Systems-level analysis of genome-scalein silico metabolic models using MetaFluxNet |
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Authors: | Sang Yup Lee Han Min Woo Dong-Yup Lee Hyung Seok Choi Tae Yong Kim Hongseok Yun |
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Institution: | 1. Metabolic and Biomolecular Engineering National Laboratory, Department of Chemical and Biomolecular, Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, 305-701, Daejeon, Korea 2. Department of BioSystems and Bioinformatics Research Center, Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, 305-701, Daejeon, Korea 3. BioProcess Engineering Research Center, Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, 305-701, Daejeon, Korea
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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 residesin 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. |
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Keywords: | systems biotechnology MetaFluxNet metabolic flux analysis in silico simulation |
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