currently , most efforts in systems biology are made on various aspects of intracellular regulation . great progress has been achieved in understanding the organization and functioning of various regulatory pathways and networks . several databases have been created to collect and organize the corresponding information on these special issues . representative examples of such databases and knowledge bases containing information about intracellular regulatory interactions are kegg ( 1,2 ) , bind ( 3 ) , dip ( 4 ) , amaze ( 5 ) , ecocyc ( 6 ) , genenet ( 7 ) , reactom ( 8) , transpath ( 9 ) and transfac ( 10 ) . altogether , they form the necessary basis for modeling and simulation of large intracellular networks . compared to this , large - scale intercellular molecular networking , which is of key importance for multicellular eukaryotes , has been largely neglected in these modeling attempts . in multicellular organisms , the functional activities of various cell types , tissues and organs need to be coordinated to provide proper functioning of the whole system ( 11 ) . a great body of information on hormones , growth factors , cytokines and other messengers involved in intercellular cross talking has been published ( 1215 ) . however , it is mainly about elementary cell  cell interactions , although some attempts to integrate such interactions into more complex regulatory pathways can be found in the literature as well ( 1215 ) . cell - to - cell regulatory interactions and networks of intercellular regulatory communication still remain challenges in the field of systems biology . the need to combine the available information on cell - to - cell interaction many of such intercellular regulatory pathways display the property of a cascade in which the initial signal is multiplied over several steps . endonet aims at modeling these network topologies and at bridging the existing gap between known genotypes and their molecular and clinical phenotypes , thus allowing the utilization of endonet in medical research . during the last years , the endonet database was subject to considerable expansion of its contents and improvements of its data organization and presentation . among the most prominent new features , the implementation of a hormone classification for browsing and the full embedding of our cytomer ontology on cells , tissues , organs and their developmental stages ( 17,18 ) will be described in this paper . historically , hormones were defined as substances produced and secreted by special endocrine glands . however , it is commonly accepted now that nearly all tissue types are capable of producing molecular signals that are informative for their direct neighbors or remote counterparts and alter their growth , function or metabolism ( 1315 ) . therefore , with the term we refer to a broad spectrum of biomolecules , which are used for cell - to - cell communication and help to coordinate complex biological functions such as growth and differentiation by directing gene expression , secretory and metabolic activities of various cells , tissues and organs . currently there are 637 hormones ( in the broadest sense ) in the endonet database ( table 1 ) . this group is highly heterogeneous in regard to the properties of such molecules , chemical structure and the type of action . table 1.contents of endonet in comparison with the previous releasecomponentsnumber of entries ( 17 october 2005)number of entries ( 15 september 2007)molecules  hormone109637  receptors117500cellular sources  cells / tissues112314relations  hormone receptor  acceptor cell2921555information sources  references2641926 contents of endonet in comparison with the previous release by considering the origin of hormones and their relations to the genome , all molecular messengers in endonet have been divided into two superfamilies . such  not genome - encoded hormones are typically small molecules that are synthesized by a set of metabolic pathways . most of them bind to intracellularly located receptors . in general , intracellular regulatory pathways that are activated by the hormones of this superfamily are relatively short and simple . the superfamily of  not genome - encoded hormones includes several families , many of which are further subdivided in subfamilies and sub - subfamilies . this may be illustrated with an example of the  androgens sub - subfamily all members of which ( 5-androstanediol , androstenedione , dehydro-3-epiandrosterone , dht and testosterone ) belong to the subfamily of steroids , which in turn is part of the sterol lipids family ( figure 1 ) . figure 1.classification of hormones in the endonet database . the fragment related to the subsubfamily of androgens the basic entities ( e.g. 5-androstanediol , androstenedione ) are clickable thereby enabling easy access to the contents of the database . the basic entities ( e.g. 5-androstanediol , androstenedione ) are clickable thereby enabling easy access to the contents of the database . the second superfamily includes peptide and protein hormones that are encoded in the genome by individual or multiple genes , in case that the messengers are multi - subunit proteins . it consists of multiple families , which are further divided into subfamilies and sub - subfamilies . thus , the family of cytokines includes multiple subfamilies and one of them , chemokines , consists