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Influence maximization in Boolean networks.


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.

SUBMITTER: Parmer T 

PROVIDER: S-EPMC9203747 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Influence maximization in Boolean networks.

Parmer Thomas T   Rocha Luis M LM   Radicchi Filippo F  

Nature communications 20220616 1


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 soci  ...[more]

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