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细胞决定代谢物浓度范围。

Cellular determinants of metabolite concentration ranges.

机构信息

System Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.

Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam-Golm, Germany.

出版信息

PLoS Comput Biol. 2019 Jan 24;15(1):e1006687. doi: 10.1371/journal.pcbi.1006687. eCollection 2019 Jan.

Abstract

Cellular functions are shaped by reaction networks whose dynamics are determined by the concentrations of underlying components. However, cellular mechanisms ensuring that a component's concentration resides in a given range remain elusive. We present network properties which suffice to identify components whose concentration ranges can be efficiently computed in mass-action metabolic networks. We show that the derived ranges are in excellent agreement with simulations from a detailed kinetic metabolic model of Escherichia coli. We demonstrate that the approach can be used with genome-scale metabolic models to arrive at predictions concordant with measurements from Escherichia coli under different growth scenarios. By application to 14 genome-scale metabolic models from diverse species, our approach specifies the cellular determinants of concentration ranges that can be effectively employed to make predictions for a variety of biotechnological and medical applications.

摘要

细胞功能由反应网络塑造,其动态由基础成分的浓度决定。然而,确保成分浓度保持在给定范围内的细胞机制仍难以捉摸。我们提出了网络特性,足以识别其浓度范围可以在质量作用代谢网络中有效计算的成分。我们表明,推导的范围与大肠杆菌详细的动力学代谢模型的模拟非常吻合。我们证明,该方法可以与基因组规模的代谢模型一起使用,从而根据不同的生长情况,从大肠杆菌的测量结果得出一致的预测。通过对来自不同物种的 14 个基因组规模的代谢模型的应用,我们的方法指定了浓度范围的细胞决定因素,可以有效地用于为各种生物技术和医学应用做出预测。

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