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      MetaNetX.org: a website and repository for accessing, analysing and manipulating metabolic networks

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          Abstract

          Summary: MetaNetX.org is a website for accessing, analysing and manipulating genome-scale metabolic networks (GSMs) as well as biochemical pathways. It consistently integrates data from various public resources and makes the data accessible in a standardized format using a common namespace. Currently, it provides access to hundreds of GSMs and pathways that can be interactively compared (two or more), analysed (e.g. detection of dead-end metabolites and reactions, flux balance analysis or simulation of reaction and gene knockouts), manipulated and exported. Users can also upload their own metabolic models, choose to automatically map them into the common namespace and subsequently make use of the website’s functionality .

          Availability and implementation: MetaNetX.org is available at http://metanetx.org.

          Contact: help@ 123456metanetx.org

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          Most cited references11

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          High-throughput generation, optimization and analysis of genome-scale metabolic models.

          Christopher S. Henry, Matthew DeJongh, Aaron A. Best … (2010)
          Genome-scale metabolic models have proven to be valuable for predicting organism phenotypes from genotypes. Yet efforts to develop new models are failing to keep pace with genome sequencing. To address this problem, we introduce the Model SEED, a web-based resource for high-throughput generation, optimization and analysis of genome-scale metabolic models. The Model SEED integrates existing methods and introduces techniques to automate nearly every step of this process, taking approximately 48 h to reconstruct a metabolic model from an assembled genome sequence. We apply this resource to generate 130 genome-scale metabolic models representing a taxonomically diverse set of bacteria. Twenty-two of the models were validated against available gene essentiality and Biolog data, with the average model accuracy determined to be 66% before optimization and 87% after optimization.
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            Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox.

            Scott A Becker, Adam M Feist, Monica L. Mo … (2007)
            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.
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              A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology.

              Markus J. Herrgard, Neil Swainston, Paul Dobson … (2008)
              Genomic data allow the large-scale manual or semi-automated assembly of metabolic network reconstructions, which provide highly curated organism-specific knowledge bases. Although several genome-scale network reconstructions describe Saccharomyces cerevisiae metabolism, they differ in scope and content, and use different terminologies to describe the same chemical entities. This makes comparisons between them difficult and underscores the desirability of a consolidated metabolic network that collects and formalizes the 'community knowledge' of yeast metabolism. We describe how we have produced a consensus metabolic network reconstruction for S. cerevisiae. In drafting it, we placed special emphasis on referencing molecules to persistent databases or using database-independent forms, such as SMILES or InChI strings, as this permits their chemical structure to be represented unambiguously and in a manner that permits automated reasoning. The reconstruction is readily available via a publicly accessible database and in the Systems Biology Markup Language (http://www.comp-sys-bio.org/yeastnet). It can be maintained as a resource that serves as a common denominator for studying the systems biology of yeast. Similar strategies should benefit communities studying genome-scale metabolic networks of other organisms.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (iso-abbrev): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Journal ID (hwp): bioinfo
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): 15 March 2013
                Publication date (Electronic): 28 January 2013
                Publication date PMC-release: 28 January 2013
                Volume: 29
                Issue: 6
                Pages: 815-816
                Affiliations
                1Department of Biosystems Science and Engineering, 2SIB Swiss Institute of Bioinformatics, ETH Zurich, 4058 Basel, Switzerland, 3Vital-IT, SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland and 4Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland
                Author notes
                *To whom correspondence should be addressed.

                †The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.

                Associate Editor: Mario Albrecht

                Article
                Publisher ID: btt036
                DOI: 10.1093/bioinformatics/btt036
                PMC ID: 3597148
                PubMed ID: 23357920
                SO-VID: 8229ec76-47aa-42a6-a26a-ff6abea0e317
                Copyright © © The Author 2013. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 3 August 2012
                Date revision received : 20 December 2012
                Date accepted : 19 January 2013
                Page count
                Pages: 2
                Categories
                Subject: Applications Notes
                Subject: Systems Biology

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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