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Link to original content: https://doi.org/10.1007/978-3-030-45016-8_3
Avalanches of Perturbations in Modular Gene Regulatory Networks | SpringerLink
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Avalanches of Perturbations in Modular Gene Regulatory Networks

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Artificial Life and Evolutionary Computation (WIVACE 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1200))

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Abstract

A well-known hypothesis, with far-reaching implications, is that biological evolution should preferentially lead to critical dynamic regimes. Useful information about the dynamical regime of gene regulatory networks can be obtained by studying their responses to small perturbations. The interpretation of these data requires the use of suitable models, where it is usually assumed that the system is homogeneous. On the other hand, it is widely acknowledged that biological networks display some degree of modularity, so it is interesting to ascertain how modularity can affect the estimation of their dynamical properties. In this study we introduce a well-defined degree of modularity and we study how it influences the network dynamics. In particular, we show how the estimate of the Derrida parameter from “avalanche” data may be affected by strong modularity.

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Notes

  1. 1.

    Note that in such a way the activation function of every node is kept unchanged, the only alteration being the source of the input link. In this way the process preserves the contribution of the nodes to the overall dynamic regime.

  2. 2.

    All links are chosen in order to prevent multiple redirections.

  3. 3.

    In any case, the behavior of networks with different number of nodes has been considered without noting qualitatively different behaviors.

  4. 4.

    That is, 100 different networks, each network measured in 10000 initial conditions for the Derrida parameter estimate, and 20000 different networks in order to obtain the avalanche distribution.

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Vezzani, A., Villani, M., Serra, R. (2020). Avalanches of Perturbations in Modular Gene Regulatory Networks. In: Cicirelli, F., Guerrieri, A., Pizzuti, C., Socievole, A., Spezzano, G., Vinci, A. (eds) Artificial Life and Evolutionary Computation. WIVACE 2019. Communications in Computer and Information Science, vol 1200. Springer, Cham. https://doi.org/10.1007/978-3-030-45016-8_3

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  • DOI: https://doi.org/10.1007/978-3-030-45016-8_3

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