab]."
In fact, the past two seasons have gone well. The 2011-2012 season ended with fishermen earning $14 for jumbo crab claws, $11 for large claws and $5 for mediums to smalls. Consumers usually pay a little less than twice that at the seafood counter.
Some lobster fishermen are reporting stone crabs beginning to show up in their lobster pots, but caution that it is too early to tell if that is a good omen.
"There were a lot of crabs all year round," said Conch Key fisherman Gary Nichols. "After [Tropical Storm Issac], the traps were loaded with stone crabs. Once we cleared them out of the traps, they didn't come back. Neither did the lobsters."
The demand for stone crab generally is at its peak in the first week of the season, as Florida Keys residents and tourists await the arrival of claws in local seafood markets and restaurants. It tapers off in late October, but demand picks up again over the Thanksgiving and Christmas holidays.
Sean Seaman, owner of Eaton Street Seafood Market, has to order claws in advance, said Nichols.
"It will be a late day and night on that first day," Seaman said. "I will have to cook a lot of crabs those first couple days."
The Florida Fish and Wildlife Conservation Commission closes the season May 16 to protect the crabs during the peak spawning season. Stone crab claws can be possessed and sold the during the closed season if they have been harvested and entered into a fish house's inventory by a licensed wholesale or retail dealer prior to May 16.
Keys fishermen harvest, on average, about 2 million pounds of claws, or about 65 percent of all the stone crab claws taken statewide, according to Florida Keys Commercial Fishermen's Association Executive Director Bill Kelly.

subset: commoncrawl


text:
Most actors, of late, choose to perform their own stunts and end up getting injured while shooting on the sets. The latest addition to the list is actress Deepika Padukone. She, apparently, was injured on sets while shooting for a dance number alongside Ranveer Singh in Sanjay Leela Bhansali's film Ram Leela.
Deepika was performing some dance steps with Ranveer on a rough surface, which left her with severe swelling and red gashes. Besides, Ranveer's character also sported some heavy accessories and piercings which added to the damage. However, despite being injured, Deepika took it in her stride and continued filming the scene after taking some rest.


text:
The Notch signaling pathway controls numerous cell fate decisions during development and adulthood through diverse mechanisms. Thus, whereas it functions as an oscillator during somitogenesis, it can mediate an all-or-none cell fate switch to influence pattern formation in various tissues during development. Furthermore, while in some contexts continuous Notch signaling is required, in others a transient Notch signal is sufficient to influence cell fate decisions. However, the signaling mechanisms that underlie these diverse behaviors in different cellular contexts have not been understood. Notch1 along with two downstream transcription factors hes1 and RBP-Jk forms an intricate network of positive and negative feedback loops, and we have implemented a systems biology approach to computationally study this gene regulation network. Our results indicate that the system exhibits bistability and is capable of switching states at a critical level of Notch signaling initiated by its ligand Delta in a particular range of parameter values. In this mode, transient activation of Delta is also capable of inducing prolonged high expression of Hes1, mimicking the "ON" state depending on the intensity and duration of the signal. Furthermore, this system is highly sensitive to certain model parameters and can transition from functioning as a bistable switch to an oscillator by tuning a single parameter value. This parameter, the transcriptional repression constant of hes1, can thus qualitatively govern the behavior of the signaling network. In addition, we find that the system is able to dampen and reduce the effects of biological noise that arise from stochastic effects in gene expression for systems that respond quickly to Notch signaling.
This work thus helps our understanding of an important cell fate control system and begins to elucidate how this context dependent signaling system can be modulated in different cellular settings to exhibit entirely different behaviors.
Author Summary
The Notch signaling pathway is an evolutionarily conserved signaling system that is involved in various cell fate decisions, both during development of an organism and during adulthood. While the same core circuit functions in various different cellular contexts, it has experimentally been shown to elicit varied behaviors and responses. On the one hand, it functions as a cellular oscillator critical for somitogenesis, whereas in other situations, it can function as a cell fate switch to pattern developing tissue, for example in the Drosophila eye. Furthermore, malfunctioning of Notch signaling is implicated in various cancers. To better understand the underlying mechanisms that allow the network to function distinctly in different contexts, we have mathematically modeled the behavior of the Notch network, encompassing the Notch gene along with two of its downstream effector transcription factors, which together form a network of positive and negative feedback loops. Our results indicate that the qualitative and quantitative behavior of the system can readily be tuned based on key parameters to reflect its multiple roles. Furthermore, our results provide insights into alterations in the signaling system that lead to malfunction and hence disease, which could be used to identify potential drug targets for therapy.
Citation: Agrawal S, Archer C, Schaffer DV (2009) Computational Models of the Notch Network Elucidate Mechanisms of Context-dependent Signaling. PLoS Comput Biol 5(5): e1000390. https://doi.org/10.1371/journal.pcbi.1000390
Editor: Satoru Miyano, University of Tokyo, Japan
Received: August 21, 2008; Accepted: April 17, 2009; Published: May 22, 2009
Copyright: © 2009 Agrawal et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by the Helen Wills Neuroscience Institute and LDRD 3668DS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Cells continuously receive signals from their microenvironments – including factors present in the extracellular matrix, soluble media, and surrounding cells – which collectively influence cell function and behavior via activating intracellular signal transduction and gene regulation networks. These networks generally involve complex, nonlinear interactions of proteins, such as phosphorylation cascades (reviewed in [1]) and second messenger signaling systems [2], whose structures feature positive and negative feedback loops, feed-forward interactions, signal amplification, and cross-talk with other pathways [3]. Mathematical models of these interactions are therefore very insightful or even necessary avenues to analyze and understand the regulation of cell behavior, as the properties of these networks can exceed an intuitive understanding [4]–[6].
Notch is a signaling system required for numerous critical cell fate specification events during the development of the nervous system, hematopoietic system, eye, and skin [7]–[11]. The receptor for this pathway is the single pass transmembrane protein Notch that, when bound by its ligands Delta or Jagged, undergoes a series of cleavage events to release its intracellular domain (NICD) [9],[12]. This NICD then translocates into the nucleus and acts as a transcriptional upregulator of target genes, including members of the hes family, through its interaction with the transcription factor RBP-Jκ [13]. In mammals there are four different Notch proteins (Notch1-4) and 5 ligands (Delta 1, 3, and 4 and Jagged 1 and 2). For this study, we have focused primarily on the Notch1 signaling pathway.
In its role as a critical regulator of cell fate [7]–[11], Notch has been known to function via lateral inhibition and induction mechanisms to create fine-grained patterns in undifferentiated cells, a process required for proper boundary formation and differentiation of various tissues [14],[15]. It can also function as a binary cell fate switch, for example during differentiation of the epidermis [16] and endodermal epithelium of the gut [17], to promote differentiation of one cell type from precursor cells at the expense of another. Furthermore, in some cases continuous Notch activity is not required for cell fate specification. For example, transient Delta-Notch signaling has been shown to be sufficient to induce T-cell [18] and NK cell differentiation [19] from their respective precursor cells, and can induce an irreversible switch to gliogenesis in neural crest stem cells [20]. Notch signaling also occurs only transiently in many instances during the development of Drosophila [21], zebrafish [22],[23], and mice [24]. It was also recently shown that human embryonic stem cells (hESCs) require activation of Notch signaling to form the progeny of all three embryonic germ layers, and subsequent transient Notch signaling enhanced generation of hematopoietic cells from committed hESCs [25]. The mechanisms by which a short Notch signaling pulse can permanently switch cell fate are not elucidated.
The Notch system has also been shown to function as an oscillator. Specifically, the expression levels of members of the hes family, a group of downstream Notch target genes [26], have been shown to oscillate with a 2 hour periodicity in some systems during development, which for example aids in somitogenesis (i.e. the patterning of somites) [27]–[29]. Hes1 protein and mRNA concentrations have also been observed to oscillate with an approximate 2 hr time period upon serum starvation in various cultured cell lines including myoblasts, fibroblasts, and neuroblastoma cells [30]. Furthermore, oscillations in the Notch network have been proposed to be important in maintaining neural progenitor cells in an undifferentiated state [31]. Finally, there is evidence that such oscillations may also afford cells the opportunity to repeatedly test for the continued existence of a signal [32], thereby increasing cellular response sensitivity and flexibility by allowing the cell to integrate the results of many periodical evaluations of the signal before making an ultimate cell fate decision.
The Delta-Notch signaling system has been previously modeled to elucidate its role in fine-grained pattern formation through the action of lateral inhibition and induction [33]–[35]. Collier et al. developed a simple 2-parameter model that focuses on pattern formation due to feedback inhibition between adjacent cells via Delta-Notch signaling [33]. Other models build upon this simple model by adding more molecular detail at the intercellular level [34],[35]. In addition, several studies have focused on trying to understand the underlying mechanism of Notch system oscillations [32],[36], where a Hes1 negative feedback loop composed of Hes1 protein repressing hes1 transcription, likely plays a central role [37]. Delays related to transcription and translation were also proposed to be important for the observed oscillations [38]. However, while several models have thus been proposed and have yielded important insights into this system [30], [36], [38]–[40], they have focused exclusively on Hes1 and not analyzed its interactions with other signaling proteins in the Notch system. Additionally, all these models focus on a particular aspect or mode of Notch signaling (e.g. lateral inhibition or oscillation) but do not yet address how complex, alternative behaviors could arise from the same network.
Here we mathematically model the Notch signaling system to analyze how the same network is capable of functioning as a cell fate switch or an oscillator in different biological contexts. This model, which includes the regulation of the notch1-RBP-Jk-hes1 gene circuit, predicts that the Notch1-Hes1 system acts as a bistable switch in certain regions of parameter space, where Hes1 levels can change by 1–2 orders of magnitude as a function of the input Delta signal. In addition, it predicts that a transient pulse of a high level of Delta is capable of inducing high Hes1 expression levels for a duration that would be sufficient to induce a cell fate switch. Moreover, the model elucidates how the network can be 'tuned' to function in different regimes, either as an oscillator or a cell fate switch, by changing a key parameter. Finally, low numbers of reactants can lead to significant statistical fluctuations in molecule numbers and reaction rates, making cells intrinsically noisy biochemical reactors [41],[42]. Stochastic simulations of the Notch system, which enable the analysis of the effect of biological noise in the system arising due to stochastic variations in gene expression, reveal that for systems that respond quickly to Notch signaling, the network is able to dampen the effects of this biological noise and function in a manner similar to what is predicted by the deterministic model. In summary, the model enables analysis of the different behavioral responses of the Notch signaling network observed over a broad spectrum of signaling inputs and parameter values and can be further expanded to study Notch signaling in numerous contexts.
