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Bioinformatics Advance Access originally published online on December 6, 2006
Bioinformatics 2007 23(3):351-357; doi:10.1093/bioinformatics/btl619
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Bayesian-based selection of metabolic objective functions

Andrea L. Knorr , Rishi Jain and Ranjan Srivastava *

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

*To whom correspondence should be addressed.


  Abstract

Motivation: A critical component of in silico analysis of underdetermined metabolic systems is the identification of the appropriate objective function. A common assumption is that the objective of the cell is to maximize growth. This objective function has been shown to be consistent in a few limited experimental cases, but may not be universally appropriate. Here a method is presented to quantitatively determine the most probable objective function.

Results: The genome-scale metabolism of Escherichia coli growing on succinate was used as a case-study for analysis. Five different objective functions, including maximization of growth rate, were chosen based on biological plausibility. A combination of flux balance analysis and linear programming was used to simulate cellular metabolism, which was then compared to independent experimental data using a Bayesian objective function discrimination technique. After comparing rates of oxygen uptake and acetate production, minimization of the production rate of redox potential was determined to be the most probable objective function. Given the appropriate reaction network and experimental data, the discrimination technique can be applied to any bacterium to test a variety of different possible objective functions.

Contact: srivasta{at}engr.uconn.edu

Supplementary information: Additional files, code and a program for carrying out model discrimination are available at http://www.engr.uconn.edu/~srivasta/modisc.html.

Associate Editor: Martin Bishop

Received on October 3, 2006; revised on November 28, 2006; accepted on November 29, 2006

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