caa a22 4500 606212507 CHVBK 20210128101043.0 cr unu---uuuuu 210128e20150301xx s 000 0 eng 10.1007/s10295-014-1554-9 doi (NATIONALLICENCE)springer-10.1007/s10295-014-1554-9 Applications of genome-scale metabolic network model in metabolic engineering [Elektronische Daten] [Byoungjin Kim, Won Kim, Dong Kim, Sang Lee] Genome-scale metabolic network model (GEM) is a fundamental framework in systems metabolic engineering. GEM is built upon extensive experimental data and literature information on gene annotation and function, metabolites and enzymes so that it contains all known metabolic reactions within an organism. Constraint-based analysis of GEM enables the identification of phenotypic properties of an organism and hypothesis-driven engineering of cellular functions to achieve objectives. Along with the advances in omics, high-throughput technology and computational algorithms, the scope and applications of GEM have substantially expanded. In particular, various computational algorithms have been developed to predict beneficial gene deletion and amplification targets and used to guide the strain development process for the efficient production of industrially important chemicals. Furthermore, an Escherichia coli GEM was integrated with a pathway prediction algorithm and used to evaluate all possible routes for the production of a list of commodity chemicals in E. coli. Combined with the wealth of experimental data produced by high-throughput techniques, much effort has been exerted to add more biological contexts into GEM through the integration of omics data and regulatory network information for the mechanistic understanding and improved prediction capabilities. In this paper, we review the recent developments and applications of GEM focusing on the GEM-based computational algorithms available for microbial metabolic engineering. Society for Industrial Microbiology and Biotechnology, 2014 Genome-scale metabolic network nationallicence Systems metabolic engineering nationallicence Gene knock-out prediction nationallicence Gene amplification prediction nationallicence Metabolic pathway prediction nationallicence Integrated genome-scale model nationallicence Kim Byoungjin Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, Bioinformatics Research Center, Center for Systems and Synthetic Biotechnology, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, 305-701, Daejeon, Republic of Korea aut Kim Won Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, Bioinformatics Research Center, Center for Systems and Synthetic Biotechnology, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, 305-701, Daejeon, Republic of Korea aut Kim Dong Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, Bioinformatics Research Center, Center for Systems and Synthetic Biotechnology, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, 305-701, Daejeon, Republic of Korea aut Lee Sang Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, Bioinformatics Research Center, Center for Systems and Synthetic Biotechnology, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, 305-701, Daejeon, Republic of Korea aut Journal of Industrial Microbiology & Biotechnology Springer Berlin Heidelberg 42/3(2015-03-01), 339-348 1367-5435 42:3<339 2015 42 10295 https://doi.org/10.1007/s10295-014-1554-9 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10295-014-1554-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Kim Byoungjin Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, Bioinformatics Research Center, Center for Systems and Synthetic Biotechnology, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, 305-701, Daejeon, Republic of Korea aut NATIONALLICENCE 700 1- Kim Won Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, Bioinformatics Research Center, Center for Systems and Synthetic Biotechnology, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, 305-701, Daejeon, Republic of Korea aut NATIONALLICENCE 700 1- Kim Dong Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, Bioinformatics Research Center, Center for Systems and Synthetic Biotechnology, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, 305-701, Daejeon, Republic of Korea aut NATIONALLICENCE 700 1- Lee Sang Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, Bioinformatics Research Center, Center for Systems and Synthetic Biotechnology, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, 305-701, Daejeon, Republic of Korea aut NATIONALLICENCE 773 0- Journal of Industrial Microbiology & Biotechnology Springer Berlin Heidelberg 42/3(2015-03-01), 339-348 1367-5435 42:3<339 2015 42 10295