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      MetExplore: collaborative edition and exploration of metabolic networks

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          Abstract

          Metabolism of an organism is composed of hundreds to thousands of interconnected biochemical reactions responding to environmental or genetic constraints. This metabolic network provides a rich knowledge to contextualize omics data and to elaborate hypotheses on metabolic modulations. Nevertheless, performing this kind of integrative analysis is challenging for end users with not sufficiently advanced computer skills since it requires the use of various tools and web servers. MetExplore offers an all-in-one online solution composed of interactive tools for metabolic network curation, network exploration and omics data analysis. In particular, it is possible to curate and annotate metabolic networks in a collaborative environment. The network exploration is also facilitated in MetExplore by a system of interactive tables connected to a powerful network visualization module. Finally, the contextualization of metabolic elements in the network and the calculation of over-representation statistics make it possible to interpret any kind of omics data. MetExplore is a sustainable project maintained since 2010 freely available at https://metexplore.toulouse.inra.fr/metexplore2/.

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          A protocol for generating a high-quality genome-scale metabolic reconstruction.

          Ines Thiele, Bernhard Ø Palsson (2010)
          Network reconstructions are a common denominator in systems biology. Bottom-up metabolic network reconstructions have been developed over the last 10 years. These reconstructions represent structured knowledge bases that abstract pertinent information on the biochemical transformations taking place within specific target organisms. The conversion of a reconstruction into a mathematical format facilitates a myriad of computational biological studies, including evaluation of network content, hypothesis testing and generation, analysis of phenotypic characteristics and metabolic engineering. To date, genome-scale metabolic reconstructions for more than 30 organisms have been published and this number is expected to increase rapidly. However, these reconstructions differ in quality and coverage that may minimize their predictive potential and use as knowledge bases. Here we present a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction, as well as the common trials and tribulations. Therefore, this protocol provides a helpful manual for all stages of the reconstruction process.
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            Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0.

            Jan Schellenberger, Richard Que, Ronan M. T. Fleming … (2011)
            Over the past decade, a growing community of researchers has emerged around the use of constraint-based reconstruction and analysis (COBRA) methods to simulate, analyze and predict a variety of metabolic phenotypes using genome-scale models. The COBRA Toolbox, a MATLAB package for implementing COBRA methods, was presented earlier. Here we present a substantial update of this in silico toolbox. Version 2.0 of the COBRA Toolbox expands the scope of computations by including in silico analysis methods developed since its original release. New functions include (i) network gap filling, (ii) (13)C analysis, (iii) metabolic engineering, (iv) omics-guided analysis and (v) visualization. As with the first version, the COBRA Toolbox reads and writes systems biology markup language-formatted models. In version 2.0, we improved performance, usability and the level of documentation. A suite of test scripts can now be used to learn the core functionality of the toolbox and validate results. This toolbox lowers the barrier of entry to use powerful COBRA methods.
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              The effects of alternate optimal solutions in constraint-based genome-scale metabolic models.

              R. Mahadevan, C.H. Schilling (2003)
              Genome-scale constraint-based models of several organisms have now been constructed and are being used for model driven research. A key issue that may arise in the use of such models is the existence of alternate optimal solutions wherein the same maximal objective (e.g., growth rate) can be achieved through different flux distributions. Herein, we investigate the effects that alternate optimal solutions may have on the predicted range of flux values calculated using currently practiced linear (LP) and quadratic programming (QP) methods. An efficient LP-based strategy is described to calculate the range of flux variability that can be present in order to achieve optimal as well as suboptimal objective states. Sample results are provided for growth predictions of E. coli using glucose, acetate, and lactate as carbon substrates. These results demonstrate the extent of flux variability to be highly dependent on environmental conditions and network composition. In addition we examined the impact of alternate optima for growth under gene knockout conditions as calculated using QP-based methods. It was observed that calculations using QP-based methods can show significant variation in growth rate if the flux variability among alternate optima is high. The underlying biological significance and general source of such flux variability is further investigated through the identification of redundancies in the network (equivalent reaction sets) that lead to alternate solutions. Collectively, these results illustrate the variability inherent in metabolic flux distributions and the possible implications of this heterogeneity for constraint-based modeling approaches. These methods also provide an efficient and robust method to calculate the range of flux distributions that can be derived from quantitative fermentation data.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Nucleic Acids Res
                Journal ID (iso-abbrev): Nucleic Acids Res
                Journal ID (publisher-id): nar
                Title: Nucleic Acids Research
                Publisher: Oxford University Press
                ISSN (Print): 0305-1048
                ISSN (Electronic): 1362-4962
                Publication date (Print): 02 July 2018
                Publication date (Electronic): 30 April 2018
                Publication date PMC-release: 30 April 2018
                Volume: 46
                Issue: Web Server issue
                Pages: W495-W502
                Affiliations
                [1 ]LIPM, Université de Toulouse, INRA, CNRS, Castanet-Tolosan, France
                [2 ]INRA, UMR1331, Toxalim, F-31000 Toulouse, France
                [3 ]MedDay Pharmaceuticals, Paris, France
                [4 ]Berlin Institute of Health Metabolomics Platform, 10178 Berlin, Germany
                [5 ]Core Unit Bioinformatics, Berlin Institute of Health, 10178 Berlin, Germany
                [6 ]Max Delbrück Center for Molecular Medicine, 13125 Berlin, Germany
                [7 ]Université de Toulouse; INSA, UPS, INP; LISBP, 135 Avenue de Rangueil, F-31077 Toulouse, France
                [8 ]INRA, UMR792, Ingénierie des Systèmes Biologiques et des Procédés, F-31400 Toulouse, France
                [9 ]CNRS, UMR5504, F-31400 Toulouse, France
                Author notes
                To whom correspondence should be addressed. Tel: +33 5 82 06 63 95; Fax: + 33 5 61 28 52 44; Email: ludovic.cottret@ 123456inra.fr , fabien.jourdan@ 123456inra.fr
                Author information
                http://orcid.org/0000-0001-7418-7750
                http://orcid.org/0000-0003-0493-8592
                Article
                Publisher ID: gky301
                DOI: 10.1093/nar/gky301
                PMC ID: 6030842
                PubMed ID: 29718355
                SO-VID: 00404931-1ec8-41d2-bf4b-2670dd90577b
                Copyright © © The Author(s) 2018. Published by Oxford University Press on behalf of Nucleic Acids Research.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                Date accepted : 11 April 2018
                Date revision received : 22 March 2018
                Date received : 31 January 2018
                Page count
                Pages: 8
                Funding
                Funded by: National Metabolomics and Fluxomics Infrastructure
                Award ID: ANR-INBS-0010
                Funded by: Horizon 2020 10.13039/501100007601
                Award ID: 654241
                Funded by: Wellcome Trust 10.13039/100004440
                Award ID: 105614/Z/14/Z
                Categories
                Subject: Web Server Issue

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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