Biotechnology and Bioprocess Engineering:BBE

The Korean Society for Biotechnology and Bioengineering (한국생물공학회)
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Systems-Level Analysis of Genome-Scale In Silico Metabolic Models Using MetaFluxNet

  • Lee, Sang-Yup (Metabolic and Biomolecular Engineering National Laboratory, Department of Chemical and Biomolecular and BioProcess Engineering Research Center, Korea Advanced Institute of Science and Technology) ;
  • Woo, Han-Min (Metabolic and Biomolecular Engineering National Laboratory, Department of Chemical and Biomolecular and BioProcess Engineering Research Center, Korea Advanced Institute of Science and Technology) ;
  • Lee, Dong-Yup (Department of BioSystems and Bioinformatics Research Center, Korea Advanced Institute of Science and Technology) ;
  • Choi, Hyun-Seok (Metabolic and Biomolecular Engineering National Laboratory, Department of Chemical and Biomolecular and BioProcess Engineering Research Center, Korea Advanced Institute of Science and Technology) ;
  • Kim, Tae-Yong (Metabolic and Biomolecular Engineering National Laboratory, Department of Chemical and Biomolecular and BioProcess Engineering Research Center, Korea Advanced Institute of Science and Technology) ;
  • Yun, Hong-Seok (Department of BioSystems and Bioinformatics Research Center, Korea Advanced Institute of Science and Technology)
  • Published : 2005.10.31
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Abstract

The systems-level analysis of microbes with myriad of heterologous data generated by omics technologies has been applied to improve our understanding of cellular function and physiology and consequently to enhance production of various bioproducts. At the heart of this revolution resides in silico genome-scale metabolic model, In order to fully exploit the power of genome-scale model, a systematic approach employing user-friendly software is required. Metabolic flux analysis of genome-scale metabolic network is becoming widely employed to quantify the flux distribution and validate model-driven hypotheses. Here we describe the development of an upgraded MetaFluxNet which allows (1) construction of metabolic models connected to metabolic databases, (2) calculation of fluxes by metabolic flux analysis, (3) comparative flux analysis with flux-profile visualization, (4) the use of metabolic flux analysis markup language to enable models to be exchanged efficiently, and (5) the exporting of data from constraints-based flux analysis into various formats. MetaFluxNet also allows cellular physiology to be predicted and strategies for strain improvement to be developed from genome-based information on flux distributions. This integrated software environment promises to enhance our understanding on metabolic network at a whole organism level and to establish novel strategies for improving the properties of organisms for various biotechnological applications.

Keywords

References

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