The advancements in genome editing techniques over the past years have rekindled interest in rational metabolic engineering strategies. While Metabolic Control Analysis (MCA) is a well-established method for quantifying the effects of metabolic engineering interventions on flows in metabolic networks and metabolic concentrations, it fails to account for the physiological limitations of the cellular environment and metabolic engineering design constraints. We report here a constraint-based framework based on MCA, Network Response Analysis (NRA), for the rational genetic strain design that incorporates biologically relevant constraints, as well as genome editing restrictions. The NRA core constraints being similar to the ones of Flux Balance Analysis, allow it to be used for a wide range of optimization criteria and with various physiological constraints. We show how the parametrization and introduction of biological constraints enhance the NRA formulation compared to the classical MCA approach, and we demonstrate its features and its ability to generate multiple alternative optimal strategies given several user-defined boundaries and objectives. In summary, NRA is a sophisticated alternative to classical MCA for rational metabolic engineering that accommodates the incorporation of physiological data at metabolic flux, metabolite concentration, and enzyme expression levels.

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基于约束的新陈代谢工程的代谢控制分析

过去几年中，基因组编辑技术的进步引起了人们对合理的代谢工程策略的兴趣。尽管代谢控制分析（MCA）是一种用于量化代谢工程干预对代谢网络中的流量和代谢浓度的影响的公认方法，但它无法解决细胞环境的生理局限性和代谢工程设计的限制。我们在这里报告基于约束的基于MCA的框架，网络响应分析（NRA），用于合理的遗传菌株设计，其中包含生物学相关的约束以及基因组编辑约束。NRA核心约束类似于通量平衡分析，使其可用于各种优化标准，并具有各种生理限制。我们展示了与经典MCA方法相比，参数化和引入生物学限制如何增强NRA配方，并展示了其功能以及在给定多个用户定义的边界和目标的情况下生成多种替代性最佳策略的能力。总而言之，对于合理的代谢工程，NRA是经典MCA的精密替代品，可在代谢通量，代谢物浓度和酶表达水平上适应生理数据的整合。并且在给定几个用户定义的边界和目标的情况下，我们展示了其功能以及生成多种替代最优策略的能力。总而言之，对于合理的代谢工程，NRA是经典MCA的精密替代品，可在代谢通量，代谢物浓度和酶表达水平上适应生理数据的整合。并且在给定几个用户定义的边界和目标的情况下，我们展示了其功能以及生成多种替代最优策略的能力。总而言之，对于合理的代谢工程，NRA是经典MCA的精密替代品，可在代谢通量，代谢物浓度和酶表达水平上适应生理数据的整合。