We developed a model of Notch signaling to investigate how this system can function as either an oscillator or as a simple binary switch capable of responding to steady state or transient inputs. Brief experimental work revealed that the notch1 promoter is positively upregulated by its gene product and is downregulated by Hes1 (Text S1, Fig. S1). We thus examined the behavior of the notch1, RBP-Jκ, and hes1 genes, which form a complex set of regulatory feedback loops (Fig. 1). A deterministic model composed of a system of differential equations was developed to analyze dynamic changes in the levels of the network constituents. However, since the concentrations of some of the species were low, stochastic simulations were also conducted to examine whether noise in the levels of the network components could significantly impact system behavior, as noise has the potential to undermine the fidelity of cell fate choices [41],[43].
(A) Each arrow represents a term or event in the differential equation model including transcription, translation, mRNA and protein degradation, nuclear import, TF binding, receptor-ligand binding and receptor processing. (B) Schematic of the positive and negative feedback loops of the Notch1-RBP-Jk-Hes1 network. (-|) represents repression and (->) represents activation of target genes.
Deterministic Model Development
A set of differential equations was developed to track changes in the concentrations of various species in the nucleus and cytoplasm of a cell as a function of time following activation of Notch by its ligand. The cell is modeled as a 10 µm diameter sphere with a 5 µm diameter nucleus. Numerous processes were modeled as terms in the differential equation system, including transcription, translation, transport, degradation or - in the case of Notch - receptor cleavage (Fig. 1A, Text S1). As examples, the three equations tracking the Hes1 cytoplasmic mRNA, Hes1 cytoplasmic protein and Hes1 nuclear protein concentrations are given by:The rate of change (in units of ) of the cytoplasmic mRNA concentration of Hes1 is given by the difference in the rates of it transcription and degradation. RfHcm is the transcription rate of hes1 mRNA in the nucleus. We assume instantaneous export of mRNA to the cytoplasm. A factor of 7 is included to take into account the dilution due to export to the cytoplasm (Text S1). kdHcm, kdHcp, and kdHnp denote the degradation constants for the hes1 mRNA (Hcm), cytoplasmic protein (Hcp), and nuclear protein (Hnp), respectively, which are assumed to undergo first order degradation kinetics. ktrHc denotes the translation constant (min−1) for conversion of cytoplasmic hes1 mRNA into cytoplasmic protein. Transcriptional and translational delay times are incorporated into the model, as these are processes that inherently involve delays between initiation and the production of a molecule of mRNA or protein, as previously described [38],[44]. Thus, the translation of Hes1 protein is based on the delayed hes1 mRNA concentration HcmD (delayed by time TpHc, the average time for translation of Hes1), which is the concentration of mRNA present when the process of translation was initiated instead of the concentration at the present time. kniHcp denotes the nuclear import rate in units of min−1. A dilution factor of 7 is again used to incorporate differences in nuclear and cytoplasmic volumes.
The hes1 Promoter
The transcription rates for notch1, hes1, and RBP-Jκ are based on the states of their respective promoters. Previous promoter analysis has been complemented with Genomatix Suite Gene2Promoter transcription factor (TF) binding site prediction software to identify potential TF binding sites in the promoters of the three genes in the model.
Takebayashi et al. [37] observed that hes1 transcription is repressed by its own gene product through Hes1 protein binding to sites in the hes1 promoter termed N-boxes. Through a series of binding and transcriptional activity assays, the study determined that Hes1 bound strongly to three N-boxes found upstream of the transcriptional start site and repressed transcription of the hes1 mRNA up to 40-fold. Also, while the work concluded that there was a synergistic rather than an additive effect of the N-box binding dependent repression of gene expression, further mathematical analysis has indicated that there is no or very weak synergy among the different binding sites [45]. Several positive regulatory regions were also found in the hes1 promoter, and it was also shown to have two adjacent RBP-Jκ binding sites [46],[47]. Thus, we have modeled the hes1 promoter to have three equivalent N-boxes where the Hes1 protein can bind and repress transcription, as well as two equivalent RBP-Jκ sites. The presence of all other positive regulators of transcription is lumped into a constant basal rate of transcription.
The notch1 Promoter
As it has not been extensively investigated, the notch1 promoter sequence was analyzed in the Gene2Promoter software. One putative Hes1 site (N-box) and two putative RBP-Jκ sites were found in the ∼1 kb notch1 promoter analyzed. This may imply that notch1 is both positively and negatively regulated by its own gene product. To test this, a transcriptional activity experiment using the dual luciferase assay system was conducted. The promoter of murine notch1 [48] was used to drive expression of hRluc cDNA (Renilla luciferase). Co-transfection studies with plasmids expressing RBP-Jκ, NICD, Hes1, and dNHes1 (a dominant negative form of Hes1) indeed demonstrated that the notch1 promoter is regulated negatively by Hes1 and RBP-Jκ in the absence of NICD but is positively regulated by NICD in the presence of RBP-Jκ (Text S1, Fig. S1). The notch1 promoter was modeled with two RBP-Jκ sites and one N-box.
The RBP-Jκ Promoter
A 418 bp sequence upstream of the RBP-Jκ gene as characterized by Amakawa et al. [49] was analyzed in the Gene2Promoter software for TF binding sites of interest. Three potential Hes1 binding sites and three potential RBP-Jκ sites were found. Thus, the RBP-Jκ gene also potentially undergoes autoregulation under Notch signaling, and a three N-box, three RBP-Jκ site model was utilized.
Modeling the Transcription Term
As discussed above, all three promoters have one or more binding sites for both Hes1 (the N-box) and RBP-Jκ. It is assumed that Hes1 can bind and repress transcription of the corresponding promoter only in its homodimer form, and the dimerization reaction is assumed to be at steady state over timescales of protein transcription, translation and import, driven by mass action kinetics such that the concentration of the dimer is given by:Where, KaHp is the association equilibrium constant for the dimerization reaction. Similarly, the time scales of transcription factor binding to and dissociation from the promoter elements are also assumed to be much faster than those of gene transcription and protein synthesis, such that binding to the promoter is at pseudo steady state. In addition, it is assumed that NICD can bind only when an RBP-Jk protein is bound to its site on the promoter, and that this NICD binding converts RBP-Jκ from a transcriptional repressor to an activator [13].
The level of promoter activation (i.e. rate of mRNA synthesis) is modeled by an approach termed BEWARE [50],[51], in which the probabilities of a promoter being in any one of its many possible states are calculated based on the relative concentrations of the three transcription factors (Hes1, RBP-Jκ and NICD), and their respective DNA binding affinities, using equilibrium binding equations. The level of activation of the promoter is then given by: , where, P[Pi] is the probability of the promoter being in state i, and vi is the activation rate of gene transcription associated to the promoter being in that state i. When the promoter is empty, the gene activation rate is assumed to be the basal transcription rate (Vb) for that promoter. When a Hes1 dimer is bound to an N-box, the rate is reduced by a factor rN that takes into account the repressive effect of the Hes1 transcription factor, and when RBP-Jκ is bound, the rate is reduced by a factor rR. Furthermore, when the promoter is in its maximally activated state with the NICD bound to the RBP-Jκ and no Hes1 dimers bound, the activation rate is assumed to be at its maximum and is given by (Vmax+Vb). In the case of multiple RBP-Jκ binding sites, an additional factor tc (<1) is used to account for states where not all RBP-Jκ sites bind NICD to represent the decrease from the maximum possible activation rate. For a detailed expression of transcription rates please refer to Supplemental Materials (Text S1).
Although explicit parameters have been included to account for cooperative binding for Hes1 dimers to multiple N-boxes and for RBP-Jκ binding (cooperativity factors Cn, Cr and Cnr - please refer to Table 1 for model parameters), they have been set to 1 for these simulations, as recent work suggests there is very little if any cooperative effect in Hes1 binding to N-boxes [45]. Finally, it is assumed that each mRNA produces a fixed number of proteins, i.e. mRNA dynamics have been neglected [50].
Parameter Determination
Experimentally determined values for half-lives of proteins and mRNA, association and dissociation constants of proteins to their respective DNA binding sites, dimerization constants, and protein translation and transcription rates have been used when possible (Table 1). These values are often not available for the exact species of interest; however, the best available estimates based on similar protein classes are used wherever applicable as the starting point. The time delays for transcription and translation for each of the three genes are calculated as previously described [52] and are detailed in the Supplemental Materials (Text S1). 4.5 transcripts per minute [45] and 20 transcripts per minute [53] were used as initial estimates for hes1 basal and maximum transcription rates respectively. The transcription rates for RBP-Jκ and notch1 were then determined from these estimates and the estimates of their minimum transcription times (Text S1).
The degradation rates for the Hes1 protein and mRNA were determined experimentally by Hirata et al. in fibroblasts [30]. They observed similar values in other cultured cell types including myoblasts, neuroblastomas, and teratocarcinomas. Pulse chase experiments of Logeat et al. [54] were used to assess the degradation rates for the full-length Notch1 protein, and an estimate of Notch1 protein half-life of ∼40 minutes was derived.
GSK3β has been shown to affect the stability of NICD [55]. Although there are conflicting results as to whether GSK3β helps to stabilize [55] or destabilize the cleaved NICD [56], our experimental results show that GSK3β is essential for the NICD regulation of neural stem cell differentiation into astrocytes (Agrawal, Ngai, and Schaffer, manuscript in preparation). Furthermore, we show that Notch1 signaling upregulates the expression of GSK3β in these cells. Thus, the effect of GSK3β is incorporated into the model by increasing the half-life of NICD from 3 to 8 hrs [55] above a threshold concentration of Hes1 (which is assumed to directly or indirectly regulate the expression of GSK3β). This increased NICD half-life does not however change the qualitative behavior of the Hes1 switch (Fig. S3A).
The repression constant of Hes1 dimer bound to an N-box (rNbox) is estimated from the results of Takebayashi et al. [37] that show that in the presence of three N-boxes, transcription is repressed by ∼40 fold. This yields a repression value of ∼0.3 per N-box (Please refer to Supplemental Materials (Text S1) for details). Since there are no reliable estimates of the NICD generation constant upon Delta binding (kfNcp), a lumped parameter of this constant with the Delta concentration is used to report the strength of the Delta signal (kfNcp*Delp). The initial parameters for which the experimentally determined values are not accurately available were later subjected to sensitivity analysis (See results).
