Living organism is an intelligent system encoded by hierarchically-organized information to perform precisely-controlled biological functions. Biophysical models are important tools to uncover the design rules underlying complex genetic-metabolic circuit interactions. Based on a previously engineered synthetic malonyl-CoA switch (Xu et al, PNAS 2014), we have formulated nine differential equations to unravel the design principles underlying an ideal metabolic switch to improve fatty acids production in E. coli. By interrogating the physiologically accessible parameter space, we have determined the optimal controller architecture to configure both the metabolic source pathway and metabolic sink pathway. We determined that low protein degradation rate, medium strength of metabolic inhibitory constant, high metabolic source pathway induction rate, strong binding affinity of the transcriptional activator toward the metabolic source pathway, weak binding affinity of the transcriptional repressor toward the metabolic sink pathway, and a strong cooperative interaction of transcriptional repressor toward metabolic sink pathway benefit the accumulation of the target molecule (fatty acids). The target molecule (fatty acid) production is increased from 50% to 10-folds upon application of the autonomous metabolic switch. With strong metabolic inhibitory constant, the system displays multiple steady states. Stable oscillation of metabolic intermediate is the driving force to allow the system deviate from its equilibrium state and permits bidirectional ON-OFF gene expression control, which autonomously compensates enzyme level for both the metabolic source and metabolic sink pathways. The computational framework may facilitate us to design and engineer predictable genetic-metabolic switches, quest for the optimal controller architecture of the metabolic source/sink pathways, as well as leverage autonomous oscillation as a powerful tool to engineer cell function.

中文翻译：

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丙二酰-CoA节点的分支点控制：一个计算框架，揭示理想的遗传-代谢开关的设计原理

活生物体是由分层组织的信息编码的智能系统，可以执行精确控制的生物学功能。生物物理模型是揭示复杂的遗传-代谢循环相互作用基础的设计规则的重要工具。基于先前设计的合成丙二酰-CoA开关（Xu等人，PNAS 2014），我们制定了九个微分方程式以阐明理想的代谢开关基础的设计原理，以改善大肠杆菌中的脂肪酸产量。通过询问生理学上可访问的参数空间，我们已经确定了最佳的控制器体系结构来配置代谢源途径和代谢宿途径。我们确定了低蛋白降解率，中等强度的代谢抑制常数，高代谢源途径诱导率，转录激活因子对代谢源途径的强结合亲和力，转录抑制因子对代谢库途径的弱结合亲和力和转录阻遏物向代谢库途径的强合作相互作用有利于靶分子（脂肪酸）的积累。应用自主代谢开关后，目标分子（脂肪酸）的产量从50％增加到10倍。具有强大的代谢抑制常数，系统显示多个稳态。代谢中间产物的稳定振荡是使系统偏离其平衡状态的驱动力，并允许双向ON-OFF基因表达控制，从而自动补偿了代谢源和代谢库途径的酶水平。该计算框架可以帮助我们设计和设计可预测的遗传-代谢开关，寻求代谢源/库途径的最佳控制器架构，以及利用自主振荡作为设计细胞功能的强大工具。可以自动补偿代谢源和代谢汇途径的酶水平。该计算框架可以帮助我们设计和设计可预测的遗传-代谢开关，寻求代谢源/库途径的最佳控制器架构，以及利用自主振荡作为设计细胞功能的强大工具。可以自动补偿代谢源和代谢汇途径的酶水平。该计算框架可以帮助我们设计和设计可预测的遗传-代谢开关，寻求代谢源/库途径的最佳控制器架构，以及利用自主振荡作为设计细胞功能的强大工具。