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.
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Yang, J., Wongsa, S., Kadirkamanathan, V., Billings, S.A., Wright, P.C. (2005). Differential Evolution and Its Application to Metabolic Flux Analysis. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2005. Lecture Notes in Computer Science, vol 3449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32003-6_12
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DOI: https://doi.org/10.1007/978-3-540-32003-6_12
Publisher Name: Springer, Berlin, Heidelberg
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