Anatolij Potapov

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 PathwayBuilder™. 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 .

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.

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.

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

BackgroundThe 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 networks 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.ResultsThe 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.ConclusionA 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.

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.

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 .

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.

Identifizierung kritischer Komponenten in genregulatorischen Netzwerken

The topological analysis of biological networks can help identify particularly relevant molecules or genes. For this, we have developed the concept of the pairwise disconnectivity index (PDI). This parameter indicates for each entity (node, edge or subgraph) in a network how many connections between any pair of nodes critically depend on this entity, as measured in a “virtual knock-out” experiment. The approach has been implemented as publicly available online service (http://diva.sybig.de).

Mining the genome and regulatory networks

Four experiments in which SPF Wistar rats were inoculated intrapleurally with asbestos or other materials are described. Mesotheliomata were observed in a considerable proportion of animals with all the samples of asbestos used and with a sample of brucite. A few were produced with synthetic aluminium silicate fibres and single ones with barium sulphate, glass powder and aluminium oxide. The risk of developing a mesothelioma at a given time after injection was approximately proportional to the dose. Of the UICC standard reference samples, crocidolite was the most carcinogenic and removal of the oils by benzene extraction did not alter the carcinogenicity of these samples. Chemical properties also seem unlikely to be the main factor producing mesotheliomata but the results support the hypothesis that the finer fibres are the more carcinogenic, and this is additional to the known aerodynamic advantage which the finer fibres have in penetrating to the periphery of the lung.A report on the 16th International Conference on Genome Informatics (GIW 2005), Yokohama, Japan, 19-21 December 2005.