Computationally efficient dynamic simulation of cellular kinetics via explicit solution of flux balance analysis: xDFBA modelling and its biochemical process applications

Recent developments in reconstruction of genome scale metabolism have seen their applications in predicting cell's dynamic responses by combining intracellular metabolism with macro-scale kinetic models. This, however, can be computationally prohibitive for genome-scale metabolic models because a large-scale linear program is solved repeatedly between integration time step. In addition, when this model is used to applications such as parameter estimation or optimal control, the resulting problem becomes bi-level optimization problem, which is difficult to solve real-time. This study proposes a novel dynamic flux balance analysis framework, which constructs intracellular fluxes parameterized as functions of boundary conditions a priori. This explicit solution does not require solving linear programs successively and can be readily exploited by macro-scale kinetic models. We refer to the proposed approach as xDFBA meaning dynamic flux balance analysis embedded with explicit solutions. The method is applied to the growth models of Escherichia coli and Saccharomyces cerevisiae and shows identical prediction capability but reduced computational load, compared to the result of existing DFBA schemes. Finally, by applying parameter estimation and optimal control to the kinetic model of E. coli with xDFBA approach, its biochemical applicability is demonstrated.