of sub - subfamilies of c chemokines , cc chemokines and cxc chemokines . to become active , most of the peptide / protein hormones require more or less extensive posttranslational processing , e.g. leader peptide cleavage or cutting of a precursor polypeptide into a series of shorter poly- and/or oligopeptides . in all cases we are aware of , genome - encoded hormones initiate the corresponding cellular responses by binding to specific membrane - associated receptors . these receptors usually trigger more complex intracellular signaling cascades towards the nucleus ( 11,13 ) . to search hormone superfamilies all primary entities are linked to a detail page , thereby enabling easy access to the available descriptive information . we are aware that the hormone classification provided is just a starting point ; in particular most parts of the genome - encoded family are very flat and just listed alphabetically . work is in progress that identifies proper criteria for a structure - driven classification which may reflect as many functional features as possible . these classes form a hierarchy and are connected to each other through the relation isa ( 19 ) . the former combines all anatomical structures , whereas the latter contains notional abstractions like species or developmentalstage . although this structure provides for the inclusion of different species , most of the information present in cytomer relates to human . are the upper structure of the ontology ( left ) and a part of the lower hierarchy , exemplifying the partof / haspart relationship ( right ) . boxes denote classes , blue arrows indicate the hassubclass relationship and orange arrows indicate the haspart connection . are the upper structure of the ontology ( left ) and a part of the lower hierarchy , exemplifying the partof / haspart relationship ( right ) . boxes denote classes , blue arrows indicate the hassubclass relationship and orange arrows indicate the haspart connection . the components of the human body are subsumed in the class anatomicalentity , which contains fluids ( class humour ) , lacunae ( anatomicalcavity ) and the diverse body parts ( anatomicalbuildingblock ) . the latter class is divided into several subclasses , including but not restricted to organ , organpart , and cell . the relations between the classes are modeled either as class  subclass relations ( isa ) or as properties of the classes ( haspart ) . in cytomer , these relation types are modeled in a reciprocal way : thus isa is equivalent to hassubclass , and haspart is equivalent to ispartof . figure 2 shows the relations between the alpha cells of the langerhans islets and its related classes as an example . the diverse body components are also related to several physiological systems as well as to the different developmental stages ( carnegie stages in case of human ) . the latter connection models the different temporal occurrences of the entities described in the successive phases of the embryonic development . this allows to access cytomer via application programming interfaces ( api ) such as the jena - api and the protg - api . both are generic toolkits designed to handle any ontology based on owl . because of the need for functionality that is specific to an anatomical ontology , we have developed the cytomer connector , which uses the protg - api and provides simple access to cytomer ( figure 3 ) . in contrast to many other information resources which are accessible in endonet via hyperlinks , cytomer is directly embedded into the endonet user interface ( figure 4 ) . with the cytomer viewer , the user can explore the whole ontology by browsing through the graph of interest and analyze all connected anatomical properties . in spite of the integrated view on cytomer and endonet , from the technical point of view , both systems are technically independent and separately maintained by experts in the respective fields . all entries in endonet have been created by manual annotation and are extracted from original publications , monographs ( 1215 ) and the linked databases . as a tool for systems biology endonet focuses on the macroscopic view of the information flow . for additional detailed information on single components and events , data on genome - encoded entities , namely peptide / protein hormones and receptors , include references to external resources such as swiss - prot [ ( 20 ) , http://www.expasy.org/sprot/ ] , transpath ( 9 ) and humanpsd ( 21 ) and ncbi ( http://www.ncbi.nlm.nih.gov/ ) . data on not genome - encoded hormones include references to the databases kegg ( 1,2 ) , lipid maps [ ( 22 ) , http://www.lipidmaps.org/ ] and lipidbank ( http://lipidbank.jp/ ) . the annotation policy for endonet is to record the information as precisely as possible without generalization . the endocrine substances and receptors are assigned to cells , tissues and organs as specifically as possible . all genome - encoded molecules are represented in their active , i. e. processed or multisubunit form , although the links to external databases such as swiss - prot and humanpsd usually refer to the corresponding precursor or subunit polypeptides . in comparison with the previous release published in 2006 ( 16 ) , the number of entries for hormones , receptors and cells / tissues / organs and in particular the number of connections between these entities