Abstract
Dynamic Flux Balance Models (DFBM) can describe the evolution of metabolites’ concentrations with time by solving a linear programming (LP) problem at each time interval. However, since multiple solutions of the LP commonly exist, different trajectories can be obtained. An interior-point based method (weighted primal–dual method) proposed in a previous study that picks a specific solution from multiple solutions of LP is computationally expensive for a large metabolic network. In this research, we propose an alternative computationally efficient method based on an ellipsoidal reflection mathematical operation. The method is applied to different examples to demonstrate the algorithm and its computational efficiency and then for modeling the fermentation of B. pertussis resulting in excellent computational performance and accurate fitting of experimental dynamic data.
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