Representing and analysing molecular and cellular function in the computer

Determining the biological function of a myriad of genes, and understanding how they interact to yield a living cell, is the major challenge of the post genome-sequencing era. The complexity of biological systems is such that this cannot be envisaged without the help of powerful computer systems capable of representing and analysing the intricate networks of physical and functional interactions between the different cellular components. In this review we try to provide the reader with an appreciation of where we stand in this regard. We discuss some of the inherent problems in describing the different facets of biological function, give an overview of how information on function is currently represented in the major biological databases, and describe different systems for organising and categorising the functions of gene products. In a second part, we present a new general data model, currently under development, which describes information on molecular function and cellular processes in a rigorous manner. The model is capable of representing a large variety of biochemical processes, including metabolic pathways, regulation of gene expression and signal transduction. It also incorporates taxonomies for categorising molecular entities, interactions and processes, and it offers means of viewing the information at different levels of resolution, and dealing with incomplete knowledge. The data model has been implemented in the database on protein function and cellular processes 'aMAZE' (http://www.ebi.ac.uk/research/pfbp/), which presently covers metabolic pathways and their regulation. Several tools for querying, displaying, and performing analyses on such pathways are briefly described in order to illustrate the practical applications enabled by the model

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

The database of quantitative cellular signaling: management and analysis of chemical kinetic models of signaling networks

Motivation: Analysis of cellular signaling interactions is expected to pose an enormous informatics challenge, perhaps even larger than analyzing the genome. The complex networks arising from signaling processes are traditionally represented as block diagrams. A key step in the evolution toward a more quantitative understanding of signaling is to explicitly specify the kinetics of all chemical reaction steps in a pathway. Technical advances in proteomics and high-throughput protein interaction assays promise a flood of such quantitative data. While annotations, molecular information and pathway connectivity have been compiled in several databases, and there are several proposals for general cell model description languages, there is currently little experience with databases of chemical kinetics and reaction level models of signaling networks. Results: The Database of Quantitative Cellular Signaling is a repository of models of signaling pathways. It is intended both to serve the growing field of chemical-reaction level simulation of signaling networks, and to anticipate issues in large-scale data management for signaling chemistry

An automated method for finding molecular complexes in large protein interaction networks

BACKGROUND: Recent advances in proteomics technologies such as two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of biomolecular interaction networks. Initial mapping efforts have already produced a wealth of data. As the size of the interaction set increases, databases and computational methods will be required to store, visualize and analyze the information in order to effectively aid in knowledge discovery. RESULTS: This paper describes a novel graph theoretic clustering algorithm, "Molecular Complex Detection" (MCODE), that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes. The method is based on vertex weighting by local neighborhood density and outward traversal from a locally dense seed protein to isolate the dense regions according to given parameters. The algorithm has the advantage over other graph clustering methods of having a directed mode that allows fine-tuning of clusters of interest without considering the rest of the network and allows examination of cluster interconnectivity, which is relevant for protein networks. Protein interaction and complex information from the yeast Saccharomyces cerevisiae was used for evaluation. CONCLUSION: Dense regions of protein interaction networks can be found, based solely on connectivity data, many of which correspond to known protein complexes. The algorithm is not affected by a known high rate of false positives in data from high-throughput interaction techniques. The program is available from

Genetic networks of antibacterial responses of eukaryotic cells. Bioinformatics analysis and modeling

This work describes the development of new methods to construction of promoter models as one of necessary steps of regulatory networks construction. Identification of characteristic promoter features shows the role of specific transcription factors (TFs) in triggering the response, which in turn sheds light on the signaling pathways activating these TFs. Treating reported results of microarray analyses together with other available information about the genes expressed in different cellular systems under consideration, we search for distinguishing features of the promoters of coexpressed genes. The application of such promoter models enables to identify additional candidate genes belonging to the same regulatory network. Four novel approaches are presented in this work: (i) subtractive approach to matrix generation; (ii) distance distribution approach; (iii) "seed" sets approach; (iv) complementary pairs approach. These approaches help to solve serious problems in promoter model construction such as the doubtful reliability of positive training sets ("seed" sets approach) and lack of knowledge about the exact signaling pathways triggering the gene expression (complementary pairs approach); the subtractive approach to matrix generation allows to refine positional weight matrices (PWM) for heterogeneous sets of binding sites, thus to improve the PWM search for single TFBS. A significant improvement of the specificity of promoter analysis has been achieved by applying statistical methods for characterizing TFBS combinations at over-represented distances rather than the mere identification of single potential TFBS (distance distributions approach). The newly developed methods were applied to the description of four defensive eukaryotic systems in terms of transcription regulation. The obtained models enabled us to gain better insights into the pathways of the corresponding signaling networks.Diese Arbeit beschreibt die Entwicklung mehrerer neuer Methoden zur Konstruktion von Promotormodellen als einen der notwendigen Schritte zur Konstruktion regulatorischer Netzwerke. Die Identifizierung charakteristischer Eigenschaften von Promotoren zeigt die Rolle bestimmter Transkriptionsfaktoren (TF) beim Auslösen spezifischer Antworten auf, was wiederum Aufschluss über die Signalwege zur Aktivierung dieser TF gibt. Durch Verarbeitung von Ergebnissen aus Microarray-Analysen zusammen mit weiteren verfügbaren Informationen über die in den betrachteten zellulären Systemen exprimierten Gene suchen wir nach kennzeichnenden Eigenschaften koregulierter Promotoren. Die Applikation solcher Promotermodelle ermöglicht die Identifizierung zusätzlicher Kandidatengene, die demselben regulatorischen Netzwerk angehören. Vier neue Ansätze werden in dieser Arbeit präsentiert: (i) der subtraktive Ansatz zur Matrixerzeugung; (ii) der Distanzverteilungsansatz; (iii) der "seed"-Set-Ansatz; (iv) der Ansatz komplementärer Paare. Diese Ansätze helfen, beträchtliche Probleme der Promotormodellkonstruktion zu lösen, wie die zweifelhafte Zuverlässigkeit positiver Trainingsets ("seed"-Set-Ansatz) und der Mangel an Wissen über die präzisen Signalwege, die bestimmte Genexpressionsereignisse auslösen (Ansatz komplementärer Paare). Der subtraktive Ansatz zur Matrixerzeugung erlaubt, Positionsgewichtungsmatrizen (PWM) für heterogene Sets von Bindungsstellen zu verfeinern und dadurch die PWM-Suche für einzelne TFBSs zur verbessern. Eine signifikante Verbesserung der Spezifität der Promotoranalyse wurde durch die Anwendung statistischer Methoden zur Charakterisierung von TFBS-Kombinationen in überrepräsentierten Distanzen anstelle der bloßen Identifizierung einzelner potentieller TFBSs erreicht. Die neuentwickelten Methoden wurden zur Beschreibung von vier eukaryotischen Abwehrsystemen verwendet. Die erhaltenen Modelle eröffneten tiefergehende Einsichten in die Pfade der zugehörigen Signalnetzwerke

