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Influence maximization in Boolean networks

Author

Listed:
  • Thomas Parmer

    (Indiana University)

  • Luis M. Rocha

    (Binghamton University (State University of New York)
    Instituto Gulbenkian de Ciência)

  • Filippo Radicchi

    (Indiana University)

Abstract

The optimization problem aiming at the identification of minimal sets of nodes able to drive the dynamics of Boolean networks toward desired long-term behaviors is central for some applications, as for example the detection of key therapeutic targets to control pathways in models of biological signaling and regulatory networks. Here, we develop a method to solve such an optimization problem taking inspiration from the well-studied problem of influence maximization for spreading processes in social networks. We validate the method on small gene regulatory networks whose dynamical landscapes are known by means of brute-force analysis. We then systematically study a large collection of gene regulatory networks. We find that for about 65% of the analyzed networks, the minimal driver sets contain less than 20% of their nodes.

Suggested Citation

  • Thomas Parmer & Luis M. Rocha & Filippo Radicchi, 2022. "Influence maximization in Boolean networks," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31066-0
    DOI: 10.1038/s41467-022-31066-0
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    References listed on IDEAS

    as
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