全局资源分配策略控制大肠杆菌的增长转变动力学

上传 / 管理员 ·2017-11-12 生物学,生物物理,分子干预,生物代谢,增长转变动力学

论文标题 / A global resource allocation strategy governs growth transition kinetics of Escherichia coli

作者 / Ulrich Gerland, Terence Hwa, et al.

期刊 / Nature

发表时间 / 2017-10-25

数字识别码 / 10.1038/nature24299

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【论文摘要】

A grand challenge of systems biology is to predict the kinetic responses of living systems to perturbations starting from the underlying molecular interactions. Changes in the nutrient environment have long been used to study regulation and adaptation phenomena in microorganisms and they remain a topic of active investigation. Although much is known about the molecular interactions that govern the regulation of key metabolic processes in response to applied perturbations, they are insufficiently quantified for predictive bottom-up modelling. Here we develop a top-down approach, expanding the recently established coarse-grained proteome allocation models from steady-state growth into the kinetic regime. Using only qualitative knowledge of the underlying regulatory processes and imposing the condition of flux balance, we derive a quantitative model of bacterial growth transitions that is independent of inaccessible kinetic parameters. The resulting flux-controlled regulation model accurately predicts the time course of gene expression and biomass accumulation in response to carbon upshifts and downshifts (for example, diauxic shifts) without adjustable parameters. As predicted by the model and validated by quantitative proteomics, cells exhibit suboptimal recovery kinetics in response to nutrient shifts owing to a rigid strategy of protein synthesis allocation, which is not directed towards alleviating specific metabolic bottlenecks. Our approach does not rely on kinetic parameters, and therefore points to a theoretical framework for describing a broad range of such kinetic processes without detailed knowledge of the underlying biochemical reactions.

 

科研圈

(导读 郭思瑶)为了更有效地定量预测分子干预对生物代谢过程的影响,科学家研发了一个从上至下的定量模型方法,只利用到了调节过程和通量平衡的定量知识,可模拟细菌生长转变而不依赖于不可获知的动力学参数。这为描述更加广泛的动力学过程提供了有效的途径。

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