EndoNet: an information resource about regulatory networks of cell-to-cell communication.

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/.

Comparative analysis of topological patterns in different mammalian networks.

We have systematically analyzed various topological patterns comprising 1, 2 or 3 nodes in the mammalian metabolic, signal transduction and transcription networks: These patterns were analyzed with regard to their frequency and statistical over-representation in each network, as well as to their topological significance for the coherence of the networks. The latter property was evaluated using the pairwise disconnectivity index, which we have recently introduced to quantify how critical network components are for the internal connectedness of a network. The 1-node pattern made up by a vertex with a self-loop has been found to exert particular properties in all three networks. In general, vertices with a self-loop tend to be topologically more important than other vertices. Moreover, self-loops have been found to be attached to most 2-node and 3-node patterns, thereby emphasizing a particular role of self-loop components in the architectural organization of the networks. For none of the networks, a positive correlation between the mean topological significance and the Z-score of a pattern could be observed. That is, in general, motifs are not per se more important for the overall network coherence than patterns that are not over-represented. All 2- and 3-node patterns that are over-represented and thus qualified as motifs in all three networks exhibit a loop structure. This intriguing observation can be viewed as an advantage of loop-like structures in building up the regulatory circuits of the whole cell. The transcription network has been found to differ from the other networks in that (i) self-loops play an even higher role, (ii) its binary loops are highly enriched with self-loops attached, and (iii) feed-back loops are not over-represented. Metabolic networks reveal some particular topological properties which may reflect the fact that metabolic paths are, to a large extent, reversible. Interestingly, some of the most important 3-node patterns of both the transcription and the signaling network can be concatenated to subnetworks comprising many genes that play a particular role in the regulation of cell proliferation.

The pairwise disconnectivity index as a new metric for the topological analysis of regulatory networks.

BACKGROUND: Currently, there is a gap between purely theoretical studies of the topology of large bioregulatory networks and the practical traditions and interests of experimentalists. While the theoretical approaches emphasize the global characterization of regulatory systems, the practical approaches focus on the role of distinct molecules and genes in regulation. To bridge the gap between these opposite approaches, one needs to combine 'general' with 'particular' properties and translate abstract topological features of large systems into testable functional characteristics of individual components. Here, we propose a new topological parameter--the pairwise disconnectivity index of a network's element - that is capable of such bridging. RESULTS: The pairwise disconnectivity index quantifies how crucial an individual element is for sustaining the communication ability between connected pairs of vertices in a network that is displayed as a directed graph. Such an element might be a vertex (i.e., molecules, genes), an edge (i.e., reactions, interactions), as well as a group of vertices and/or edges. The index can be viewed as a measure of topological redundancy of regulatory paths which connect different parts of a given network and as a measure of sensitivity (robustness) of this network to the presence (absence) of each individual element. Accordingly, we introduce the notion of a path-degree of a vertex in terms of its corresponding incoming, outgoing and mediated paths, respectively. The pairwise disconnectivity index has been applied to the analysis of several regulatory networks from various organisms. The importance of an individual vertex or edge for the coherence of the network is determined by the particular position of the given element in the whole network. CONCLUSION: Our approach enables to evaluate the effect of removing each element (i.e., vertex, edge, or their combinations) from a network. The greatest potential value of this approach is its ability to systematically analyze the role of every element, as well as groups of elements, in a regulatory network.

EndoNet: an information resource about endocrine networks.

EndoNet is a new database that provides information about the components of endocrine networks and their relations. It focuses on the endocrine cell-to-cell communication and enables the analysis of intercellular regulatory pathways in humans. In the EndoNet data model, two classes of components span a bipartite directed graph. One class represents the hormones (in the broadest sense) secreted by defined donor cells. The other class consists of the acceptor or target cells expressing the corresponding hormone receptors. The identity and anatomical environment of cell types, tissues and organs is defined through references to the CYTOMER ontology. With the EndoNet user interface, it is possible to query the database for hormones, receptors or tissues and to combine several items from different search rounds in one complex result set, from which a network can be reconstructed and visualized. For each entity, a detailed characteristics page is available. Some well-established endocrine pathways are offered as showcases in the form of predefined result sets. These sets can be used as a starting point for a more complex query or for obtaining a quick overview. The EndoNet database is accessible at http://endonet.bioinf.med.uni-goettingen.de/.

