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Bayesian-based selection of metabolic objective functions.

Andrea L Knorr1, Rishi Jain, Ranjan Srivastava

  • 1Department of Chemical, Materials and Biomolecular Engineering, University of Connecticut, 191 Auditorium Road U3222, Storrs, CT 06269-3222, USA.

Bioinformatics (Oxford, England)
|December 8, 2006
PubMed
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Identifying the correct objective function for metabolic models is crucial. Minimizing redox potential production, not maximizing growth, best describes Escherichia coli metabolism on succinate, according to new research.

Area of Science:

  • Systems Biology
  • Metabolic Engineering
  • Computational Biology

Background:

  • Accurate in silico analysis of metabolic systems requires appropriate objective functions.
  • Maximizing cellular growth is a common but not universally applicable assumption for objective functions.
  • A novel method is presented to quantitatively determine the most probable objective function.

Purpose of the Study:

  • To identify the most probable objective function for Escherichia coli metabolism on succinate.
  • To compare different biologically plausible objective functions using computational methods.
  • To develop a generalizable technique for objective function discrimination in microbial systems.

Main Methods:

  • Utilized genome-scale metabolic models of Escherichia coli.

Related Experiment Videos

  • Employed flux balance analysis and linear programming for metabolic simulations.
  • Applied a Bayesian objective function discrimination technique comparing simulated to experimental data.

    • Main Results:

    • Minimization of the production rate of redox potential was identified as the most probable objective function.
    • This finding contrasts with the common assumption of maximizing growth rate.
    • The method successfully discriminated between objective functions using oxygen uptake and acetate production rates.

    Conclusions:

    • The objective function for Escherichia coli on succinate is likely the minimization of redox potential production.
    • The developed discrimination technique is applicable to any bacterium with sufficient reaction network and experimental data.
    • This approach enhances the accuracy of in silico metabolic modeling.