For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
66
views
149
references
 Top references
cited by
227
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      132
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Current status and applications of genome-scale metabolic models

      review-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Genome-scale metabolic models (GEMs) computationally describe gene-protein-reaction associations for entire metabolic genes in an organism, and can be simulated to predict metabolic fluxes for various systems-level metabolic studies. Since the first GEM for Haemophilus influenzae was reported in 1999, advances have been made to develop and simulate GEMs for an increasing number of organisms across bacteria, archaea, and eukarya. Here, we review current reconstructed GEMs and discuss their applications, including strain development for chemicals and materials production, drug targeting in pathogens, prediction of enzyme functions, pan-reactome analysis, modeling interactions among multiple cells or organisms, and understanding human diseases.

          Electronic supplementary material

          The online version of this article (10.1186/s13059-019-1730-3) contains supplementary material, which is available to authorized users.

          Related collections

          Most cited references149

          • Record: found
          • Abstract: found
          • Article: not found

          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.
          Article 0 comments Cited 642 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information

            Adam M Feist, Christopher S. Henry, Jennifer Reed … (2007)
            An updated genome-scale reconstruction of the metabolic network in Escherichia coli K-12 MG1655 is presented. This updated metabolic reconstruction includes: (1) an alignment with the latest genome annotation and the metabolic content of EcoCyc leading to the inclusion of the activities of 1260 ORFs, (2) characterization and quantification of the biomass components and maintenance requirements associated with growth of E. coli and (3) thermodynamic information for the included chemical reactions. The conversion of this metabolic network reconstruction into an in silico model is detailed. A new step in the metabolic reconstruction process, termed thermodynamic consistency analysis, is introduced, in which reactions were checked for consistency with thermodynamic reversibility estimates. Applications demonstrating the capabilities of the genome-scale metabolic model to predict high-throughput experimental growth and gene deletion phenotypic screens are presented. The increased scope and computational capability using this new reconstruction is expected to broaden the spectrum of both basic biology and applied systems biology studies of E. coli metabolism.
            Article 0 comments Cited 504 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              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.
              Article 0 comments Cited 425 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Contributors
                Hyun Uk Kim: ehukim@kaist.ac.kr
                Sang Yup Lee:
                ORCID: http://orcid.org/0000-0003-0599-3091
                leesy@kaist.ac.kr
                Journal
                Journal ID (nlm-ta): Genome Biol
                Journal ID (iso-abbrev): Genome Biol
                Title: Genome Biology
                Publisher: BioMed Central (London )
                ISSN (Print): 1474-7596
                ISSN (Electronic): 1474-760X
                Publication date (Electronic): 13 June 2019
                Publication date PMC-release: 13 June 2019
                Publication date Collection: 2019
                Volume: 20
                Electronic Location Identifier: 121
                Affiliations
                [1 ]ISNI 0000 0001 2292 0500, GRID grid.37172.30, Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Metabolic and Biomolecular Engineering National Research Laboratory, , Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), ; Daejeon, 34141 Republic of Korea
                [2 ]ISNI 0000 0001 2292 0500, GRID grid.37172.30, Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Systems Biology and Medicine Laboratory, KAIST, ; Daejeon, 34141 Republic of Korea
                [3 ]ISNI 0000 0001 2292 0500, GRID grid.37172.30, Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, ; Daejeon, 34141 Republic of Korea
                [4 ]ISNI 0000 0001 2292 0500, GRID grid.37172.30, BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, ; Daejeon, 34141 Republic of Korea
                Author information
                http://orcid.org/0000-0003-0599-3091
                Article
                Publisher ID: 1730
                DOI: 10.1186/s13059-019-1730-3
                PMC ID: 6567666
                PubMed ID: 31196170
                SO-VID: 882aa754-2a69-40d6-b030-86ffd44dbcbc
                Copyright © © The Author(s). 2019
                License:

                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. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                Funding
                Funded by: Ministry of Science and ICT
                Award ID: NRF-2012M1A2A2026556
                Award ID: NRF-2012M1A2A2026557
                Award ID: 2018M3A9F3079664
                Award Recipient :
                Categories
                Subject: Review
                Custom metadata
                issue-copyright-statement © The Author(s) 2019

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

                Comments

                Comment on this article

                scite_

                Similar content132

                See all similar

                Cited by223

                See all cited by

                Most referenced authors3,089

                See all reference authors