Organisms have evolved a variety of mechanisms to cope with the unpredictability of environmental conditions, and yet mainstream models of metabolic regulation are typically based on strict optimality principles that do not account for uncertainty. This paper introduces a dynamic metabolic modelling framework that is a synthesis of recent ideas on resource allocation and the powerful optimal control formulation of Ramkrishna and colleagues. In particular, their work is extended based on the hypothesis that cellular resources are allocated among elementary flux modes according to the principle of maximum entropy. These concepts both generalise and unify prior approaches to dynamic metabolic modelling by establishing a smooth interpolation between dynamic flux balance analysis and dynamic metabolic models without regulation. The resulting theory is successful in describing 'bet-hedging' strategies employed by cell populations dealing with uncertainty in a fluctuating environment, including heterogenous resource investment, accumulation of reserves in growth-limiting conditions, and the observed behaviour of yeast growing in batch and continuous cultures. The maximum entropy principle is also shown to yield an optimal control law consistent with partitioning resources between elementary flux mode families, which has important practical implications for model reduction, selection, and simulation.

中文翻译：

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基于最大熵原理的动态代谢资源分配。

生物体已经进化出多种机制来应对环境条件的不可预测性，然而，新陈代谢调节的主流模型通常基于严格的最佳化原理，该原理不能解释不确定性。本文介绍了一个动态代谢建模框架，该框架是关于资源分配和Ramkrishna及其同事强大的最佳控制公式的最新思想的综合。尤其是，根据基于最大熵原理在基本通量模式之间分配蜂窝资源的假设，扩展了它们的工作。这些概念通过在动态流量平衡分析和动态代谢模型之间建立平滑插值而无需调节，从而概括并统一了动态代谢模型的现有方法。由此产生的理论成功地描述了细胞群在波动环境中处理不确定性的“对冲”策略，包括异质性资源投资，生长受限条件下的储备积累以及观察到的酵母分批和连续生长的行为。文化。还显示出最大熵原理产生了与在基本通量模式族之间分配资源一致的最优控制律，这对模型的简化，选择和仿真具有重要的实际意义。以及分批和连续培养的酵母生长行为。还显示出最大熵原理产生了与在基本通量模式族之间分配资源一致的最优控制律，这对模型的简化，选择和仿真具有重要的实际意义。以及分批和连续培养的酵母生长行为。还显示出最大熵原理产生了与在基本通量模式族之间分配资源一致的最优控制律，这对模型的简化，选择和仿真具有重要的实际意义。