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Influence maximization in Boolean networks
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AI
DOI：10.1038/s41467-022-31066-0.png)
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摘要
En 中文
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. Boolean networks modelling various biological processes are characterized by nonlinear reversible dynamics that makes their control challenging. The authors introduce extended concepts of influence and control, typically considered in the study of spreading processes, for Boolean dynamics.
Keyword:
REGULATORY NETWORKS
COMPLEX NETWORKS
EXTERNAL CONTROL
MODEL
DYNAMICS
CONTROLLABILITY
NODES
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期刊
IF:
15.7
论文数:
9.3W
被引数:
91.2W
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