Influence maximization in Boolean networks

Thomas Parmer, Luis M. Rocha and Filippo Radicchi ()
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Thomas Parmer: Indiana University
Luis M. Rocha: Binghamton University (State University of New York)
Filippo Radicchi: Indiana University

Nature Communications, 2022, vol. 13, issue 1, 1-11

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-31066-0

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