Computational Methods and Initial Conditions
The differential equations described in the model were solved (with parameter values given in Table 1) using Berkeley Madonna 8.3.11 software (www.berkeleymadonna.com) with the Runge Kutta 4 module at a step size of 1 min. To arrive at realistic initial conditions for the model, the initial concentrations of all species were set to 0 with zero Delta signal, and the simulations were run until the various species attained steady state concentration levels. These steady state values (listed in Table 2) were then used as the initial conditions for subsequent simulations. For the various experiments, the system was run for 750 minutes without stimulation with the Delta ligand to attain a basal steady state, and the Delta concentration was then increased to different levels to initiate Notch1 signaling. Simulations were run either with a constant Delta signal throughout or with varying duration pulses of the Delta signal. The system was simulated for a duration of 5,000–10,000 minutes (∼3.5–7 days), as neural progenitor stem cells have been previously shown to undergo differentiation upon Notch activation in 3–5 days ([57]). Longer simulations up to 50,000 minutes were conducted when required to confirm Hes1 had reached steady state levels.
Stochastic Model Development
Since the levels of several protein species in the deterministic model simulations were very low (Table 2), at the level of tens of molecules per cell, assumptions of mass action kinetics and pseudo steady state may not hold true, and stochastic effects may play an important role in the dynamics of the signaling network [41],[58]. To analyze whether noise in protein and mRNA concentrations would impact the dynamics of the system, a stochastic simulation of the model using the Gillespie algorithm [43] was implemented in C++ (code available upon request). To relax the assumptions of mass action kinetics and pseudo steady state, we explicitly simulated every reaction step, making a total of 299 reactions. For example, every interaction between a transcription factor and a promoter was modeled as a discrete reaction in the simulation. The τ-leap method [59] was also incorporated into the algorithm to accelerate the stochastic simulations and increase their efficiency.
The Notch1-Hes1 Network as a Bistable Switch
The response of the Notch1-RBP-Jκ-Hes1 system to a step change in an input Delta signal was analyzed. Simulations were initiated using the steady state levels of the different species in the absence of any external Delta (also listed in Table 2), and at t = 750 minutes a Delta signal was applied. Fig. 2A demonstrates that when a low input Delta stimulus is applied, the Hes1 concentration settles to a correspondingly low steady state value. However, when the input Delta signal was increased (10-fold), Hes1 shows a rapid increase to a new, 20-fold higher steady state value. Further steady state analysis at a range of input Delta levels and initial conditions reveals that the system exhibits bistability. At low levels of Delta signal, basal levels of Hes1 are maintained in the cell ("OFF" state), but as the Delta signal strength is increased beyond a threshold level, it stimulates the production of Hes1, which is then maintained at high levels ("ON" state) through the concerted regulation of the Notch1-RBP-Jκ-Hes1 network (Fig. 2B). Bistability – which has previously been proposed as an advantageous mechanism to mediate an unambiguous cell fate switch, including in stem cells [51],[60] – is evident within an intermediate range of Delta signal values (Fig. 2B).
(A) Deterministic Hes1 trajectories as a function of time for two different strengths of Delta signals given as a product of the Delta concentration and the rate constant of formation of NICD upon Delta-Notch binding (kfNcp*Delp = kDelp). These deterministic simulations were initiated using steady state values of the system under no Delta signal and at t = 750 minutes (indicated by vertical arrow) the input Delta signal was applied. (B) Hysteresis in the Notch1-Hes1 network, where Hes1 concentration can attain two possible steady states for an intermediate range of Delta inputs. The point of switching depends on whether the Delta signal is increasing or decreasing.
Network Sensitivity to Biological Noise
The initial numbers of some protein and mRNA species in the system were in the range of tens of molecules per cell (Table 2), such that stochastic fluctuations in individual species may impact the dynamics of the network. In particular, intracellular noise inherent in systems with small numbers of molecules and/or slow biochemical reactions can randomize or undermine the "accuracy" of cell fate choices [41],[58]. To analyze such behavior, stochastic simulations based on the Gillespie algorithm [43], distinct from the deterministic model, were developed. Steady state analysis shows that at low, constant Delta signals, the Hes1 levels fluctuate about a low mean value corresponding to the "OFF" state, as expected (data not shown). However, if the Delta signal is increased to a level just below the concentration at which the deterministic model would predict a switch in state (Fig. 2B), stochastic simulations reveal that noise in the network can induce some trajectories to spontaneously switch states (Fig. 3A). Analogous to results previously observed in other systems [51],[61],[62], noise thus undermines the bistable switch and induces spontaneous flipping between states. Analysis of the time it takes the system to initially pass from the lower to the upper state reveals that as the strength of the input signal is increased, this average first passage time (FPT) decreases, and the percentage of trajectories that change state increases (Fig. 3B). However, this "uncertainty" occurs within a narrow range of intermediate Delta signal levels, and if this intermediate window is avoided, the system effectively behaves deterministically.
(A) Representative stochastic Hes1 trajectories as a function of time after application of a constant Delta stimulus at 750 min at levels just below "ON" levels predicted by the deterministic model. Some Hes1 trajectories remain at low levels ("OFF" state) while others randomly switch state to higher levels ("ON" state). (B) First passage time (FPT) of stochastic trajectories for passage from "OFF" to "ON" state as a function of Delta signal strength in the bistable region. The mean and standard deviation of 40–60 runs in each case are plotted. The percentage of trajectories that switched to "ON" state under the given Delta signal is indicated below each data point. All points except those connected by the same letter (A) are statistically distinct (p<0.01, 2-tail t-test).
In addition, "ON" to "OFF" transitions were simulated by first stimulating with a high Delta signal for 4000 minutes to induce high Hes1 expression levels. When Delta was then reduced to levels that were in the predicted bistable region based on the deterministic model, the system maintained high expression levels of Hes1 (Fig. 4A), as anticipated from the deterministic results (Fig. 2B). Contrary to what was expected based on the deterministic model, however, when the Delta signal was instead reduced to zero, some trajectories remained in the high Hes1 expression ("ON") state (Fig. 4B). This indicates the role of stochastics in potentiating high Hes1 expression levels even in the absence of continued signal.
(A) Hes1 stochastic trajectories are shown during high Delta levels for 4000 minutes, following which the Delta signal is brought down to levels that failed to switch the state to "ON" when provided for a prolonged duration (kDelp = 4×10−4) in the deterministic model. All the trajectories remain in the "ON" state – corresponding to the region of bistability seen in the deterministic simulations. (B) Hes1 stochastic trajectories are shown after application of a high Delta signal for 4000 minutes, after which the Delta signal is brought down to 0. Some trajectories persist in the "ON" state.
Response of the System to Transient Delta Activation
It has been shown for neural crest stem cells [20] that a transient Notch signal is sufficient to induce cell differentiation. Also, there are numerous situations where transient Notch-Delta signaling determines the fates of immature cells, both in tissue culture [18],[19] and during organismal development [21]–[24]. Under continuous Delta stimulation, the system can attain high steady-state Hes1 expression levels, thus acting as a switch, but we next wanted to examine whether transient Delta activation was also capable of eliciting high Hes1 expression. We thus examined the dynamic response of the system to transient activation of the Notch1 pathway upon variation in the strength and duration of an applied Delta signal.
When the system is stimulated for a short duration (10 minutes) with a moderate strength Delta signal, the deterministic model predicts a transient peak in the Hes1 expression that eventually decays to its low steady state value (Fig. 5A). However, the peak expression of Hes1 continually increases with increasing input signal duration up to ∼800 minutes, beyond which the maximum expression levels of Hes1 attained remain the same but the duration of prolonged high expression levels progressively increases (Fig. 5A). Similarly, as the input Delta signal strength is increased for a constant pulse duration, the peak Hes1 concentrations attained also increase up to a maximum value, after which a further increase in the signal strength only increases the duration of high Hes1 levels (Fig. 5B). The cell is thus able to attain high Hes1 expression either under prolonged low intensity Delta signaling or a short burst of high intensity Delta signaling.
(A) Effect of Delta signal duration on the Hes1 expression levels: a transient Delta signal of kfNcp*Delp = 5×10−3 was provided in the deterministic model for varying amounts of time ranging from 10 minutes to 3000 minutes, and the resulting Hes1 trajectories were simulated up to 15000 minutes. (B) The effect of Delta signal strength on Hes1 expression: a transient Delta signal of varying strengths (expressed as kDelp = kfNcp*Delp (min−1)) was provided in the deterministic model for 100 minutes, and the resulting Hes1 were simulated up to 20000 minutes.
Stochastic Effects on Transient Delta Signaling
We also examined the effect of stochastics on transient activation of the network. Simulations were run using the parameter values as in the deterministic model for various Delta pulse durations ranging from 10 minutes to 3000 minutes and >40 trajectories per input duration value were analyzed. For Delta pulse durations of less than 500 minutes, the stochastic simulations followed the prediction of the deterministic model (data not shown). However, for a 500-minute Delta pulse, even though the deterministic model predicts a transient Hes1 peak that does not attain the maximum possible expression level, a small percentage of the stochastic trajectories in fact did switch to the "ON" state (corresponding to high Hes1 expression levels) (data not shown). Also, as the duration of the Delta pulse is increased, the percentage of trajectories that remain in the "ON" state for the simulated 15,000 minutes progressively increases even though the deterministic model predicts that the system would revert back to the "OFF" state within that time. Furthermore, the average first passage time (FPT) of the trajectories that do switch state increases as the Delta pulse duration increases (Fig. 6). It is likely that for shorter Delta pulse durations, if the system is to undergo the spontaneous "OFF" to "ON" transition, it does so early, soon after the application of the Delta signal. However, in the case of longer duration input signals, the continued presence of the signal allows trajectories to switch state even much later in the simulation, resulting in an apparently longer first passage time. Collectively, these results imply that even for very short signal pulse, a small fraction of a population of cells receiving a pulse of Delta signal could switch their state due to stochastic effects.
The mean and standard deviation of >20 runs in each case are plotted. The percentage of trajectories that switched to the "ON" state under the given Delta signal is indicated below each data point. All points except those connected by the same letter (A,B,C) are statistically distinct (p<0.01, 2-tail t-test).