TRANSPATH: an information resource for storing and visualizing signaling pathways and their pathological aberrations.

TRANSPATH is a database about signal transduction events. It provides information about signaling molecules, their reactions and the pathways these reactions constitute. The representation of signaling molecules is organized in a number of orthogonal hierarchies reflecting the classification of the molecules, their species-specific or generic features, and their post-translational modifications. Reactions are similarly hierarchically organized in a three-layer architecture, differentiating between reactions that are evidenced by individual publications, generalizations of these reactions to construct species-independent 'reference pathways' and the 'semantic projections' of these pathways. A number of search and browse options allow easy access to the database contents, which can be visualized with the tool PathwayBuildertrade mark. The module PathoSign adds data about pathologically relevant mutations in signaling components, including their genotypes and phenotypes. TRANSPATH and PathoSign can be used as encyclopaedia, in the educational process, for vizualization and modeling of signal transduction networks and for the analysis of gene expression data. TRANSPATH Public 6.0 is freely accessible for users from non-profit organizations under http://www.gene-regulation.com/pub/databases.html.

Integrative content-driven concepts for bioinformatics "beyond the cell".

Bioinformatics has delivered great contributions to genome and genomics research, without which the world-wide success of this and other global ('omics') approaches would not have been possible. More recently, it has developed further towards the analysis of different kinds of networks thus laying the foundation for comprehensive description, analysis and manipulation of whole living systems in modern "systems biology". The next step which is necessary for developing a systems biology that deals with systemic phenomena is to expand the existing and develop new methodologies that are appropriate to characterize intercellular processes and interactions without omitting the causal underlying molecular mechanisms. Modelling the processes on the different levels of complexity involved requires a comprehensive integration of information on gene regulatory events, signal transduction pathways, protein interaction and metabolic networks as well as cellular functions in the respective tissues / organs.

TRANSPATH: an integrated database on signal transduction and a tool for array analysis.

TRANSPATH is a database system about gene regulatory networks that combines encyclopedic information on signal transduction with tools for visualization and analysis. The integration with TRANSFAC, a database about transcription factors and their DNA binding sites, provides the possibility to obtain complete signaling pathways from ligand to target genes and their products, which may themselves be involved in regulatory action. As of July 2002, the TRANSPATH Professional release 3.2 contains about 9800 molecules, >1800 genes and >11 400 reactions collected from approximately 5000 references. With the ArrayAnalyzer, an integrated tool has been developed for evaluation of microarray data. It uses the TRANSPATH data set to identify key regulators in pathways connected with up- or down-regulated genes of the respective array. The key molecules and their surrounding networks can be viewed with the PathwayBuilder, a tool that offers four different modes of visualization. More information on TRANSPATH is available at http://www.biobase.de/pages/products/databases.html.

Topology of mammalian transcription networks.

We present a first attempt to evaluate the generic topological principles underlying the mammalian transcriptional regulatory networks. Transcription networks, TN, studied here are represented as graphs where vertices are genes coding for transcription factors and edges are causal links between the genes, each edge combining both gene expression and trans-regulation events. Two transcription networks were retrieved from the TRANSPATH database: The first one, TN_RN, is a 'complete' transcription network referred to as a reference network. The second one, TN_p53, displays a particular transcriptional sub-network centered at p53 gene. We found these networks to be fundamentally non-random and inhomogeneous. Their topology follows a power-law degree distribution and is best described by the scale-free model. Shortest-path-length distribution and the average clustering coefficient indicate a small-world feature of these networks. The networks show the dependence of the clustering coefficient on the degree of a vertex, thereby indicating the presence of hierarchical modularity. Clear positive correlation between the values of betweenness and the degree of vertices has been observed in both networks. The top list of genes displaying high degree and high betweennes, such as p53, c-fos, c-jun and c-myc, is enriched with genes that are known as having tumor-suppressor or proto-oncogene properties, which supports the biological significance of the identified key topological elements.

Consistent re-modeling of signaling pathways and its implementation in the TRANSPATH database.

