Computational method for inferring objective function of glycerol metabolism in Klebsiella pneumoniae

Flux balance analysis (FBA) is an effective tool in the analysis of metabolic network. It can predict the flux distribution of engineered cells, whereas the accurate prediction depends on the reasonable objective function. In this work, we propose two nonlinear bilevel programming models on anaerobic glycerol metabolism in Klebsiella pneumoniae (K. pneumoniae) for 1,3-propanediol (1,3-PD) production. One intends to infer the metabolic objective function, and the other is to analyze the robustness of the objective function. In view of the models' characteristic an improved genetic algorithm is constructed to solve them, where some techniques are adopted to guarantee all chromosomes are feasible and move quickly towards the global optimal solution. Numerical results reveal some interesting conclusions, e.g., biomass production is the main force to drive K. pneumoniae metabolism, and the objective functions, which are obtained in term of several different groups of flux distributions, are similar.