Document Detail


Prediction of metabolic fluxes by incorporating genomic context and flux-converging pattern analyses.
MedLine Citation:
PMID:  20679215     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Flux balance analysis (FBA) of a genome-scale metabolic model allows calculation of intracellular fluxes by optimizing an objective function, such as maximization of cell growth, under given constraints, and has found numerous applications in the field of systems biology and biotechnology. Due to the underdetermined nature of the system, however, it has limitations such as inaccurate prediction of fluxes and existence of multiple solutions for an optimal objective value. Here, we report a strategy for accurate prediction of metabolic fluxes by FBA combined with systematic and condition-independent constraints that restrict the achievable flux ranges of grouped reactions by genomic context and flux-converging pattern analyses. Analyses of three types of genomic contexts, conserved genomic neighborhood, gene fusion events, and co-occurrence of genes across multiple organisms, were performed to suggest a group of fluxes that are likely on or off simultaneously. The flux ranges of these grouped reactions were constrained by flux-converging pattern analysis. FBA of the Escherichia coli genome-scale metabolic model was carried out under several different genotypic (pykF, zwf, ppc, and sucA mutants) and environmental (altered carbon source) conditions by applying these constraints, which resulted in flux values that were in good agreement with the experimentally measured (13)C-based fluxes. Thus, this strategy will be useful for accurately predicting the intracellular fluxes of large metabolic networks when their experimental determination is difficult.
Authors:
Jong Myoung Park; Tae Yong Kim; Sang Yup Lee
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2010-08-02
Journal Detail:
Title:  Proceedings of the National Academy of Sciences of the United States of America     Volume:  107     ISSN:  1091-6490     ISO Abbreviation:  Proc. Natl. Acad. Sci. U.S.A.     Publication Date:  2010 Aug 
Date Detail:
Created Date:  2010-08-18     Completed Date:  2010-09-27     Revised Date:  2011-07-25    
Medline Journal Info:
Nlm Unique ID:  7505876     Medline TA:  Proc Natl Acad Sci U S A     Country:  United States    
Other Details:
Languages:  eng     Pagination:  14931-6     Citation Subset:  IM    
Affiliation:
Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering, KAIST, Daejeon 305-701, Republic of Korea.
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Carbon / metabolism
Escherichia coli / genetics,  metabolism
Escherichia coli Proteins / genetics,  metabolism
Genome, Bacterial / genetics
Genomics / methods
Metabolic Networks and Pathways / genetics,  physiology*
Metabolome / genetics,  physiology
Metabolomics / methods*
Models, Biological*
Mutation
Systems Biology / methods
Chemical
Reg. No./Substance:
0/Escherichia coli Proteins; 7440-44-0/Carbon
Comments/Corrections

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