Comparing various evolutionary algorithms on the parameter optimization of the valine and leucine biosynthesis in Corynebacterium glutamicum

Parameter estimation for biochemical model systems has become an important problem in systems biology. Here we focus on the metabolic subnetwork of the valine and leucine biosynthesis in C glutamicum. Due to the lack of indisputable information regarding reversibility of the reactions in the pathway we derived two alternative ordinary differential equation models based on the formalisms of the generalized mass-action rate law. We introduced two alternative modeling approaches for feedback inhibition and evaluated the applicability of six optimization procedures (multi start Hill Climber, binary and real valued Genetic Algorithm, standard and covariance matrix adaption Evolution Strategy as well as Simulated Annealing) to the problem of parameter fitting. The model considering irreversible reactions performed worse and was therefore rejected from further analysis. We benchmarked the impact of different mutation and crossover operators as well as the influence of the population size on the remaining system and the two best optimization procedures namely binary Genetic Algorithm and the Evolution Strategy. The GA performed best on average and found the best total result based on the relative squared error.