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Dynamical robustness and its structural dependence in biological networks.

Natsuhiro Ichinose1, Takeshi Kawashima2, Tetsushi Yada3

  • 1Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan.

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Dynamically robust biological networks, like neural networks, have low indegree variance and high outdegree variance. This robustness is linked to narrow basins of attraction for equilibrium states.

Keywords:
Basin of attractionBiological networkDynamical robustnessStructural dependence

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Area of Science:

  • Computational neuroscience
  • Systems biology
  • Network science

Background:

  • Biological networks, including neural and gene regulatory networks, exhibit complex dynamics.
  • Understanding the factors contributing to the stability and robustness of these networks is crucial.

Purpose of the Study:

  • To investigate the relationship between network structure and dynamical robustness in biological networks.
  • To propose and validate a machine learning approach for analyzing network dynamics and robustness.

Main Methods:

  • Theoretical analysis of directed graphs representing biological networks.
  • Development of a machine learning method to assign equilibrium states to recurrent input-output networks.
  • Empirical verification using learned networks with varied indegree and outdegree distributions.

Main Results:

  • Networks with low indegree variance and high outdegree variance demonstrate enhanced dynamical robustness.
  • The proposed machine learning method successfully assigns equilibrium states.
  • Dynamically robust networks are characterized by narrow basins of attraction for their equilibrium states.

Conclusions:

  • Network topology, specifically indegree and outdegree variance, significantly influences dynamical robustness.
  • Machine learning offers a viable approach to study and predict the robustness of biological networks.
  • The narrow basins of attraction in robust networks suggest specific stability properties.