An important problem in systems biology is parameter estimation for biochemical system models. Our work concentrates on the metabolic subnetwork of the valine and leucine biosynthesis in Corynebacterium glutamicum, an anaerobic actinobacterium of high biotechnological importance. Using data of an in vivo experiment measuring 13 metabolites during a glucose stimulus-response experiment we investigate the performance of various Evolutionary Algorithms on the parameter inference problem in biochemical modeling. Due to the inconclusive information on the reversibility of the reactions in the pathway, we develop both a reversible and an irreversible differential equation model based on the recent convenience kinetics approach. As the reversible model allows better approximation on the whole, we use it to analyze the impact of different settings on four especially promising EAs. We show that Particle Swarm Optimization as well as Differential Evolution are useful methods for parameter estimation on convenience kinetics models outperforming Genetic Algorithm and Evolution Strategy approaches and nearly reaching the quality of independent spline approximations on the raw data.
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