GeneNet in 2005

The GeneNet system is designed for collection and analysis of the data on gene and metabolic networks, signal transduction pathways and kinetic characteristics of elementary processes. In the past 2 years, the GeneNet structure was considerably improved: (i) the current version of the database is now implemented using ORACLE9i; (ii) the capacities to describe the structure of the protein complexes and the interactions between the units are increased; (iii) two tables with kinetic constants and more detailed descriptions of certain reactions were added; and (iv) a module for kinetic modeling was supplemented. The current SRS release of the GeneNet database contains 37 graphical maps of gene networks, as well as descriptions of 1766 proteins, 1006 genes, 241 small molecules and 3254 relationships between gene network units, and 552 kinetic constants. Information distributed between 16 interlinked tables was obtained by annotating 1980 journal publications. SRS release of the GeneNet database, the graphical viewer and the modeling section are available at http://wwwmgs.bionet.nsc.ru/mgs/gnw/genenet/

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

Modelling of molecular genetic systems in bacterial cell 45 AROMATIC AMINO ACID BIOSYNTHESIS IN ESCHERICHIA COLI: GENERALIZED HILL FUNCTION MODEL OF THE TRYPTOPHAN- SENSITIVE 3-DEOXY-D-ARABINO- HEPTULOSONATE-7-PHOSPHATE SYNTHASE REACTION DEMONSTRATE COMPL

SUMMARY Motivation: Development of an in silico cell as a computer resource for simulation and analysis of processes within living cells is an urgent task of systems biology and computational biology. Results: By using the GeneNet technology, we reproduced the gene network of the regulation of aromatic amino acid biosynthesis in the E. coli cell. Mathematical models were constructed by the method of generalized Hill functions. The models describe the efficiency of enzymatic systems and regulation of expression of related genes. Mathematical model of the enzyme tryptophan-sensitive 3-deoxy-d-arabinoheptulosonate-7-phosphate synthase reaction demonstrate complicated mechanism. Availability: Models are available on request. The diagram of the gene network regulating aromatic amino acid biosynthesis in E. coli is available through the GeneNet viewer a

GENE NETWORK RECONSTRUCTION AND MATHEMATICAL MODELING OF SALVAGE PATHWAYS: REGULATION OF ADENINE PHOSPHORIBOSYLTRANSFERASE ACTIVITY BY STRUCTURALLY SIMILAR SUBSTRATES

SUMMARY Motivation: Development of an in silico cell, a computer resource for modeling and analysis of physiological processes is an urgent task of systems biology and computational biology. Mathematical modeling of the genetic regulation of cell metabolism pathways, in particular, salvage pathways, is an important problem to be solved as part of this line of work. Results: By using the GeneNet technology, we reproduced the gene network of the regulation of salvage pathways in the E. coli cell. Mathematical models were constructed by the method of generalized Hill functions to describe the efficiency of enzyme systems and regulation of expression of genes coding for these enzymes. Availability: The diagram of the gene network is available through the GeneNet viewer at http://wwwmgs.bionet.nsc.ru/mgs/gnw/genenet/viewer/index.shtml. Models are available on request

CellCircuits: a database of protein network models

CellCircuits () is an open-access database of molecular network models, designed to bridge the gap between databases of individual pairwise molecular interactions and databases of validated pathways. CellCircuits captures the output from an increasing number of approaches that screen molecular interaction networks to identify functional subnetworks, based on their correspondence with expression or phenotypic data, their internal structure or their conservation across species. This initial release catalogs 2019 computationally derived models drawn from 11 journal articles and spanning five organisms (yeast, worm, fly, Plasmodium falciparum and human). Models are available either as images or in machine-readable formats and can be queried by the names of proteins they contain or by their enriched biological functions. We envision CellCircuits as a clearinghouse in which theorists may distribute or revise models in need of validation and experimentalists may search for models or specific hypotheses relevant to their interests. We demonstrate how such a repository of network models is a novel systems biology resource by performing several meta-analyses not currently possible with existing databases

Innovative Algorithms and Evaluation Methods for Biological Motif Finding

Biological motifs are defined as overly recurring sub-patterns in biological systems. Sequence motifs and network motifs are the examples of biological motifs. Due to the wide range of applications, many algorithms and computational tools have been developed for efficient search for biological motifs. Therefore, there are more computationally derived motifs than experimentally validated motifs, and how to validate the biological significance of the ‘candidate motifs’ becomes an important question. Some of sequence motifs are verified by their structural similarities or their functional roles in DNA or protein sequences, and stored in databases. However, biological role of network motifs is still invalidated and currently no databases exist for this purpose. In this thesis, we focus not only on the computational efficiency but also on the biological meanings of the motifs. We provide an efficient way to incorporate biological information with clustering analysis methods: For example, a sparse nonnegative matrix factorization (SNMF) method is used with Chou-Fasman parameters for the protein motif finding. Biological network motifs are searched by various clustering algorithms with Gene ontology (GO) information. Experimental results show that the algorithms perform better than existing algorithms by producing a larger number of high-quality of biological motifs. In addition, we apply biological network motifs for the discovery of essential proteins. Essential proteins are defined as a minimum set of proteins which are vital for development to a fertile adult and in a cellular life in an organism. We design a new centrality algorithm with biological network motifs, named MCGO, and score proteins in a protein-protein interaction (PPI) network to find essential proteins. MCGO is also combined with other centrality measures to predict essential proteins using machine learning techniques. We have three contributions to the study of biological motifs through this thesis; 1) Clustering analysis is efficiently used in this work and biological information is easily integrated with the analysis; 2) We focus more on the biological meanings of motifs by adding biological knowledge in the algorithms and by suggesting biologically related evaluation methods. 3) Biological network motifs are successfully applied to a practical application of prediction of essential proteins

Services for biological network feature detection

The complex environment of a living cell contains many molecules interacting in a variety of ways. Examples include the physical interaction between two proteins, or the biochemical interaction between an enzyme and its substrate. A challenge of systems biology is to understand the network of interactions between biological molecules, derived experimentally or computationally. Sophisticated dynamic modelling approaches provide detailed knowledge about single processes or individual pathways. However such methods are far less tractable for holistic cellular models, which are instead represented at the level of network topology. Current network analysis packages tend to be standalone desktop tools which rely on local resources and whose operations are not easily integrated with other software and databases. A key contribution of this thesis is an extensible toolkit of biological network construction and analysis operations, developed as web services. Web services are a distributed technology that enable machine-to-machine interaction over a network, and promote interoperability by allowing tools deployed on heterogeneous systems to interface. A conceptual framework has been created, which is realised practically through the proposal of a common graph format to standardise network data, and the investigation of open-source deployment technologies. Workflows are a graph of web services, allowing analyses to be carried out as part of a bigger software pipeline. They may be constructed using web services within the toolkit together with those from other providers, and can be saved, shared and reused, allowing biologists to construct their own complex queries over various tools and datasets, or execute pre-constructed workflows designed by expert bioinformaticians. Biologically relevant results have been produced as a result of this approach. One very interesting hypothesis has been generated regarding the regulation of yeast glycolysis by a protein found to interact with seven glycolytic enzymes. This has implied a potentially novel regulatory mechanism whereby the protein in question binds these enzymes to form an 'energy production unit'. Also of interest are workflows which identify termini (system inputs and outputs), and cycles, which are crucial for acquiring a physiological perspective on network behaviour