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动力学模块是生化网络中浓度稳健性的来源。

Kinetic modules are sources of concentration robustness in biochemical networks.

作者信息

Langary Damoun, Küken Anika, Nikoloski Zoran

机构信息

Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany.

Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany.

出版信息

Sci Adv. 2025 May 30;11(22):eads7269. doi: 10.1126/sciadv.ads7269. Epub 2025 May 28.

Abstract

Modules represent fundamental building blocks of cellular networks and are thought to facilitate robustness of phenotypes against perturbations. While reaction kinetic shapes the concentration of components and reaction rates, its use in identification of modules entails knowledge of parameter values. Here, we demonstrate that kinetic modules can be efficiently identified on the basis of steady-state reaction rate couplings in large-scale biochemical networks endowed with mass action kinetics without knowledge of parameter values. We then link the kinetic modules of metabolic networks with robustness of metabolite concentrations to perturbations. Analyzing 34 metabolic network models of 26 organisms, we demonstrate that the ordered binding enzyme mechanism leads to increased concentration robustness compared to random binding. Our findings pave the way for usage of modules in synthetic biology and biotechnological applications.

摘要

模块是细胞网络的基本构建单元,被认为有助于表型对扰动的鲁棒性。虽然反应动力学塑造了组分的浓度和反应速率,但在模块识别中使用它需要参数值的知识。在这里,我们证明了在具有质量作用动力学的大规模生化网络中,基于稳态反应速率耦合,无需参数值知识就可以有效地识别动力学模块。然后,我们将代谢网络的动力学模块与代谢物浓度对扰动的鲁棒性联系起来。通过分析26种生物体的34个代谢网络模型,我们证明与随机结合相比,有序结合酶机制导致浓度鲁棒性增加。我们的发现为模块在合成生物学和生物技术应用中的使用铺平了道路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ccba/12118627/fc0ba67a01f0/sciadv.ads7269-f1.jpg

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