Bifurcation Analysis
A number of parameters in the model have not been directly experimentally measured and were estimated from data available for similar protein classes in different contexts, and we thus performed sensitivity analysis for all such parameters by varying them individually through a broad range of values in the deterministic model (Table 3, Fig. S2). Although in most cases the qualitative behavior of the system remained unchanged, the system did exhibit considerable sensitivity to specific parameters, which were then subjected to further analysis. These include: the half-life of NICD, the equilibrium binding constant of NICD with RBP-Jκ (Ka), the maximal transcription rates (Vmax), and the repression constant of Hes1 (rNbox). NICD has a long half-life of a few hours under normal physiological conditions [55]. However, our model indicates that if the NICD half-life is drastically reduced, the system fails to function as a switch and cannot express high levels of Hes1 (Fig. S3). In addition, the equilibrium binding constant (Ka) of NICD to RBP-Jκ in the model is 108 M−1, but as Ka increases – denoting stronger interactions of NICD with the promoter – bifurcation analysis demonstrates that the OFF-ON transition occurs at accordingly lower values of the Delta signal (kDelp) (Fig. 7A). Similarly, increasing the maximal transcription rate of Hes1 (Vmaxh) to indicate a stronger promoter shifts the OFF-ON transitions to lower Delta signal strengths (Fig. 7B).
(A) Bifurcation analysis of how the switching points vary with the equilibrium binding constant (Ka) of NICD to RBP-Jk. Stronger interaction between NICD and RBP-Jk lowers the threshold of Delta signal required to turn the system ON. (B) Bifurcation analysis of how the switching points vary with the maximal transcription rate of Hes1 (Vmaxh). A higher maximal transcription rate, indicating a stronger Hes1 promoter, also slightly shifts the region of bistability towards lower Delta signal strengths.
The Degree of Repression by Hes1 Determines the Qualitative Nature of the Cellular Response to Delta Stimuli
Interestingly, the response of the deterministic model was most sensitive to the extent to which Hes1 binding reduced or repressed expression of target genes (rNbox). As the Hes1 repression constant (rNbox) is progressively decreased (or the repressive strength of Hes1 progressively increased) from 0.3 to 0.1, the final steady state concentrations of Hes1 progressively decrease for a given level of Delta signaling (Fig. S4), but the system continues to exhibit bistability. Intriguingly, as the value of rNbox is further decreased below 0.1, there is a dramatic qualitative change in the response of the system. Specifically, the system undergoes a bifurcation or transition from bistable to monostable behavior and at such high repressive strengths is unable to attain high steady state Hes1 expression levels. Finally at very low values of rNbox (<0.03), it once again undergoes a transition to a stable oscillatory response where the Hes1 levels in the cell oscillate about a low mean steady state value (Fig. 8A). A phase plot of the response of the system with variable rNbox (Fig. 8B) demonstrates how the same gene network can transition from behaving as a bistable switch to being an oscillator. The model thus elucidates the versatility of the system, where tuning of a single key parameter can convert its behavior from a switch to a clock. Previous hes1 models showing sustained oscillations have focused exclusively on the low rNbox region (i.e. rNbox = 0) of such a phase plot [30],[36],[38],[39].
(A) At lower values or rNbox (higher repression constants for Hes1), the network predicts oscillations in Hes1 levels. As the value of rNbox is decreased to 0.03 and lower, the system exhibits stable oscillations. (B) At a fixed Delta signal strength of kDelp = 2×10−4, as the rNbox is progressively decreased, the response of Hes1 transitions from behaving as a bistable switch to a brief region of monostability to an oscillator.
The Notch signaling system is an evolutionarily conserved network that functions in multiple organs to orchestrate cell fate specification [63]–[65] in a context dependent manner. In some cases, it can function as a binary cell fate switch at the individual cell level [16],[17], whereas in other situations cell-cell contact dependent Notch signaling can result in pattern formation in an array of cells [14],[15], and in yet other contexts it can function as a biological clock to govern pattern formation and differentiation during somitogenesis [27]–[29]. Although several additional components such as Fringe, Numb, and Presenilin can feed into and modulate the Notch signaling cascade, the core of the signaling pathway is relatively simple, where Notch acts as a membrane bound transcription factor that is activated by ligand binding and induces transcription of target hes genes via its interaction with the RBP-Jk transcription factor [10]. However, the system can exhibit complex inter-regulation of its components. A better understanding of the functioning and regulation of this signaling system – and in particular how it exhibits diverse behaviors in different contexts – is valuable from a basic biology standpoint, in understanding how misregulation of the Notch signaling pathway can underlie disease, and from regenerative medicine viewpoint in therapeutic applications of stem cells.
Mathematical modeling can provide valuable insights into the behavior of this gene regulatory circuit. Previous models have focused either on the level of cell-cell interactions to simulate the levels of Notch and Delta within adjacent cells and thereby analyze pattern formation based on levels of Delta and Notch levels in an array of cells [33]–[35], or on the autoregulation of the hes genes in isolation to examine the oscillatory behavior of the gene circuit [30],[36],[39],[40],[44],[45],[66],[67]. Here we have developed an integrative model that takes into account the intracellular signaling network downstream of Notch activation through its ligand Delta, leading to the activation of the hes1 gene via interaction with RBP-Jκ. These three genes potentially regulate the transcription of one another (Text S1, Fig S1) [37],[46],[47], forming a network of positive and negative feedback loops (Fig. 1B). Our model begins to elucidate how a cell can potentially tune key system parameters in the resulting Notch1-Hes1 gene circuit to elicit diverse responses.
The behavior of the system was most sensitive to the repression constant of Hes1, rNbox. The degree of Hes1 repression of a transcriptional target can be modulated by the presence of co-factors. For example, whereas Groucho can act as a transcriptional co-repressor for Hes1, Runx2 can act as a negative regulator of the repressive activity of Hes1 by interfering with the interaction of Hes1 with the TLE corepressors [68]. The repressive activity of Hes1 can also be further potentiated by its interaction with the winged-helix protein brain factor 1 [69]. Therefore, because different cells can express these factors to different extents, which can thereby modulate the value of rNbox, the same gene circuit can be tuned to transduce an input Delta signal into qualitatively different responses – oscillation vs. switching.
The model predicts that for low repressive strengths of Hes1 (0.1<rNbox<0.3), the Hes1 expression level functions as a bistable switch in response to varying the strength of the Delta signal, thereby providing an unambiguous fate switch that is insensitive to the presence of small fluctuations in input signal (Fig. 2). Hysteresis has been previously observed experimentally in other biological systems including the JNK signaling cascade [70],[71] and the Cdc2 cell cycle regulation [72]. Parameters such as Ka (the association binding constant of NICD to RBP-Jκ) and Vmax (the maximal transcription rates) can shift the region of bistability, thus changing the sensitivity of the system to the Delta signal, but the qualitative nature of the gene network remains the same for a broad range of these parameter values. Positive feedback loops with nonlinearity can yield bistability [51], and both Notch1 autoregulation and NICD-mediated conversion of RBP-Jκ into a transcriptional activator that in turn upregulates Notch1 expression constitute positive feedback loops that can drive this behavior.
Since the numbers of some protein and mRNA species in the model were low (Table 2), we developed a stochastic model to examine the effect of biological noise and cell-to-cell variability on the bistable response of the system to Delta signaling. Spontaneous OFF to ON switching of states was observed even in regions not predicted by the deterministic model. For example, as the Delta values are increased through the bistable range, the percentage of trajectories switching to the ON state increases, and the average FPT for these trajectories decreases (Fig. 3B). These results are consistent with observations in other bistable systems [73], and computationally in other signaling systems [51], where noise has been shown to cause spontaneous switching of states. However, since the timescale of a system's downstream response to the Notch network's state varies from a few hours (for example during somitogenesis) [74] to a few days (for example during stem cell differentiation) ([57]), the impact of stochastic noise on the fate switch will also be different in different contexts. Thus, for very low Delta signals, the average FPT is sufficiently high (>110 hrs) such that the cell remains in the OFF state for prolonged periods of time and would be non-responsive to Delta signaling over timescales of a few hours, whereas in the case of a population of cells experiencing Notch signaling over a period of 4–6 days, spontaneous switching could undermine the genetic switch and cause some cells to change fate at these low Delta input signals.
While the system can behave as a switch in a particular range of parameters at steady state, there are also many situations in which Notch signaling is transient, yet is sufficient to induce a switch in cell fate [18]–[24]. To simulate this, the model behavior was analyzed under transient Delta activation. The network response to a transient Delta stimulus was a strong function of both the signal intensity and duration, and either a high intensity signal for a short duration or a low intensity signal for a prolonged duration was capable of inducing transient increase in Hes1 expression levels for up to 2.5 days after withdrawal of the signal (Fig. 5), a time sufficient to initiate a biological response [57].
This prolonged expression of Hes1 upon transient Delta activation is due to the long half-life of NICD [55]. The bistable switch is thus sensitive to the degradation constant of NICD. If the NICD half-life were for example drastically reduced, the model would predict that the system would fail to express high levels of Hes1 regardless of Delta levels (Fig. S3). Hes1 is a repressive transcription factor that in some systems plays a crucial role in suppressing the activation of oncogenes. For example, in breast cancer cells, Hes1 can inhibit both estrogen- and heregulin-beta1-stimulated growth via downregulation of E2F-1 expression [75]. Thus, a malfunction in the Notch system, such as a reduction in NICD half-life, could contribute to cell transformation. Indeed, aberrant Notch signaling is implicated in many cancers (reviewed in [76]). For example, integrin-linked kinase (ILK), which is either activated or overexpressed in many types of cancers including breast cancer [77], can remarkably reduce the protein stability of Notch1 and thus decrease its half-life drastically [78]. Interestingly, high ILK and low NICD levels are detected in basal cell carcinoma and melanoma patients [78].
