Kinetic modeling of multiple-strain artificial consortium to improve fengycin production of Bacillus subtilis

Artificial microbial consortium has been widely employed to improve the production of fengycin, a natural lipopeptide. Kinetic models are essential for understanding and predicting the dynamic behavior of metabolic systems, especially microbial consortia. Since the time evolution of metabolite concentrations and biomass is continuous and dynamic, systems of ordinary differential equations (ODEs) provide a natural and effective framework for capturing such interactions. In this work, a kinetic model based on ODEs was established to describe a multi-strain artificial consortium for fengycin synthesis, utilizing Bacillus subtilis, Yarrowia lipolytica, and Corynebacterium glutamicum as target strains.

The model captures microbial growth, intermediate metabolite formation, final product synthesis, and substrate consumption. It was successfully applied to analyze and interpret the cultivation data of target strains on various substrates. The model explicitly incorporates the accumulation of amino acids synthesized by C. glutamicum, the accumulation of fatty acids synthesized by Y. lipolytica, and the process in which B. subtilis utilized amino acids and fatty acids as partial precursors for fengycin production. The mathematical model ended up as a nonlinear ordinary differential system, which was solved with an adaptive step-size Runge-Kutta method, coupled with a genetic algorithm to roughly estimate the optimal model parameters associated with cellular growth, substrate consumption, and product level in fermentation broths.

The numerical results of the kinetic model agreed well with experimental data, and all seven sets of experimental conditions were fitted with overall relative errors ranging from 7.4 to 15.1%. This kinetic modeling provided a meaningful tool for the rational design and construction of further artificial consortia.