The data model of the signaling pathways database TRANSPATH has been re-engineered to a three-layer model comprising experimental evidences and summarized pathway information, both in a mechanistically detailed manner, and a "semantic" projection for the abstract overview. Each molecule is described in the context of a certain reaction in the multidimensional space of posttranslational modification, molecular family relationships, and the biological species of its origin. The new model makes the data better suitable for reconstructing signaling pathways and networks and mapping expression data, for instance from microarray experiments, onto regulatory networks.

An approach to evaluate the topological significance of motifs and other patterns in regulatory networks.

BACKGROUND: The identification of network motifs as statistically over-represented topological patterns has become one of the most promising topics in the analysis of complex networks. The main focus is commonly made on how they operate by means of their internal organization. Yet, their contribution to a network's global architecture is poorly understood. However, this requires switching from the abstract view of a topological pattern to the level of its instances. Here, we show how a recently proposed metric, the pairwise disconnectivity index, can be adapted to survey if and which kind of topological patterns and their instances are most important for sustaining the connectivity within a network. RESULTS: The pairwise disconnectivity index of a pattern instance quantifies the dependency of the pairwise connections between vertices in a network on the presence of this pattern instance. Thereby, it particularly considers how the coherence between the unique constituents of a pattern instance relates to the rest of a network. We have applied the method exemplarily to the analysis of 3-vertex topological pattern instances in the transcription networks of a bacteria (E. coli), a unicellular eukaryote (S. cerevisiae) and higher eukaryotes (human, mouse, rat). We found that in these networks only very few pattern instances break lots of the pairwise connections between vertices upon the removal of an instance. Among them network motifs do not prevail. Rather, those patterns that are shared by the three networks exhibit a conspicuously enhanced pairwise disconnectivity index. Additionally, these are often located in close vicinity to each other or are even overlapping, since only a small number of genes are repeatedly present in most of them. Moreover, evidence has gathered that the importance of these pattern instances is due to synergistic rather than merely additive effects between their constituents. CONCLUSION: A new method has been proposed that enables to evaluate the topological significance of various connected patterns in a regulatory network. Applying this method onto transcriptional networks of three largely distinct organisms we could prove that it is highly suitable to identify most important pattern instances, but that neither motifs nor any pattern in general appear to play a particularly important role per se. From the results obtained so far, we conclude that the pairwise disconnectivity index will most likely prove useful as well in identifying other (higher-order) pattern instances in transcriptional and other networks.

Integrating pathway data for systems pathology.

The HumanPSD database on the complete proteomes of human, mouse and rat has been integrated with the databases TRANSFAC on gene regulation and TRANSPATH on signal transduction to provide a comprehensive systems biological platform for these organisms. As a next step, integration with PathoDB and PathoSign on pathologically relevant mutations is planned together with an extension beyond the limits of the individual cell, towards intercellular networks, by integrating the database EndoNet on hormonal networks as well. The overall aim is to come up with a platform that is suitable to provide knowledge for systems pathology, i. e. a system-wide modeling of pathological states and their development.

TRANSPATH--a high quality database focused on signal transduction.

TRANSPATH can either be used as an encyclopedia, for both specific and general information on signal transduction, or can serve as a network analyser. Therefore, three modules have been created: the first one is the data, which have been manually extracted, mostly from the primary literature; the second is PathwayBuilder, which provides several different types of network visualization and hence faciliates understanding; the third is ArrayAnalyzer, which is particularly suited to gene expression array interpretation, and is able to identify key molecules within signalling networks (potential drug targets). These key molecules could be responsible for the coordinated regulation of downstream events. Manual data extraction focuses on direct reactions between signalling molecules and the experimental evidence for them, including species of genes/proteins used in individual experiments, experimental systems, materials and methods. This combination of materials and methods is used in TRANSPATH to assign a quality value to each experimentally proven reaction, which reflects the probability that this reaction would happen under physiological conditions. Another important feature in TRANSPATH is the inclusion of transcription factor-gene relations, which are transferred from TRANSFAC, a database focused on transcription regulation and transcription factors. Since interactions between molecules are mainly direct, this allows a complete and stepwise pathway reconstruction from ligands to regulated genes. More information is available at www.biobase.de/pages/products/databases.html.