By increasing the repressive strength of the Hes1 dimer by 10-fold (rNbox<0.03), the cell can transition from being a bistable system, to a brief region of monostability, and finally to an oscillator (Fig. 8B). Oscillations occur with a time period of approximately 2 hrs, similar to what Hirata et al. observed in cell culture [30]. This value also compares well with the various models that have been developed (for the Hes system in isolation) to explain oscillations in the hes family of genes and their homologues. These models assume complete repression in the presence of even a single Hes homodimer bound to the promoter region [36],[39],[45],[66]. This corresponds to an rNbox value of 0, in which case there would be no difference between the repressive strength of promoters with 1, 2 or 3 N-boxes. From the experimental observations of Takebayashi et al. [37], where the repressive strength of the promoter did in fact increase with the number of N-boxes, the estimated value of rNbox is 0.3. However, during somitogenesis, the factors expressed in the presomitic mesoderm (PSM) may enhance the repression due to Hes1 such that the value of rNbox is very low.
This current model represents the Notch signaling network core in a single cell, and it can readily be extended to a field of cells to analyze the role of Notch in patterning tissue formation [60]. In addition, there are numerous cell-specific mechanisms and factors that feed into this important signaling core [79]–[81]. Additional molecular species can be added to this model framework, or the parameter values of the current model can readily be modulated for example to simulate changes in DNA binding affinities, repressive constants, or the protein and mRNA stabilities as a function of cell-specific factors. This simple but versatile model can therefore be expanded by incorporation of additional molecular mechanism, specific to particular cell types, to make predictions on the role of Notch signaling in diverse cells and tissues.
In summary, we have theoretically and computationally analyzed the Notch1-RBP-Jκ-Hes1 signaling network, which is responsible for cell fate specification in numerous contexts. Our results indicate that the network, consisting of both positive and negative feedback mechanisms, can be tuned to function either as a bistable cell fate switch or an oscillator based on relatively small changes in a key parameter value. Furthermore, the duration and strength of the Delta signal regulate either the peak or the final steady state levels of Hes1 attained. Therefore, cells can readily tune the Notch system to regulate a variety downstream cell fates and functions.
Supporting Information
Text S1.
Supplemental Materials
Figure S1.
Transcriptional analysis of the Notch1 promoter. Relative fold changes in the activity of the Notch1 promoter in the presence of exogenous Hes1 (H), dNHes1 (dN), RBP-Jκ(R) and NICD (N) are shown. Relative amounts of plasmids used in each case encoding the respective cDNA are indicated. For example H0.1dN1R0.5N0.5 indicates Hes1 = 0.1 µg, dNHes1 = 1 µg, RBP-Jk = 0.5 µg and NICD = 0.5 µg in a total of 4 µg transfection.
Figure S2.
Bifurcation Analysis. (A) Bifurcation analysis of how the switching points vary with the Hes1 DNA association constant (Kn). Varying the association constant over two orders of magnitude causes a slight shift in the strength of the Delta signal required to switch the system state. Thus, stronger DNA association of Hes1 (higher values of Kn) increases the threshold values of Delta signal strength (Kdelp) required to turn the system ON. (B) Bifurcation analysis of how the switching points vary with the Hes1 dimerization constant (KaHp). Varying the dimerization constant over two orders of magnitude causes a slight increase in the strength of the Delta signal required to switch the system state to ON. (C) Bifurcation analysis of how the switching points vary with the repression constant of RBP-Jκ (rR). Increasing the repression constant from 0 to 0.5 (corresponding to a decrease in the RBP-Jκ repressive strength), has very little effect on the threshold of Delta signaling strength required to turn the system ON.
Figure S3.
Effect of half-life of NICD on the Hes1 switch. (A) Changing the half life from 3 hrs (kdNcp = 0.00385) to 8 hrs (0.0014) due to the effect of GSK3β causes a slight increase in the steady state Hes1 concentration in response to a Delta signal of kDelp = 5×10−4, but no qualitative change in the switch. Increasing the degradation constant by 10-fold (kdNcp = 0.0014 to 0.014) however, causes complete suppression of the switch. (B) Analysis of how the threshold value of Delta signal required to switch the system from OFF to ON increases with the increasing degradation constant of NICD (decreasing NICD half life).
Figure S4.
Effect of repression through Hes1 (rNbox) on the high steady state values of Hes1 expression.Decreasing rNbox progressively decreases the steady state concentrations of Hes1 in the rNbox range of 0.3 to 0.1.
We would like to thank Dr. Wei-Qiang Gao, Dr. Diane Hayward, Dr. R. Kopan, Dr. R. Kageyama and Dr. C. Cepko for providing valuable plasmid constructs.
Author Contributions
Conceived and designed the experiments: SA DVS. Performed the experiments: SA. Analyzed the data: SA. Contributed reagents/materials/analysis tools: SA CA. Wrote the paper: SA.
- 1. Denhardt DT (1996) Signal-transducing protein phosphorylation cascades mediated by Ras/Rho proteins in the mammalian cell: the potential for multiplex signalling. Biochem J 318(Pt 3): 729–747.DT Denhardt1996Signal-transducing protein phosphorylation cascades mediated by Ras/Rho proteins in the mammalian cell: the potential for multiplex signalling.Biochem J318(Pt 3)729747
- 2. Keltner NL, Gorman AG (2007) Second messengers. Perspect Psychiatr Care 43: 60–64.NL KeltnerAG Gorman2007Second messengers.Perspect Psychiatr Care436064
- 3. Weng G, Bhalla US, Iyengar R (1999) Complexity in biological signaling systems. Science 284: 92–96.G. WengUS BhallaR. Iyengar1999Complexity in biological signaling systems.Science2849296
- 4. Klipp E, Liebermeister W (2006) Mathematical modeling of intracellular signaling pathways. BMC Neurosci 7: Suppl 1S10.E. KlippW. Liebermeister2006Mathematical modeling of intracellular signaling pathways.BMC Neurosci7Suppl 1S10
- 5. Barua D, Faeder JR, Haugh JM (2008) Computational models of tandem SRC homology 2 domain interactions and application to phosphoinositide 3-kinase. J Biol Chem 283: 7338–7345.D. BaruaJR FaederJM Haugh2008Computational models of tandem SRC homology 2 domain interactions and application to phosphoinositide 3-kinase.J Biol Chem28373387345
- 6. Obrink B, Sawa H, Scheffrahn I, Singer BB, Sigmundsson K, et al. (2002) Computational analysis of isoform-specific signal regulation by CEACAM1-A cell adhesion molecule expressed in PC12 cells. Ann N Y Acad Sci 971: 597–607.B. ObrinkH. SawaI. ScheffrahnBB SingerK. Sigmundsson2002Computational analysis of isoform-specific signal regulation by CEACAM1-A cell adhesion molecule expressed in PC12 cells.Ann N Y Acad Sci971597607
- 7. Carthew RW (2007) Pattern formation in the Drosophila eye. Curr Opin Genet Dev 17: 309–313.RW Carthew2007Pattern formation in the Drosophila eye.Curr Opin Genet Dev17309313
- 8. Ikawa T, Kawamoto H, Goldrath AW, Murre C (2006) E proteins and Notch signaling cooperate to promote T cell lineage specification and commitment. J Exp Med 203: 1329–1342.T. IkawaH. KawamotoAW GoldrathC. Murre2006E proteins and Notch signaling cooperate to promote T cell lineage specification and commitment.J Exp Med20313291342
- 9. Artavanis-Tsakonas S, Rand MD, Lake RJ (1999) Notch signaling: cell fate control and signal integration in development. Science 284: 770–776.S. Artavanis-TsakonasMD RandRJ Lake1999Notch signaling: cell fate control and signal integration in development.Science284770776
- 10. Kopan R (2002) Notch: a membrane-bound transcription factor. J Cell Sci 115: 1095–1097.R. Kopan2002Notch: a membrane-bound transcription factor.J Cell Sci11510951097
- 11. Weinmaster G, Roberts VJ, Lemke G (1991) A homolog of Drosophila Notch expressed during mammalian development. Development 113: 199–205.G. WeinmasterVJ RobertsG. Lemke1991A homolog of Drosophila Notch expressed during mammalian development.Development113199205
- 12. Greenwald I (1994) Structure/function studies of lin-12/Notch proteins. Curr Opin Genet Dev 4: 556–562.I. Greenwald1994Structure/function studies of lin-12/Notch proteins.Curr Opin Genet Dev4556562
- 13. Lu FM, Lux SE (1996) Constitutively active human Notch1 binds to the transcription factor CBF1 and stimulates transcription through a promoter containing a CBF1-responsive element. Proc Natl Acad Sci U S A 93: 5663–5667.FM LuSE Lux1996Constitutively active human Notch1 binds to the transcription factor CBF1 and stimulates transcription through a promoter containing a CBF1-responsive element.Proc Natl Acad Sci U S A9356635667
- 14. Cornell RA, Eisen JS (2005) Notch in the pathway: the roles of Notch signaling in neural crest development. Semin Cell Dev Biol 16: 663–672.RA CornellJS Eisen2005Notch in the pathway: the roles of Notch signaling in neural crest development.Semin Cell Dev Biol16663672
- 15. Brooker R, Hozumi K, Lewis J (2006) Notch ligands with contrasting functions: Jagged1 and Delta1 in the mouse inner ear. Development 133: 1277–1286.R. BrookerK. HozumiJ. Lewis2006Notch ligands with contrasting functions: Jagged1 and Delta1 in the mouse inner ear.Development13312771286
- 16. Blanpain C, Lowry WE, Pasolli HA, Fuchs E (2006) Canonical notch signaling functions as a commitment switch in the epidermal lineage. Genes Dev 20: 3022–3035.C. BlanpainWE LowryHA PasolliE. Fuchs2006Canonical notch signaling functions as a commitment switch in the epidermal lineage.Genes Dev2030223035
- 17. Matsuda Y, Wakamatsu Y, Kohyama J, Okano H, Fukuda K, et al. (2005) Notch signaling functions as a binary switch for the determination of glandular and luminal fates of endodermal epithelium during chicken stomach development. Development 132: 2783–2793.Y. MatsudaY. WakamatsuJ. KohyamaH. OkanoK. Fukuda2005Notch signaling functions as a binary switch for the determination of glandular and luminal fates of endodermal epithelium during chicken stomach development.Development13227832793
