ArticleNature communications2022
Influence maximization in Boolean networks.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 21 citations in OpenAlex.
- Cell dynamics on energy landscape: Comparing attractor detection in Boolean network and diffusion-based models under in silico gene perturbations.Quantitative biology (Beijing, China) · 2026Article
- Dynamics of attractor transitions in Boolean networks under noise.Chaos (Woodbury, N.Y.) · 2026Article
- Canalization as a stabilizing principle of gene regulatory networks: a discrete dynamical systems perspective.NPJ systems biology and applications · 2026Review
- Reverse control of biological networks to restore phenotype landscapes.Science advances · 2025Article
- Article
- OneSC: a computational platform for recapitulating cell state transitions.Bioinformatics (Oxford, England) · 2024Article
- OneSC: A computational platform for recapitulating cell state transitions.bioRxiv : the preprint server for biology · 2024Article
- Dynamical methods for target control of biological networks.Royal Society open science · 2023Article
- Decoding dysregulated angiogenesis in HTLV-1 asymptomatic carriers compared to healthy individuals.Medical oncology (Northwood, London, England) · 2023Article
- A new stochastic diffusion model for influence maximization in social networks.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.