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A model-driven quantitative metabolomics analysis of aerobic and anaerobic metabolism in E. coli K-12 MG1655 that is biochemically and thermodynamically consistent

Authors

  • Douglas McCloskey,

    1. Department of Bioengineering, University of California, San Diego, La Jolla, California
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  • Jon A. Gangoiti,

    1. Departments of Medicine, Pediatrics, and Pathology, University of California, San Diego School of Medicine, San Diego, California
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  • Zachary A. King,

    1. Department of Bioengineering, University of California, San Diego, La Jolla, California
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  • Robert K. Naviaux,

    1. Departments of Medicine, Pediatrics, and Pathology, University of California, San Diego School of Medicine, San Diego, California
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  • Bruce A. Barshop,

    1. Departments of Medicine, Pediatrics, and Pathology, University of California, San Diego School of Medicine, San Diego, California
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  • Bernhard O. Palsson,

    1. Department of Bioengineering, University of California, San Diego, La Jolla, California
    2. Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
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  • Adam M. Feist

    Corresponding author
    1. Department of Bioengineering, University of California, San Diego, La Jolla, California
    2. Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
    • Correspondence to: A.M. Feist

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ABSTRACT

The advent of model-enabled workflows in systems biology allows for the integration of experimental data types with genome-scale models to discover new features of biology. This work demonstrates such a workflow, aimed at establishing a metabolomics platform applied to study the differences in metabolomes between anaerobic and aerobic growth of Escherichia coli. Constraint-based modeling was utilized to deduce a target list of compounds for downstream method development. An analytical and experimental methodology was developed and tailored to the compound chemistry and growth conditions of interest. This included the construction of a rapid sampling apparatus for use with anaerobic cultures. The resulting genome-scale data sets for anaerobic and aerobic growth were validated by comparison to previous small-scale studies comparing growth of E. coli under the same conditions. The metabolomics data were then integrated with the E. coli genome-scale metabolic model (GEM) via a sensitivity analysis that utilized reaction thermodynamics to reconcile simulated growth rates and reaction directionalities. This analysis highlighted several optimal network usage inconsistencies, including the incorrect use of the beta-oxidation pathway for synthesis of fatty acids. This analysis also identified enzyme promiscuity for the pykA gene, that is critical for anaerobic growth, and which has not been previously incorporated into metabolic models of E coli. Biotechnol. Bioeng. 2014;111: 803–815. © 2013 Wiley Periodicals, Inc.

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