- 18. Lefort N, Benne C, Lelievre JD, Dorival C, Balbo M, et al. (2006) Short exposure to Notch ligand Delta-4 is sufficient to induce T-cell differentiation program and to increase the T cell potential of primary human CD34+ cells. Exp Hematol 34: 1720–1729.N. LefortC. BenneJD LelievreC. DorivalM. Balbo2006Short exposure to Notch ligand Delta-4 is sufficient to induce T-cell differentiation program and to increase the T cell potential of primary human CD34+ cells.Exp Hematol3417201729
- 19. Carotta S, Brady J, Wu L, Nutt SL (2006) Transient Notch signaling induces NK cell potential in Pax5-deficient pro-B cells. Eur J Immunol 36: 3294–3304.S. CarottaJ. BradyL. WuSL Nutt2006Transient Notch signaling induces NK cell potential in Pax5-deficient pro-B cells.Eur J Immunol3632943304
- 20. Morrison SJ, Perez SE, Qiao Z, Verdi JM, Hicks C, et al. (2000) Transient Notch activation initiates an irreversible switch from neurogenesis to gliogenesis by neural crest stem cells. Cell 101: 499–510.SJ MorrisonSE PerezZ. QiaoJM VerdiC. Hicks2000Transient Notch activation initiates an irreversible switch from neurogenesis to gliogenesis by neural crest stem cells.Cell101499510
- 21. Hooper SD, Boue S, Krause R, Jensen LJ, Mason CE, et al. (2007) Identification of tightly regulated groups of genes during Drosophila melanogaster embryogenesis. Mol Syst Biol 3: 72.SD HooperS. BoueR. KrauseLJ JensenCE Mason2007Identification of tightly regulated groups of genes during Drosophila melanogaster embryogenesis.Mol Syst Biol372
- 22. Appel B, Givan LA, Eisen JS (2001) Delta-Notch signaling and lateral inhibition in zebrafish spinal cord development. BMC Dev Biol 1: 13.B. AppelLA GivanJS Eisen2001Delta-Notch signaling and lateral inhibition in zebrafish spinal cord development.BMC Dev Biol113
- 23. Appel B, Eisen JS (1998) Regulation of neuronal specification in the zebrafish spinal cord by Delta function. Development 125: 371–380.B. AppelJS Eisen1998Regulation of neuronal specification in the zebrafish spinal cord by Delta function.Development125371380
- 24. Bettenhausen B, Hrabe de Angelis M, Simon D, Guenet JL, Gossler A (1995) Transient and restricted expression during mouse embryogenesis of Dll1, a murine gene closely related to Drosophila Delta. Development 121: 2407–2418.B. BettenhausenM. Hrabe de AngelisD. SimonJL GuenetA. Gossler1995Transient and restricted expression during mouse embryogenesis of Dll1, a murine gene closely related to Drosophila Delta.Development12124072418
- 25. Yu X, Zou J, Ye Z, Hammond H, Chen G, et al. (2008) Notch signaling activation in human embryonic stem cells is required for embryonic, but not trophoblastic, lineage commitment. Cell Stem Cell 2: 461–471.X. YuJ. ZouZ. YeH. HammondG. Chen2008Notch signaling activation in human embryonic stem cells is required for embryonic, but not trophoblastic, lineage commitment.Cell Stem Cell2461471
- 26. Pourquie O (2001) The vertebrate segmentation clock. Journal of Anatomy 199: 169–175.O. Pourquie2001The vertebrate segmentation clock.Journal of Anatomy199169175
- 27. Saga Y, Takeda H (2001) The making of the somite: molecular events in vertebrate segmentation. Nat Rev Genet 2: 835–845.Y. SagaH. Takeda2001The making of the somite: molecular events in vertebrate segmentation.Nat Rev Genet2835845
- 28. Palmeirim I, Henrique D, Ish-Horowicz D, Pourquie O (1997) Avian hairy gene expression identifies a molecular clock linked to vertebrate segmentation and somitogenesis. Cell 91: 639–648.I. PalmeirimD. HenriqueD. Ish-HorowiczO. Pourquie1997Avian hairy gene expression identifies a molecular clock linked to vertebrate segmentation and somitogenesis.Cell91639648
- 29. Jouve C, Palmeirim I, Henrique D, Beckers J, Gossler A, et al. (2000) Notch signalling is required for cyclic expression of the hairy-like gene HES1 in the presomitic mesoderm. Development 127: 1421–1429.C. JouveI. PalmeirimD. HenriqueJ. BeckersA. Gossler2000Notch signalling is required for cyclic expression of the hairy-like gene HES1 in the presomitic mesoderm.Development12714211429
- 30. Hirata H, Yoshiura S, Ohtsuka T, Bessho Y, Harada T, et al. (2002) Oscillatory expression of the bHLH factor Hes1 regulated by a negative feedback loop. Science 298: 840–843.H. HirataS. YoshiuraT. OhtsukaY. BesshoT. Harada2002Oscillatory expression of the bHLH factor Hes1 regulated by a negative feedback loop.Science298840843
- 31. Shimojo H, Ohtsuka T, Kageyama R (2008) Oscillations in notch signaling regulate maintenance of neural progenitors. Neuron 58: 52–64.H. ShimojoT. OhtsukaR. Kageyama2008Oscillations in notch signaling regulate maintenance of neural progenitors.Neuron585264
- 32. Lahav G (2004) The strength of indecisiveness: oscillatory behavior for better cell fate determination. Sci STKE 2004: pe55.G. Lahav2004The strength of indecisiveness: oscillatory behavior for better cell fate determination.Sci STKE2004pe55
- 33. Collier JR, Monk NA, Maini PK, Lewis JH (1996) Pattern formation by lateral inhibition with feedback: a mathematical model of delta-notch intercellular signalling. J Theor Biol 183: 429–446.JR CollierNA MonkPK MainiJH Lewis1996Pattern formation by lateral inhibition with feedback: a mathematical model of delta-notch intercellular signalling.J Theor Biol183429446
- 34. Wearing HJ, Owen MR, Sherratt JA (2000) Mathematical modelling of juxtacrine patterning. Bull Math Biol 62: 293–320.HJ WearingMR OwenJA Sherratt2000Mathematical modelling of juxtacrine patterning.Bull Math Biol62293320
- 35. Webb SD, Owen MR (2004) Oscillations and patterns in spatially discrete models for developmental intercellular signalling. J Math Biol 48: 444–476.SD WebbMR Owen2004Oscillations and patterns in spatially discrete models for developmental intercellular signalling.J Math Biol48444476
- 36. Monk NA (2003) Oscillatory expression of Hes1, p53, and NF-kappaB driven by transcriptional time delays. Curr Biol 13: 1409–1413.NA Monk2003Oscillatory expression of Hes1, p53, and NF-kappaB driven by transcriptional time delays.Curr Biol1314091413
- 37. Takebayashi K, Sasai Y, Sakai Y, Watanabe T, Nakanishi S, et al. (1994) Structure, chromosomal locus, and promoter analysis of the gene encoding the mouse helix-loop-helix factor HES-1. Negative autoregulation through the multiple N box elements. J Biol Chem 269: 5150–5156.K. TakebayashiY. SasaiY. SakaiT. WatanabeS. Nakanishi1994Structure, chromosomal locus, and promoter analysis of the gene encoding the mouse helix-loop-helix factor HES-1. Negative autoregulation through the multiple N box elements.J Biol Chem26951505156
- 38. Jensen MH, Sneppen K, Tiana G (2003) Sustained oscillations and time delays in gene expression of protein Hes1. FEBS Lett 541: 176–177.MH JensenK. SneppenG. Tiana2003Sustained oscillations and time delays in gene expression of protein Hes1.FEBS Lett541176177
- 39. Bernard S, Cajavec B, Pujo-Menjouet L, Mackey MC, Herzel H (2006) Modelling transcriptional feedback loops: the role of Gro/TLE1 in Hes1 oscillations. Philos Transact A Math Phys Eng Sci 364: 1155–1170.S. BernardB. CajavecL. Pujo-MenjouetMC MackeyH. Herzel2006Modelling transcriptional feedback loops: the role of Gro/TLE1 in Hes1 oscillations.Philos Transact A Math Phys Eng Sci36411551170
- 40. Heron EA, Finkenstadt B, Rand DA (2007) Bayesian inference for dynamic transcriptional regulation; the Hes1 system as a case study. Bioinformatics 23: 2596–2603.EA HeronB. FinkenstadtDA Rand2007Bayesian inference for dynamic transcriptional regulation; the Hes1 system as a case study.Bioinformatics2325962603
- 41. Rao CV, Wolf DM, Arkin AP (2002) Control, exploitation and tolerance of intracellular noise. Nature 420: 231–237.CV RaoDM WolfAP Arkin2002Control, exploitation and tolerance of intracellular noise.Nature420231237
- 42. Weinberger LS, Burnett JC, Toettcher JE, Arkin AP, Schaffer DV (2005) Stochastic gene expression in a lentiviral positive-feedback loop: HIV-1 Tat fluctuations drive phenotypic diversity. Cell 122: 169–182.LS WeinbergerJC BurnettJE ToettcherAP ArkinDV Schaffer2005Stochastic gene expression in a lentiviral positive-feedback loop: HIV-1 Tat fluctuations drive phenotypic diversity.Cell122169182
- 43. Gillespie DT (1977) Exact stochastic simulation of coupled chemical reactions. J Phys Chem 81: 2340–2361.DT Gillespie1977Exact stochastic simulation of coupled chemical reactions.J Phys Chem8123402361
- 44. Barrio M, Burrage K, Leier A, Tian T (2006) Oscillatory regulation of Hes1: Discrete stochastic delay modelling and simulation. PLoS Comput Biol 2: e117.M. BarrioK. BurrageA. LeierT. Tian2006Oscillatory regulation of Hes1: Discrete stochastic delay modelling and simulation.PLoS Comput Biol2e117
- 45. Zeiser S, Muller J, Liebscher V (2007) Modeling the Hes1 oscillator. J Comput Biol 14: 984–1000.S. ZeiserJ. MullerV. Liebscher2007Modeling the Hes1 oscillator.J Comput Biol149841000
- 46. Jarriault S, Brou C, Logeat F, Schroeter EH, Kopan R, et al. (1995) Signalling downstream of activated mammalian Notch. Nature 377: 355–358.S. JarriaultC. BrouF. LogeatEH SchroeterR. Kopan1995Signalling downstream of activated mammalian Notch.Nature377355358
- 47. Brou C, Logeat F, Lecourtois M, Vandekerckhove J, Kourilsky P, et al. (1994) Inhibition of the DNA-binding activity of Drosophila suppressor of hairless and of its human homolog, KBF2/RBP-J kappa, by direct protein-protein interaction with Drosophila hairless. Genes Dev 8: 2491–2503.C. BrouF. LogeatM. LecourtoisJ. VandekerckhoveP. Kourilsky1994Inhibition of the DNA-binding activity of Drosophila suppressor of hairless and of its human homolog, KBF2/RBP-J kappa, by direct protein-protein interaction with Drosophila hairless.Genes Dev824912503
- 48. Lewis AK, Frantz GD, Carpenter DA, de Sauvage FJ, Gao WQ (1998) Distinct expression patterns of notch family receptors and ligands during development of the mammalian inner ear. Mech Dev 78: 159–163.AK LewisGD FrantzDA CarpenterFJ de SauvageWQ Gao1998Distinct expression patterns of notch family receptors and ligands during development of the mammalian inner ear.Mech Dev78159163
