ArticleNature communications2021
The basis of easy controllability in Boolean networks.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- LogicSR: prior-guided symbolic regression for gene regulatory network inference from single-cell transcriptomics data.Briefings in bioinformatics · 2025Article
- Canalization reduces the nonlinearity of regulation in biological networks.NPJ systems biology and applications · 2024Article
- LogicGep: Boolean networks inference using symbolic regression from time-series transcriptomic profiling data.Briefings in bioinformatics · 2024Article
- The effect of deep brain stimulation on cortico-subcortical networks in Parkinson's disease patients with freezing of gait: Exhaustive exploration of a basic model.Network neuroscience (Cambridge, Mass.) · 2024Article
- Modularity of biological systems: a link between structure and function.Journal of the Royal Society, Interface · 2023Article
- Modularity of biological systems: a link between structure and function.bioRxiv : the preprint server for biology · 2023Article
- Phenotype Control techniques for Boolean gene regulatory networks.Bulletin of mathematical biology · 2023Review
- Phenotype control techniques for Boolean gene regulatory networks.bioRxiv : the preprint server for biology · 2023Article
- The nonlinearity of regulation in biological networks.NPJ systems biology and applications · 2023Article
- Influence maximization in Boolean networks.Nature communications · 2022Article
- Translocating proteins compartment-specifically alter the fate of epithelial-mesenchymal transition in a compartmentalized Boolean network model.NPJ systems biology and applications · 2022Article
- Discovering sparse control strategies in neural activity.PLoS computational biology · 2022Article
- Dynamical Analysis of a Boolean Network Model of the Oncogene Role of lncRNA ANRIL and lncRNA UFC1 in Non-Small Cell Lung Cancer.Biomolecules · 2022Article
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Authors and funding
2 authors.
Funding
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Abstract
Effective control of biological systems can often be achieved through the control of a surprisingly small number of distinct variables. We bring clarity to such results using the formalism of Boolean dynamical networks, analyzing the effectiveness of external control in selecting a desired final state when that state is among the original attractors of the dynamics. Analyzing 49 existing biological network models, we find strong numerical evidence that the average number of nodes that must be forced scales logarithmically with the number of original attractors. This suggests that biological networks may be typically easy to control even when the number of interacting components is large. We provide a theoretical explanation of the scaling by separating controlling nodes into three types: those that act as inputs, those that distinguish among attractors, and any remaining nodes. We further identify characteristics of dynamics that can invalidate this scaling, and speculate about how this relates more broadly to non-biological systems.
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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.