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      Recon 2.2: from reconstruction to model of human metabolism

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          Abstract

          Introduction

          The human genome-scale metabolic reconstruction details all known metabolic reactions occurring in humans, and thereby holds substantial promise for studying complex diseases and phenotypes. Capturing the whole human metabolic reconstruction is an on-going task and since the last community effort generated a consensus reconstruction, several updates have been developed.

          Objectives

          We report a new consensus version, Recon 2.2, which integrates various alternative versions with significant additional updates. In addition to re-establishing a consensus reconstruction, further key objectives included providing more comprehensive annotation of metabolites and genes, ensuring full mass and charge balance in all reactions, and developing a model that correctly predicts ATP production on a range of carbon sources.

          Methods

          Recon 2.2 has been developed through a combination of manual curation and automated error checking. Specific and significant manual updates include a respecification of fatty acid metabolism, oxidative phosphorylation and a coupling of the electron transport chain to ATP synthase activity. All metabolites have definitive chemical formulae and charges specified, and these are used to ensure full mass and charge reaction balancing through an automated linear programming approach. Additionally, improved integration with transcriptomics and proteomics data has been facilitated with the updated curation of relationships between genes, proteins and reactions.

          Results

          Recon 2.2 now represents the most predictive model of human metabolism to date as demonstrated here. Extensive manual curation has increased the reconstruction size to 5324 metabolites, 7785 reactions and 1675 associated genes, which now are mapped to a single standard. The focus upon mass and charge balancing of all reactions, along with better representation of energy generation, has produced a flux model that correctly predicts ATP yield on different carbon sources.

          Conclusion

          Through these updates we have achieved the most complete and best annotated consensus human metabolic reconstruction available, thereby increasing the ability of this resource to provide novel insights into normal and disease states in human. The model is freely available from the Biomodels database ( http://identifiers.org/biomodels.db/MODEL1603150001).

          Electronic supplementary material

          The online version of this article (doi:10.1007/s11306-016-1051-4) contains supplementary material, which is available to authorized users.

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          Most cited references21

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

          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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            ChEBI in 2016: Improved services and an expanding collection of metabolites

            ChEBI is a database and ontology containing information about chemical entities of biological interest. It currently includes over 46 000 entries, each of which is classified within the ontology and assigned multiple annotations including (where relevant) a chemical structure, database cross-references, synonyms and literature citations. All content is freely available and can be accessed online at http://www.ebi.ac.uk/chebi. In this update paper, we describe recent improvements and additions to the ChEBI offering. We have substantially extended our collection of endogenous metabolites for several organisms including human, mouse, Escherichia coli and yeast. Our front-end has also been reworked and updated, improving the user experience, removing our dependency on Java applets in favour of embedded JavaScript components and moving from a monthly release update to a ‘live’ website. Programmatic access has been improved by the introduction of a library, libChEBI, in Java, Python and Matlab. Furthermore, we have added two new tools, namely an analysis tool, BiNChE, and a query tool for the ontology, OntoQuery.
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              A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology.

              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

                Contributors
                +441613065131 , neil.swainston@manchester.ac.uk
                Journal
                Metabolomics
                Metabolomics
                Metabolomics
                Springer US (New York )
                1573-3882
                1573-3890
                7 June 2016
                7 June 2016
                2016
                : 12
                : 109
                Affiliations
                [ ]Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN UK
                [ ]Faculty of Life Sciences, The University of Manchester, Manchester, M13 9PL UK
                [ ]School of Computer Science, The University of Manchester, Manchester, M13 9PL UK
                [ ]Department of Bioengineering, University of California, San Diego, La Jolla, CA USA
                [ ]Novo Nordisk Foundation Center for Biosustainability, University of California, San Diego School of Medicine, La Jolla, CA USA
                [ ]Harvard Extension School, 51 Brattle St., Cambridge, MA 02138 USA
                [ ]Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St., Cambridge, MA 02139 USA
                [ ]Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria
                [ ]Austrian Centre of Industrial Biotechnology, Vienna, Austria
                [ ]Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585 Singapore
                [ ]Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, Centros, Singapore, 138668 Singapore
                [ ]Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner College and Cooper Roads (Bldg 75), Brisbane, QLD 4072 Australia
                [ ]Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA USA
                [ ]School of Chemistry, The University of Manchester, Manchester, M13 9PL UK
                [ ]Department of Pediatrics, University of California, San Diego, La Jolla, CA USA
                [ ]Center for Quantitative Medicine, UConn Health, 263 Farmington Avenue, Farmington, CT 06030-6033 USA
                Article
                1051
                10.1007/s11306-016-1051-4
                4896983
                27358602
                57e99f5b-4bf5-4ae6-bcfb-c25d4e13ae2f
                © The Author(s) 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 29 March 2016
                : 27 May 2016
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000268, Biotechnology and Biological Sciences Research Council;
                Award ID: BB/M006891/1
                Award ID: BB/K019783/1
                Award ID: BB/M017702/1
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: GM080219
                Award Recipient :
                Categories
                Short Communication
                Custom metadata
                © Springer Science+Business Media New York 2016

                Molecular biology
                human,metabolism,modelling,reconstruction,model,systems biology
                Molecular biology
                human, metabolism, modelling, reconstruction, model, systems biology

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