- 49. Amakawa R, Jing W, Ozawa K, Matsunami N, Hamaguchi Y, et al. (1993) Human Jk recombination signal binding protein gene (IGKJRB): comparison with its mouse homologue. Genomics 17: 306–315.R. AmakawaW. JingK. OzawaN. MatsunamiY. Hamaguchi1993Human Jk recombination signal binding protein gene (IGKJRB): comparison with its mouse homologue.Genomics17306315
- 50. Gilman A, Arkin AP (2002) Genetic "code": representations and dynamical models of genetic components and networks. Annu Rev Genomics Hum Genet 3: 341–369.A. GilmanAP Arkin2002Genetic "code": representations and dynamical models of genetic components and networks.Annu Rev Genomics Hum Genet3341369
- 51. Lai K, Robertson MJ, Schaffer DV (2004) The sonic hedgehog signaling system as a bistable genetic switch. Biophys J 86: 2748–2757.K. LaiMJ RobertsonDV Schaffer2004The sonic hedgehog signaling system as a bistable genetic switch.Biophys J8627482757
- 52. Lewis J (2003) Autoinhibition with transcriptional delay: a simple mechanism for the zebrafish somitogenesis oscillator. Curr Biol 13: 1398–1408.J. Lewis2003Autoinhibition with transcriptional delay: a simple mechanism for the zebrafish somitogenesis oscillator.Curr Biol1313981408
- 53. Cinquin O (2007) Repressor dimerization in the zebrafish somitogenesis clock. PLoS Comput Biol 3: e32.O. Cinquin2007Repressor dimerization in the zebrafish somitogenesis clock.PLoS Comput Biol3e32
- 54. Logeat F, Bessia C, Brou C, LeBail O, Jarriault S, et al. (1998) The Notch1 receptor is cleaved constitutively by a furin-like convertase. Proc Natl Acad Sci U S A 95: 8108–8112.F. LogeatC. BessiaC. BrouO. LeBailS. Jarriault1998The Notch1 receptor is cleaved constitutively by a furin-like convertase.Proc Natl Acad Sci U S A9581088112
- 55. Foltz DR, Santiago MC, Berechid BE, Nye JS (2002) Glycogen synthase kinase-3beta modulates notch signaling and stability. Curr Biol 12: 1006–1011.DR FoltzMC SantiagoBE BerechidJS Nye2002Glycogen synthase kinase-3beta modulates notch signaling and stability.Curr Biol1210061011
- 56. Espinosa L, Ingles-Esteve J, Aguilera C, Bigas A (2003) Phosphorylation by glycogen synthase kinase-3 beta down-regulates Notch activity, a link for Notch and Wnt pathways. J Biol Chem 278: 32227–32235.L. EspinosaJ. Ingles-EsteveC. AguileraA. Bigas2003Phosphorylation by glycogen synthase kinase-3 beta down-regulates Notch activity, a link for Notch and Wnt pathways.J Biol Chem2783222732235
- 57. Tanigaki K, Nogaki F, Takahashi J, Tashiro K, Kurooka H, et al. (2001) Notch1 and Notch3 instructively restrict bFGF-responsive multipotent neural progenitor cells to an astroglial fate. Neuron 29: 45–55.K. TanigakiF. NogakiJ. TakahashiK. TashiroH. Kurooka2001Notch1 and Notch3 instructively restrict bFGF-responsive multipotent neural progenitor cells to an astroglial fate.Neuron294555
- 58. Hasty J, McMillen D, Isaacs F, Collins JJ (2001) Computational studies of gene regulatory networks: in numero molecular biology. Nat Rev Genet 2: 268–279.J. HastyD. McMillenF. IsaacsJJ Collins2001Computational studies of gene regulatory networks: in numero molecular biology.Nat Rev Genet2268279
- 59. Gillespie DT (2001) Approximate accelerated stochastic simulation of chemically reacting systems. The Journal of Chemical Physics 115: 1716–1733.DT Gillespie2001Approximate accelerated stochastic simulation of chemically reacting systems.The Journal of Chemical Physics11517161733
- 60. Saha K, Schaffer DV (2006) Signal dynamics in Sonic hedgehog tissue patterning. Development 133: 889–900.K. SahaDV Schaffer2006Signal dynamics in Sonic hedgehog tissue patterning.Development133889900
- 61. Arkin A, Ross J, McAdams HH (1998) Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells. Genetics 149: 1633–1648.A. ArkinJ. RossHH McAdams1998Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells.Genetics14916331648
- 62. Hasty J, Pradines J, Dolnik M, Collins JJ (2000) Noise-based switches and amplifiers for gene expression. Proc Natl Acad Sci U S A 97: 2075–2080.J. HastyJ. PradinesM. DolnikJJ Collins2000Noise-based switches and amplifiers for gene expression.Proc Natl Acad Sci U S A9720752080
- 63. Maillard I, Adler SH, Pear WS (2003) Notch and the immune system. Immunity 19: 781–791.I. MaillardSH AdlerWS Pear2003Notch and the immune system.Immunity19781791
- 64. Bolos V, Grego-Bessa J, de la Pompa JL (2007) Notch signaling in development and cancer. Endocr Rev 28: 339–363.V. BolosJ. Grego-BessaJL de la Pompa2007Notch signaling in development and cancer.Endocr Rev28339363
- 65. Beatus P, Lendahl U (1998) Notch and neurogenesis. J Neurosci Res 54: 125–136.P. BeatusU. Lendahl1998Notch and neurogenesis.J Neurosci Res54125136
- 66. Zeiser S, Liebscher HV, Tiedemann H, Rubio-Aliaga I, Przemeck GK, et al. (2006) Number of active transcription factor binding sites is essential for the Hes7 oscillator. Theor Biol Med Model 3: 11.S. ZeiserHV LiebscherH. TiedemannI. Rubio-AliagaGK Przemeck2006Number of active transcription factor binding sites is essential for the Hes7 oscillator.Theor Biol Med Model311
- 67. Zeiser S, Rivera O, Kuttler C, Hense B, Lasser R, et al. (2007) Oscillations of Hes7 caused by negative autoregulation and ubiquitination. Comput Biol Chem 18: 18.S. ZeiserO. RiveraC. KuttlerB. HenseR. Lasser2007Oscillations of Hes7 caused by negative autoregulation and ubiquitination.Comput Biol Chem1818
- 68. McLarren KW, Theriault FM, Stifani S (2001) Association with the nuclear matrix and interaction with Groucho and RUNX proteins regulate the transcription repression activity of the basic helix loop helix factor Hes1. J Biol Chem 276: 1578–1584.KW McLarrenFM TheriaultS. Stifani2001Association with the nuclear matrix and interaction with Groucho and RUNX proteins regulate the transcription repression activity of the basic helix loop helix factor Hes1.J Biol Chem27615781584
- 69. Yao J, Lai E, Stifani S (2001) The winged-helix protein brain factor 1 interacts with groucho and hes proteins to repress transcription. Mol Cell Biol 21: 1962–1972.J. YaoE. LaiS. Stifani2001The winged-helix protein brain factor 1 interacts with groucho and hes proteins to repress transcription.Mol Cell Biol2119621972
- 70. Bagowski CP, Ferrell JE Jr (2001) Bistability in the JNK cascade. Curr Biol 11: 1176–1182.CP BagowskiJE Ferrell Jr2001Bistability in the JNK cascade.Curr Biol1111761182
- 71. Bagowski CP, Besser J, Frey CR, Ferrell JE Jr (2003) The JNK cascade as a biochemical switch in mammalian cells: ultrasensitive and all-or-none responses. Curr Biol 13: 315–320.CP BagowskiJ. BesserCR FreyJE Ferrell Jr2003The JNK cascade as a biochemical switch in mammalian cells: ultrasensitive and all-or-none responses.Curr Biol13315320
- 72. Pomerening JR, Sontag ED, Ferrell JE Jr (2003) Building a cell cycle oscillator: hysteresis and bistability in the activation of Cdc2. Nat Cell Biol 5: 346–351.JR PomereningED SontagJE Ferrell Jr2003Building a cell cycle oscillator: hysteresis and bistability in the activation of Cdc2.Nat Cell Biol5346351
- 73. Hänggi P, Talkner P, Borkovec M (1990) Reaction-rate theory: fifty years after Kramers. Reviews of Modern Physics 62: 251.P. HänggiP. TalknerM. Borkovec1990Reaction-rate theory: fifty years after Kramers.Reviews of Modern Physics62251
- 74. Giudicelli F, Lewis J (2004) The vertebrate segmentation clock. Curr Opin Genet Dev 14: 407–414.F. GiudicelliJ. Lewis2004The vertebrate segmentation clock.Curr Opin Genet Dev14407414
- 75. Hartman J, Muller P, Foster JS, Wimalasena J, Gustafsson JA, et al. (2004) HES-1 inhibits 17beta-estradiol and heregulin-beta1-mediated upregulation of E2F-1. Oncogene 23: 8826–8833.J. HartmanP. MullerJS FosterJ. WimalasenaJA Gustafsson2004HES-1 inhibits 17beta-estradiol and heregulin-beta1-mediated upregulation of E2F-1.Oncogene2388268833
- 76. Harper JA, Yuan JS, Tan JB, Visan I, Guidos CJ (2003) Notch signaling in development and disease. Clin Genet 64: 461–472.JA HarperJS YuanJB TanI. VisanCJ Guidos2003Notch signaling in development and disease.Clin Genet64461472
- 77. Troussard AA, McDonald PC, Wederell ED, Mawji NM, Filipenko NR, et al. (2006) Preferential dependence of breast cancer cells versus normal cells on integrin-linked kinase for protein kinase B/Akt activation and cell survival. Cancer Res 66: 393–403.AA TroussardPC McDonaldED WederellNM MawjiNR Filipenko2006Preferential dependence of breast cancer cells versus normal cells on integrin-linked kinase for protein kinase B/Akt activation and cell survival.Cancer Res66393403
- 78. Mo JS, Kim MY, Han SO, Kim IS, Ann EJ, et al. (2007) Integrin-linked kinase controls Notch1 signaling by down-regulation of protein stability through Fbw7 ubiquitin ligase. Mol Cell Biol 27: 5565–5574.JS MoMY KimSO HanIS KimEJ Ann2007Integrin-linked kinase controls Notch1 signaling by down-regulation of protein stability through Fbw7 ubiquitin ligase.Mol Cell Biol2755655574
- 79. Campos-Ortega JA (1996) Numb diverts notch pathway off the tramtrack. Neuron 17: 1–4.JA Campos-Ortega1996Numb diverts notch pathway off the tramtrack.Neuron1714
- 80. Dierick H, Bejsovec A (1999) Cellular mechanisms of wingless/Wnt signal transduction. Curr Top Dev Biol 43: 153–190.H. DierickA. Bejsovec1999Cellular mechanisms of wingless/Wnt signal transduction.Curr Top Dev Biol43153190
- 81. Irvine KD (1999) Fringe, Notch, and making developmental boundaries. Curr Opin Genet Dev 9: 434–441.KD Irvine1999Fringe, Notch, and making developmental boundaries.Curr Opin Genet Dev9434441
- 82. Meir E, von Dassow G, Munro E, Odell GM (2002) Robustness, flexibility, and the role of lateral inhibition in the neurogenic network. Curr Biol 12: 778–786.E. MeirG. von DassowE. MunroGM Odell2002Robustness, flexibility, and the role of lateral inhibition in the neurogenic network.Curr Biol12778786
- 83. Kessler CB, Delany AM (2007) Increased Notch 1 expression and attenuated stimulatory G protein coupling to adenylyl cyclase in osteonectin-null osteoblasts. Endocrinology 148: 1666–1674.CB KesslerAM Delany2007Increased Notch 1 expression and attenuated stimulatory G protein coupling to adenylyl cyclase in osteonectin-null osteoblasts.Endocrinology14816661674
