Differential evolution and its application to metabolic flux analysis

Authors:
Jing Yang;Sarawan Wongsa;Visakan Kadirkamanathan;Stephen A. Billings;Phillip C. Wright
Affiliations:
Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom;Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom;Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom;Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom;Department of Chemical and Process Engineering, University of Sheffield, Sheffield, United Kingdom
Venue:
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Year:
2005

Citing 2
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Abstract

Metabolic flux analysis with measurement data from 13C tracer experiments has been an important approach for exploring metabolic networks. Though various methods were developed for 13C positional enrichment or isotopomer modelling, few researchers have investigated flux estimation problem in detail. In this paper, flux estimation is formulated as a global optimization problem by carbon enrichment balances. Differential evolution, which is a simple and robust evolutionary algorithm, is applied to flux estimation. The algorithm performances are illustrated and compared with ordinary least squares estimation through simulation of the cyclic pentose phosphate metabolic network in a noisy environment. It is shown that differential evolution is an efficient approach for flux quantification.