A method is presented to tackle primal multiplicity of Dynamic flux balance analysis (DFBA) which is a Linear Programming (LP) based modeling approach that assumes that the cell distributes fluxes such as to maximize a specific biological objective. When the LP problem has multiple optima, the LP solvers usually only report the first optimum that it is reached which may not fit well the experimental data. To tackle this primal multiplicity problem, the weighted primal-dual method with auxiliary parameters is used to calculate a unique time trajectory for a given set of initial conditions. Through tuning of these auxiliary parameters, a unique optimal solution can be obtained and calibrated to fit available experimental data. Beyond its capability to tackle multiplicity, the algorithm is shown to significantly improve the prediction of some metabolites in a case study of the fed-batch fermentation of Bordetella pertussis.

提出了一种方法来解决动态通量平衡分析（DFBA）的原始多样性，这是一种基于线性规划（LP）的建模方法，该方法假设细胞分配通量以最大化特定的生物学目的。当LP问题具有多个最优值时，LP解算器通常仅报告达到的第一个最优值，这可能不太适合实验数据。为了解决这个原始多重性问题，使用带有辅助参数的加权原始对偶方法来计算给定初始条件集的唯一时间轨迹。通过调整这些辅助参数，可以获得独特的最佳解决方案，并进行校准以适合可用的实验数据。除了解决多样性的能力外，百日咳博德特氏菌。