- 84. Lam LT, Bresnick EH (1998) Identity of the beta-globin locus control region binding protein HS2NF5 as the mammalian homolog of the notch-regulated transcription factor suppressor of hairless. J Biol Chem 273: 24223–24231.LT LamEH Bresnick1998Identity of the beta-globin locus control region binding protein HS2NF5 as the mammalian homolog of the notch-regulated transcription factor suppressor of hairless.J Biol Chem2732422324231
- 85. Watai Y, Sase I, Shiono H, Nakano Y (2001) Regulation of nuclear import by light-induced activation of caged nuclear localization signal in living cells. FEBS Lett 488: 39–44.Y. WataiI. SaseH. ShionoY. Nakano2001Regulation of nuclear import by light-induced activation of caged nuclear localization signal in living cells.FEBS Lett4883944


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Pentagon dodges decision on Sanchez
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Pentagon leaders today declined to say whether they would keep army Lieutenant General Ricardo Sanchez, freshly exonerated in the Abu Ghraib scandal, in the US military after his current posting ends.
Sanchez, the top US commander in Iraq when prisoners were being abused at Abu Ghraib and other US-run jails, is now based in Germany as commander of the US army's 5th Corps but is due to wrap up the assignment soon.
US Defence Secretary Rumsfeld told a Pentagon briefing Sanchez's future had not been decided.
"Everyone does not go on to another post," Rumsfeld said.
"It gets tight at the top. And he is clearly a person in an important position at the present time, has been in the past, and he is a person who would be considered in the future."
Air Force General Richard Myers, chairman of the Joint Chiefs of Staff, noted that "people stay in their jobs until they are given new jobs or asked to retire".
Sanchez, one of the most prominent Latino officers in the US military, would need Senate confirmation for a promotion or new assignment.
Confirming what Army officials announced on Friday, Myers said the inspector general's office had cleared Sanchez of any wrongdoing in the Abu Ghraib scandal in which American forces physically abused and sexually humiliated prisoners at the jail on the outskirts of Baghdad.
Sanchez's former top deputy, top intelligence official and top legal adviser in Iraq were all cleared as well, officials said.
Human rights activists have criticised Sanchez for authorising prisoner interrogation tactics more harsh than accepted army practice, including painful "stress positions" and using guard dogs to exploit "Arab fear of dogs".
Asked for his assessment of Sanchez, Rumsfeld said: "Well, he's obviously an officer who is serving in an important, responsible position. And he served in an important, responsible position in Iraq before that, and has an excellent reputation".
Myers added, "I've said in the past that General Sanchez did a terrific job inside Iraq. I'm on the record for that".
The US military court-martial of a leading figure in the Abu Ghraib scandal, Private First Class Lynndie England, is set to begin next Tuesday at Fort Hood in Texas.
England, seen in photos grinning behind a pile of naked Iraqi prisoners and holding an Iraqi detainee on a leash, faces nine charges.
- Reuters
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I want to hang a planter (with flowers) from our soffit. How do I determine where the best place to do this is? I'm assuming I need to find the corner of a truss or something. Thanks.
I want to hang a planter (with flowers) from our soffit. How do I determine where the best place to do this is? I'm assuming I need to find the corner of a truss or something. Thanks.
chances are that you can see a nail or screw through the paint on the soffit where it screws into the rafter tail. once you find one you can find them all, probably 16" or 24" on center. then screw your hook into one of the rafter tails through the soffit and you're good to go.
Well, I ended up prying back a piece of the vinyl from the soffit and felt around and found what appeared to be plywood. So I got an eyehook that was long enough, drilled a hole, screwed the eyehook in, and voila. Works perfect and seems very sturdy.
For small plants this may be enough, but it's best to screw into the framing members which should be either 16 or 24 inches apart through the entire run. You're going to need that better support with heavier plants! You may have to snap out a few panels to find that framing- look for lines of nails in the plywood perpendicular to the house, then drive a nail 16 inches away to see what you hit. If there's just plywood there, try 24 inches from the first one and you should hit framing.
I don't want you to see your plants destroyed from falling!
Katiesommer - If you your plywood is in good shape and your plants are not too heavy I would just screw directly into the plywood. If you think your planters are a little heavy get a clip from your local hardware store that folds out once you screw it into your plywood. The clip will help keep it secure from falling.
It is never a good idea to place any load in a place which was not designed for it to be carried. I can tell you from experience that the carpenters did not intend their nailing of the soffit to carry much more than the soffit material itself, and that neither was soffit designed to be used in this manner, so no matter what kind of hanger you use if it doesn't directly transfer the load to framing members, you're asking for trouble. You may get by with a light load but the average hanging planter soaked with rain is pretty heavy and pushing the limits- especially if 3/8" plywood was used for the soffit as it commonly is.


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Reshmi tying knot with Amit
"Amit and I have been dating each other past 5 years. 5 years is long enough to know that one can spend his/her life with their partners. I know Amit in and out. He is the kind of guy always dreamt of. We understand each other very well and that is what makes a relationship", says Reshmi confidently.
Reshmi still is not confirming her marriage dates. She says, "It is for our parents to decide. I always wanted to get married to a marwari and after meeting Amit I knew he is the one. The one with whom I can spend my life with. I am in love with the marwari culture and I am happy Amit is a marwari". Reshmi says she hope to have a big fat Indian wedding.
"I want to do it in a very grand way. A wedding of 11 days with all my friends and relatives," says the actress. So will it follow Amit's marwari culture or Reshmi's Bengali tradition? "I really haven't thought about it as of now! But shaadi ke baad ladke ke yahaan ka culture hi follow kiya jaata hai," she says.
Reshmi says she will let everyone know once her marriage dates are out. "I am too excited to know when I am getting married and so I will tell the whole world too". Amit and Reshmi met at Namit Kapoor's acting school. Soon after that they became very good friends and gradually they became a couple. The duo can be presently seen swirling to the tunes of Bollywood numbers on Nach Baliye 4.


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2 – Print Without Heartache – The only message worse than "cleaning the heads" is "cartridge empty." With a laser printer you'll get 5 to 25 times more printouts before you will have to perform any kind of maintenance. Printing becomes a joy not a chore.
3 – Print From Anywhere on Your Network – Whether from the living room, kitchen or pool house (yeah right), your network connected laser printer is ready to go.
4 – Printing For Everyone – Anyone with access to your network can, and will want to go laser in your house. Don't deny them the joy.
5 – Print Clearly – Laser printers today produce publication ready output. At better than 600 dots per inch and with the ability to print at 2400 dots per inch, images, charts and text are very crisp and clear.
6 – Print In Color – Yes, in beautiful, vivid color, the way printing should be. Black and white is so 1990's.
7 – Print Without Breaking the Bank – Prices for color laser printers have come down drastically and with some careful shopping, you can have a very good network ready color laser printer for around $300.
Here's a link to a PC World review of color laser printers. The one I've been using for the past 3 months is the Brother HL-4040CN. I shopped around and waited a couple of weeks for it to go on sale at a national office supply chain store in my town where Ipicked it up for $299.
It's time. Get a color laser printer for your home and turn a chore into a joy.General home laser network printing


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Will be discontinued as of December 31, 2014.
Most block heating technology employs natural convection within the engine block's system to drive circulation—more commonly known as thermosiphon. This program promotes the replacement of thermosiphon style engine block heaters with pump driven circulation units, which reduces overall block temperature. Because it also decreases the heat transfer rate from the block to the environment, it can reduce overall block heater energy consumption, which is tied to the circulation method.
Because thermosiphon heaters require temperature variation to drive circulation warmer coolant rises to the top of the block with cooler coolant descending to the lower sections of the block. The coolant in the lower portions of the block must meet the minimum block temp requirements, which means the coolant in the upper parts of the block will exceed the minimum temperature requirements. A pump driven heater does not require temperature difference to drive flow. The coolant temperature is more uniform throughout the block. This reduces the overall average block temperature and minimizes the driving force affecting heat transfer.
Click here (PDF) for more details and to print a Standby Generator Block Heater Program Incentive Agreement. Please note: you can fill out this form online prior to printing.
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