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Applications of genome-scale metabolic network model in metabolic engineering

  • Byoungjin Kim
  • Won Jun Kim
  • Dong In Kim
  • Sang Yup LeeEmail author
Metabolic Engineering and Synthetic Biology

Abstract

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.

Keywords

Genome-scale metabolic network Systems metabolic engineering Gene knock-out prediction Gene amplification prediction Metabolic pathway prediction Integrated genome-scale model 

Notes

Acknowledgments

This work was supported by the Intelligent Synthetic Biology Center through the Global Frontier Project (2011-0031963) of the Ministry of Science, ICT & Future Planning through the National Research Foundation of Korea.

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Copyright information

© Society for Industrial Microbiology and Biotechnology 2014

Authors and Affiliations

  • Byoungjin Kim
    • 1
  • Won Jun Kim
    • 1
  • Dong In Kim
    • 1
  • Sang Yup Lee
    • 1
    Email author
  1. 1.Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, Bioinformatics Research Center, Center for Systems and Synthetic BiotechnologyInstitute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST)DaejeonRepublic of Korea

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