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The interdependent network of gene regulation and metabolism is robust where it needs to be

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  • David F. Klosik

    (University of Bremen)

  • Anne Grimbs

    (Jacobs University)

  • Stefan Bornholdt

    (University of Bremen)

  • Marc-Thorsten Hütt

    (Jacobs University)

Abstract

Despite being highly interdependent, the major biochemical networks of the living cell—the networks of interacting genes and of metabolic reactions, respectively—have been approached mostly as separate systems so far. Recently, a framework for interdependent networks has emerged in the context of statistical physics. In a first quantitative application of this framework to systems biology, here we study the interdependent network of gene regulation and metabolism for the model organism Escherichia coli in terms of a biologically motivated percolation model. Particularly, we approach the system’s conflicting tasks of reacting rapidly to (internal and external) perturbations, while being robust to minor environmental fluctuations. Considering its response to perturbations that are localized with respect to functional criteria, we find the interdependent system to be sensitive to gene regulatory and protein-level perturbations, yet robust against metabolic changes. We expect this approach to be applicable to a range of other interdependent networks.

Suggested Citation

  • David F. Klosik & Anne Grimbs & Stefan Bornholdt & Marc-Thorsten Hütt, 2017. "The interdependent network of gene regulation and metabolism is robust where it needs to be," Nature Communications, Nature, vol. 8(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-00587-4
    DOI: 10.1038/s41467-017-00587-4
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    Cited by:

    1. Zheng, Kexian & Liu, Ying & Gong, Jie & Wang, Wei, 2022. "Robustness of circularly interdependent networks," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    2. Chen, Wei & Yang, Zhihu & Wu, Te, 2021. "Evolution of cooperation driven by collective interdependence on multilayer networks," Applied Mathematics and Computation, Elsevier, vol. 388(C).
    3. Dong, Gaogao & Luo, Yanting & Liu, Yangyang & Wang, Fan & Qin, Huanmei & Vilela, André L.M., 2022. "Percolation behaviors of a network of networks under intentional attack with limited information," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).

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