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Bioinformatics Advance Access published online on March 1, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm050
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Improving metabolic flux estimation via evolutionary optimization for convex solution space

Jiusheng Chen 1, Haoran Zheng 1,3,*, Haiyan Liu 2,3, Junqing Niu 1, Jianping Liu 2, Tie Shen 2, Bin Rui 2 and Yunyu Shi 2,3

1Department of Computer Science and Technology, University of Science and Technology of China, Hefei, China, 2School of Life Science, University of Science and Technology of China, Hefei, China and 3Department of Systems Biology, University of Science and Technology of China, Hefei, China

*To whom correspondence should be addressed. Haoran Zheng, E-mail: hrzheng{at}ustc.edu.cn


  Abstract

Motivation: Flux estimation by using 13C-labeling pattern information of metabolites is currently the only method that can give accurate, detailed quantification of all intracellular fluxes in the central metabolism of a microorganism. In essence, it corresponds to a constrained optimization problem which minimizes a weighted distance between measured and simulated results. Characteristics such as existence of multiple local minima, nonlinear and nondifferentiable make this problem a special difficulty.

Results: In the present work, we propose an evolutionary-based global optimization algorithm taking advantage of the convex feature of the problem's solution space. Based on the characteristics of convex spaces, specialized initial population and evolutionary operators are designed to solve 13C-based metabolic flux estimation problem robustly and efficiently. The algorithm was applied to estimate the central metabolic fluxes in Escherichia coli and compared with conventional optimization technique. Experimental results illustrated that our algorithm is capable of achieving fast convergence to good near-optima and maintaining the robust nature of evolutionary algorithms at the same time.

Availability: Available from the authors upon request.

Associate Editor: Dr. Trey Ideker

Received on September 10, 2006; revised on January 6, 2007; accepted on February 8, 2007

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