An essential prerequisite for any systems-level understanding of cellular functions is to correctly uncover and annotate all functional interactions among proteins in the cell. Toward this goal, remarkable progress has been made in recent years, both in terms of experimental measurements and computational prediction techniques. However, public efforts to collect and present protein interaction information have struggled to keep up with the pace of interaction discovery, partly because protein-protein interaction information can be error-prone and require considerable effort to annotate. Here, we present an update on the online database resource Search Tool for the Retrieval of Interacting Genes (STRING); it provides uniquely comprehensive coverage and ease of access to both experimental as well as predicted interaction information. Interactions in STRING are provided with a confidence score, and accessory information such as protein domains and 3D structures is made available, all within a stable and consistent identifier space. New features in STRING include an interactive network viewer that can cluster networks on demand, updated on-screen previews of structural information including homology models, extensive data updates and strongly improved connectivity and integration with third-party resources. Version 9.0 of STRING covers more than 1100 completely sequenced organisms; the resource can be reached at http://string-db.org.

https://www.zora.uzh.ch/id/eprint/41629/4/21045058_V.pdf

The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored

Flux balance analysis is a mathematical approach for analyzing the flow of metabolites through a metabolic network. This primer covers the theoretical basis of the approach, several practical examples and a software toolbox for performing the calculations.

/pdf/what-is-flux-balance-analysis-4wuuwjci7s.pdf

What is flux balance analysis

Motivation: Simulation and modeling is becoming a standard approach to understand complex biochemical processes. Therefore, there is a big need for software tools that allow access to diverse simulation and modeling methods as well as support for the usage of these methods.

Results: Here, we present COPASI, a platform-independent and user-friendly biochemical simulator that offers several unique features. We discuss numerical issues with these features; in particular, the criteria to switch between stochastic and deterministic simulation methods, hybrid deterministic--stochastic methods, and the importance of random number generator numerical resolution in stochastic simulation.

Availability: The complete software is available in binary (executable) for MS Windows, OS X, Linux (Intel) and Sun Solaris (SPARC), as well as the full source code under an open source license from http://www.copasi.org.

Contact: mendes@vbi.vt.edu

/pdf/copasi-a-complex-pathway-simulator-320i5gmr5f.pdf

COPASI---a COmplex PAthway SImulator

The PANTHER (protein annotation through evolutionary relationship) classification system (http://wwwpantherdborg/) is a comprehensive system that combines gene function, ontology, pathways and statistical analysis tools that enable biologists to analyze large-scale, genome-wide data from sequencing, proteomics or gene expression experiments The system is built with 82 complete genomes organized into gene families and subfamilies, and their evolutionary relationships are captured in phylogenetic trees, multiple sequence alignments and statistical models (hidden Markov models or HMMs) Genes are classified according to their function in several different ways: families and subfamilies are annotated with ontology terms (Gene Ontology (GO) and PANTHER protein class), and sequences are assigned to PANTHER pathways The PANTHER website includes a suite of tools that enable users to browse and query gene functions, and to analyze large-scale experimental data with a number of statistical tests It is widely used by bench scientists, bioinformaticians, computer scientists and systems biologists In the 2013 release of PANTHER (v80), in addition to an update of the data content, we redesigned the website interface to improve both user experience and the system's analytical capability This protocol provides a detailed description of how to analyze genome-wide experimental data with the PANTHER classification system

Large-scale gene function analysis with the PANTHER classification system

The manner in which microorganisms utilize their metabolic processes can be predicted using constraint-based analysis of genome-scale metabolic networks. Herein, we present the constraint-based reconstruction and analysis toolbox, a software package running in the Matlab environment, which allows for quantitative prediction of cellular behavior using a constraint-based approach. Specifically, this software allows predictive computations of both steady-state and dynamic optimal growth behavior, the effects of gene deletions, comprehensive robustness analyses, sampling the range of possible cellular metabolic states and the determination of network modules. Functions enabling these calculations are included in the toolbox, allowing a user to input a genome-scale metabolic model distributed in Systems Biology Markup Language format and perform these calculations with just a few lines of code. The results are predictions of cellular behavior that have been verified as accurate in a growing body of research. After software installation, calculation time is minimal, allowing the user to focus on the interpretation of the computational results.

/pdf/quantitative-prediction-of-cellular-metabolism-with-10nwgiv8r8.pdf

Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0

Motivation: Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively. Results: We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others. ∗ To whom correspondence should be addressed. Availability: The specification of SBML Level 1 is freely available from http://www.sbml.org/.

The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models.

