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A systematic approach for finding the objective function and active constraints for dynamic flux balance analysis

机译:寻找动态通量平衡分析的目标函数和主动约束的系统方法

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Dynamic flux balance analysis (DFBA) has become an instrumental modeling tool for describing the dynamic behavior of bioprocesses. DFBA involves the maximization of a biologically meaningful objective subject to kinetic constraints on the rate of consumption/production of metabolites. In this paper, we propose a systematic data-based approach for finding both the biological objective function and a minimum set of active constraints necessary for matching the model predictions to the experimental data. The proposed algorithm accounts for the errors in the experiments and eliminates the need for ad hoc choices of objective function and constraints as done in previous studies. The method is illustrated for two cases: (1) for in silico (simulated) data generated by a mathematical model for Escherichia coli and (2) for actual experimental data collected from the batch fermentation of Bordetella Pertussis (whooping cough).
机译:动态通量平衡分析(DFBA)已成为描述生物过程动态行为的工具建模工具。 DFBA涉及最大化具有生物学意义的目标,该目标受到代谢物消耗/产生速率的动力学约束。在本文中,我们提出了一种基于系统的基于数据的方法,用于查找生物学目标函数和将模型预测与实验数据进行匹配所需的最小活动约束集。所提出的算法解决了实验中的错误,并且消除了先前研究中对目标函数和约束进行临时选择的需要。说明了两种情况下的方法:(1)用于通过大肠杆菌数学模型生成的计算机模拟(模拟)数据,以及(2)从百日咳博德特氏菌分批发酵(w咳)中收集的实际实验数据。

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