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      An analysis of a ‘community-driven’ reconstruction of the human metabolic network

      review-article
      ,   ,  
      Metabolomics
      Springer US
      Metabolism, Modelling, Systems biology, Networks, Metabolic networks

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          Abstract

          Following a strategy similar to that used in baker’s yeast (Herrgård et al. Nat Biotechnol 26:1155–1160, 2008). A consensus yeast metabolic network obtained from a community approach to systems biology (Herrgård et al. 2008; Dobson et al. BMC Syst Biol 4:145, 2010). Further developments towards a genome-scale metabolic model of yeast (Dobson et al. 2010; Heavner et al. BMC Syst Biol 6:55, 2012). Yeast 5—an expanded reconstruction of the Saccharomyces cerevisiae metabolic network (Heavner et al. 2012) and in Salmonella typhimurium (Thiele et al. BMC Syst Biol 5:8, 2011). A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonella typhimurium LT2 (Thiele et al. 2011), a recent paper (Thiele et al. Nat Biotechnol 31:419–425, 2013). A community-driven global reconstruction of human metabolism (Thiele et al. 2013) described a much improved ‘community consensus’ reconstruction of the human metabolic network, called Recon 2, and the authors (that include the present ones) have made it freely available via a database at http://humanmetabolism.org/ and in SBML format at Biomodels ( http://identifiers.org/biomodels.db/MODEL1109130000). This short analysis summarises the main findings, and suggests some approaches that will be able to exploit the availability of this model to advantage.

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          Membrane transporters in drug development.

          Membrane transporters can be major determinants of the pharmacokinetic, safety and efficacy profiles of drugs. This presents several key questions for drug development, including which transporters are clinically important in drug absorption and disposition, and which in vitro methods are suitable for studying drug interactions with these transporters. In addition, what criteria should trigger follow-up clinical studies, and which clinical studies should be conducted if needed. In this article, we provide the recommendations of the International Transporter Consortium on these issues, and present decision trees that are intended to help guide clinical studies on the currently recognized most important drug transporter interactions. The recommendations are generally intended to support clinical development and filing of a new drug application. Overall, it is advised that the timing of transporter investigations should be driven by efficacy, safety and clinical trial enrolment questions (for example, exclusion and inclusion criteria), as well as a need for further understanding of the absorption, distribution, metabolism and excretion properties of the drug molecule, and information required for drug labelling.
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            High-throughput generation, optimization and analysis of genome-scale metabolic models.

            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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              Is Open Access

              MetaboLights—an open-access general-purpose repository for metabolomics studies and associated meta-data

              MetaboLights (http://www.ebi.ac.uk/metabolights) is the first general-purpose, open-access repository for metabolomics studies, their raw experimental data and associated metadata, maintained by one of the major open-access data providers in molecular biology. Metabolomic profiling is an important tool for research into biological functioning and into the systemic perturbations caused by diseases, diet and the environment. The effectiveness of such methods depends on the availability of public open data across a broad range of experimental methods and conditions. The MetaboLights repository, powered by the open source ISA framework, is cross-species and cross-technique. It will cover metabolite structures and their reference spectra as well as their biological roles, locations, concentrations and raw data from metabolic experiments. Studies automatically receive a stable unique accession number that can be used as a publication reference (e.g. MTBLS1). At present, the repository includes 15 submitted studies, encompassing 93 protocols for 714 assays, and span over 8 different species including human, Caenorhabditis elegans, Mus musculus and Arabidopsis thaliana. Eight hundred twenty-seven of the metabolites identified in these studies have been mapped to ChEBI. These studies cover a variety of techniques, including NMR spectroscopy and mass spectrometry.
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                Author and article information

                Contributors
                +44-161-306-4492 , +44-161-306-4556 , neil.swainston@manchester.ac.uk
                pedro.mendes@manchester.ac.uk
                dbk@manchester.ac.uk
                Journal
                Metabolomics
                Metabolomics
                Metabolomics
                Springer US (Boston )
                1573-3882
                1573-3890
                12 July 2013
                12 July 2013
                August 2013
                : 9
                : 4
                : 757-764
                Affiliations
                [ ]School of Chemistry, The University of Manchester, Oxford Road, Manchester, M13 9PL UK
                [ ]Manchester Institute of Biotechnology, The University of Manchester, Princess Street, Manchester, M1 7DN UK
                [ ]School of Computer Science, The University of Manchester, Oxford Road, Manchester, M13 9PL UK
                [ ]Virginia Bioinformatics Institute, Virginia Tech, Washington St. 0477, Blacksburg, VA 24060 USA
                Article
                564
                10.1007/s11306-013-0564-3
                3715687
                23888127
                e74ed496-3514-4d58-ac5f-b4ebab7713bf
                © The Author(s) 2013

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

                History
                : 16 May 2013
                : 28 June 2013
                Categories
                Review Article
                Custom metadata
                © Springer Science+Business Media New York 2013

                Molecular biology
                metabolism,modelling,systems biology,networks,metabolic networks
                Molecular biology
                metabolism, modelling, systems biology, networks, metabolic networks

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