has significantly increased as shown in table 1 . in the publicly accessible version , there are no orphan entries for hormones and receptors : that is , all of them are linked to the corresponding receptor or ligand entities , respectively , thereby increasing the size and the edge density of the whole network . the contents of endonet can be browsed or searched . searching for a specific hormone or receptor can be done by name or synonym or a substring thereof . to facilitate retrieval of a hormone when just the family it belongs to but not the exact name is known , the newly implemented hormone classification offers an alternative search strategy ( figure 1 ) . for cells , tissues and organs the search algorithm includes the medical name and synonyms from cytomer ( figure 4 ) . the result of a query is a list of matching entities which can be added to a user - defined set to be combined with the results of further searches . from this set a network can be built using its items as starting points for up- and downstream searches . such a graph is displayed using graphviz ( 8) . hormones and receptors are shown as vertices grouped together into subgraphs that represent the cells / tissues / organs where they are secreted from or expressed in , respectively . all objects in endonet whether displayed textually or graphically are linked to a corresponding detail page . here this comprises alternative names , lists of connected endonet objects , links to external databases and to the original literature . some well - established endocrine pathways are offered in the form of  predefined sets. when selected , such sets can be used for obtaining a quick overview or as a starting point for more complex queries . by now endonet has reached a high level of coverage of existing knowledge about intercellular regulatory communication in humans . the usability was improved by adding meta - information to the core data , enabling the user to browse the anatomical hierarchy provided by cytomer and the newly introduced hormone classification . to unburden the user from the task to combine the relevant information from two independent resources , we did not just link cytomer to endonet , but rather truly embedded the ontology into the endonet interface . besides being an encyclopedia on hormones , receptors , various cells , tissues , organs and direct regulatory links between them , endonet can serve for modeling the integral organization of large intercellular regulatory networks . as mentioned , many entries , i.e. hormones and receptors , in endonet have links to external information resources including transpath ( 9 ) . the latter is one of the few databases providing information about signal transduction and gene expression in higher eukaryotes and in particular in mammals . we see a great advantage in combining these complementary databases into a synergistic system , which is capable of integrating multiple intracellular and intercellular regulatory networks . this system thus allows for making an integral analysis of human regulatory pathways by means of significantly extended level of molecular details ( 23,24 ) . in addition , we plan to expand the endonet view onto rodent components as soon as the human network has reached a more complete status . endonet represents an important contribution to systems biology approaches by exceeding the previous emphasis on intracellular objects and processes . as far as we know , endonet is the first and yet the only system capable of modeling large - scale intercellular regulatory networks in humans . the purpose of this database is to provide a useful resource for biomedical research in its different facets like pharmacologic research , diagnostics or therapeutic aspects . finally , endonet aims at bridging the existing gap between known genotypes and their molecular and clinical phenotypes , thus allowing utilization of endonet in medical research . considering the high complexity of this task , we would like to emphasize that endonet as well as cytomer are ongoing projects .
endonet is an information resource about intercellular regulatory communication . 
 it provides information about hormones , hormone receptors , the sources ( i.e. cells , tissues and organs ) where the hormones are synthesized and secreted , and where the respective receptors are expressed . 
 the database focuses on the regulatory relations between them . 
 an elementary communication is displayed as a causal link from a cell that secretes a particular hormone to those cells which express the corresponding hormone receptor and respond to the hormone . 
 whenever expression , synthesis and/or secretion of another hormone are part of this response , it renders the corresponding cell an internal node of the resulting network . 
 this intercellular communication network coordinates the function of different organs . 
 therefore , the database covers the hierarchy of cellular organization of tissues and organs as it has been modeled in the cytomer ontology , which has now been directly embedded into endonet . 
 the user can query the database ; the results can be used to visualize the intercellular information flow . 
 a newly implemented hormone classification enables to browse the database and may be used as alternative entry point . 
 endonet is accessible at : http://endonet.bioinf.med.uni-goettingen.de/