Constraint-based, genome-scale metabolic models are an essential tool to guide metabolic engineering. However, they lack the detail and time dimension that kinetic models with enzyme dynamics offer. Model reduction can be used to bridge the gap between the two methods and allow for the integration of kinetic models into the Design-Built-Test-Learn cycle. Here we show that these reduced size models can be representative of the dynamics of the original model and demonstrate the automated generation and parameterisation of such models. Using these minimal models of metabolism could allow for further exploration of dynamic responses in metabolic networks.

中文翻译：

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基因组规模代谢模型的模型简化作为靶向动力学模型的基础

基于约束的基因组规模代谢模型是指导代谢工程的重要工具。然而，它们缺乏酶动力学动力学模型提供的细节和时间维度。模型简化可用于弥合两种方法之间的差距，并允许将动力学模型集成到设计-构建-测试-学习循环中。在这里，我们展示了这些缩小尺寸的模型可以代表原始模型的动态，并演示此类模型的自动生成和参数化。使用这些最小代谢模型可以进一步探索代谢网络中的动态响应。