Biological networks are systems of biochemical processes inside a cell that involve cellular constituents such as DNA, RNA, proteins, and various small molecules. Pathway maps are often used to represent the structure of such networks with associated biological information. Several pathway editors exist, and they vary according to specific domains of knowledge. This paper presents a review of existing pathway editors, along with an introduction to the Edinburgh Pathway Editor (EPE). EPE was designed for the annotation, visualization, and presentation of a wide variety of biological networks that include metabolic, genetic, and signal transduction pathways. EPE is based on a metadata-driven architecture. The editor supports the presentation and annotation of maps, in addition to the storage and retrieval of reaction kinetics information in relational databases that are either local or remote. EPE also has facilities for linking graphical objects to external databases and Web resources, and is capable of reproducing most existing graphical notations and visual representations of pathway maps. In summary, EPE provides a highly flexible tool for combining visualization, editing, and database manipulation of information relating to biological networks. EPE is open-source software, distributed under the Eclipse open-source application platform license.

The pathway editor: a tool for managing complex biological networks

Based on the available experimental data, we developed a kinetic model of the catalytic cycle of imidazologlycerol-phosphate synthetase from Escherichia coli accounting for the synthetase and glutaminase activities of the enzyme The rate equations describing synthetase and glutaminase activities of imidazologlycerol-phosphate synthetase were derived from this catalytic cycle Using the literature data, we evaluated all kinetic parameters of the rate equations characterizing individually synthetase and glutaminase activities as well as the contribution of each activity depending on concentration of the substrates, products, and effectors As shown, in the presence of 5′-phosphoribosylformimino-5-aminoimidazolo-4-carboxamideribonucleotide (ProFAR) and imidazologlycerol phosphate (IGP) glutaminase activity dominates over synthetase activity at sufficiently low concentrations of 5′-phosphoribulosylformimino-5-aminoimidazolo-4-carboxamideribonucleotide (PRFAR) Increased PRFAR concentrations resulted in decreased contribution of glutaminase activity and, consequently, increased the contribution of synthetase activity in the enzyme functioning

Kinetic model of imidazologlycerol-phosphate synthetase from Escherichia coli.

We are currently in the process of integrating the metabolic, genetic and product-interaction networks of Escherichia coli, with a view to creating a whole cell model [1]. In an effort to assess the physiological accuracy of our current model, we undertook extensive analysis of the metabolic component of the model, which consists of over 850 enzymes. Conversion of this network into a stoichiometric matrix allowed interpretation through the use of graph theory and its associated algorithms. Our strategy, and subsequent results, differed from that previously described [3]. Advantageously, our strategy allows consideration of either metabolites or enzymes as nodes and ameliorates the need to introduce temporary complexes. Various analyses were performed to describe the topology and other properties of the metabolic network. The ‘small-world’ characteristic described as being inherent in metabolic systems was investigated by determining node interconnectivity . To determine the metabolic ‘centre-of-the-world’, substrates were ranked based upon connections to and from them. This also allowed identification of metabolites that have only outgoingdirected connections (absolute products, or sinks), and those whose connectivity is purely incoming (absolute substrates, or sources). Our analysis suggests the metabolic network to be scale-free and ‘small-world’ in property. We have identified nodes with up to 119 connections (when direction is not considered). Our current metabolic model consists of 1 large cluster (containing 92% of the network) as well as smaller (2-3 nodes) or entirely disconnected nodes. Analysis of this major cluster suggested a network diameter of 4.64. This is approximately 50% larger than the value reported elsewhere. We believe this discrepancy is due to the erroneous inclusion of cofactors such as ATP, in the previous study. This also affects the maximum path length between metabolites, which we believe to be 18, rather than the 8 described elsewhere [3]. A metabolic network can be described in terms of components or clusters. These may be connected through articulation vertices, or may be entirely disconnected. Introducing connections between isolated components potentially identifies refinements that need to be made to the model. Conversely, a large component may potentially be splintered into smaller components by identification and removal of an articulation vertex. This may be of consequence in drug target identification. In addition we describe graph-based comparisons between the Escherichia coli metabolic network, and that of another microbial metabolic network. Future work will centre on implementation of algorithms to simultaneously remove multiple nodes (for connectivity analysis), identify ‘cycles’ and on graph visualization. We also seek to automate some of the analyses described.

S. Dronov

Papers

The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models.

The pathway editor: a tool for managing complex biological networks

Kinetic model of imidazologlycerol-phosphate synthetase from Escherichia coli.

Network analysis of Escherichia coli metabolic models