In-silico-Systemanalyse von Biopathways

Chen M. In silico systems analysis of biopathways. Bielefeld (Germany): Bielefeld University; 2004.In the past decade with the advent of high-throughput technologies, biology has migrated from a descriptive science to a predictive one. A vast amount of information on the metabolism have been produced; a number of specific genetic/metabolic databases and computational systems have been developed, which makes it possible for biologists to perform in silico analysis of metabolism. With experimental data from laboratory, biologists wish to systematically conduct their analysis with an easy-to-use computational system. One major task is to implement molecular information systems that will allow to integrate different molecular database systems, and to design analysis tools (e.g. simulators of complex metabolic reactions). Three key problems are involved: 1) Modeling and simulation of biological processes; 2) Reconstruction of metabolic pathways, leading to predictions about the integrated function of the network; and 3) Comparison of metabolism, providing an important way to reveal the functional relationship between a set of metabolic pathways. This dissertation addresses these problems of in silico systems analysis of biopathways. We developed a software system to integrate the access to different databases, and exploited the Petri net methodology to model and simulate metabolic networks in cells. It develops a computer modeling and simulation technique based on Petri net methodology; investigates metabolic networks at a system level; proposes a markup language for biological data interchange among diverse biological simulators and Petri net tools; establishes a web-based information retrieval system for metabolic pathway prediction; presents an algorithm for metabolic pathway alignment; recommends a nomenclature of cellular signal transduction; and attempts to standardize the representation of biological pathways. Hybrid Petri net methodology is exploited to model metabolic networks. Kinetic modeling strategy and Petri net modeling algorithm are applied to perform the processes of elements functioning and model analysis. The proposed methodology can be used for all other metabolic networks or the virtual cell metabolism. Moreover, perspectives of Petri net modeling and simulation of metabolic networks are outlined. A proposal for the Biology Petri Net Markup Language (BioPNML) is presented. The concepts and terminology of the interchange format, as well as its syntax (which is based on XML) are introduced. BioPNML is designed to provide a starting point for the development of a standard interchange format for Bioinformatics and Petri nets. The language makes it possible to exchange biology Petri net diagrams between all supported hardware platforms and versions. It is also designed to associate Petri net models and other known metabolic simulators. A web-based metabolic information retrieval system, PathAligner, is developed in order to predict metabolic pathways from rudimentary elements of pathways. It extracts metabolic information from biological databases via the Internet, and builds metabolic pathways with data sources of genes, sequences, enzymes, metabolites, etc. The system also provides a navigation platform to investigate metabolic related information, and transforms the output data into XML files for further modeling and simulation of the reconstructed pathway. An alignment algorithm to compare the similarity between metabolic pathways is presented. A new definition of the metabolic pathway is proposed. The pathway defined as a linear event sequence is practical for our alignment algorithm. The algorithm is based on strip scoring the similarity of 4-hierarchical EC numbers involved in the pathways. The algorithm described has been implemented and is in current use in the context of the PathAligner system. Furthermore, new methods for the classification and nomenclature of cellular signal transductions are recommended. For each type of characterized signal transduction, a unique ST number is provided. The Signal Transduction Classification Database (STCDB), based on the proposed classification and nomenclature, has been established. By merging the ST numbers with EC numbers, alignments of biopathways are possible. Finally, a detailed model of urea cycle that includes gene regulatory networks, metabolic pathways and signal transduction is demonstrated by using our approaches. A system biological interpretation of the observed behavior of the urea cycle and its related transcriptomics information is proposed to provide new insights for metabolic engineering and medical care

Computational study of therapeutic targets and ADME-Associated proteins and application in drug design

Ph.DDOCTOR OF PHILOSOPH