ArticleNPJ systems biology and applications2024
Canalization reduces the nonlinearity of regulation in biological networks.
Article in NPJ systems biology and applications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- BoolForge: controlled generation and analysis of Boolean functions and networks.Bioinformatics (Oxford, England) · 2026Article
- Gene expression plasticity under multiple stresses drives higher tolerance to a macrolide in saline and warmer environments.npj antimicrobials and resistance · 2026Article
- Canalization as a stabilizing principle of gene regulatory networks: a discrete dynamical systems perspective.NPJ systems biology and applications · 2026Review
- Attractors are less stable than their basins: Canalization creates a coherence gap in gene regulatory networks.bioRxiv : the preprint server for biology · 2025Article
- HSP90 as an evolutionary capacitor drives adaptive eye size reduction via atonal.Nature communications · 2025Article
- Reverse control of biological networks to restore phenotype landscapes.Science advances · 2025Article
- Emergent dynamics of cellular decision making in multi-node mutually repressive regulatory networks.Journal of the Royal Society, Interface · 2025Article
- Operating principles of interconnected feedback loops driving cell fate transitions.NPJ systems biology and applications · 2025Article
- iDDN: determining trans-omics network structure and rewiring with integrative differential dependency networks.Bioinformatics advances · 2025Article
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Authors and funding
2 authors.
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Abstract
Biological networks, such as gene regulatory networks, possess desirable properties. They are more robust and controllable than random networks. This motivates the search for structural and dynamical features that evolution has incorporated into biological networks. A recent meta-analysis of published, expert-curated Boolean biological network models has revealed several such features, often referred to as design principles. Among others, the biological networks are enriched for certain recurring network motifs, the dynamic update rules are more redundant, more biased, and more canalizing than expected, and the dynamics of biological networks are better approximable by linear and lower-order approximations than those of comparable random networks. Since most of these features are interrelated, it is paramount to disentangle cause and effect, that is, to understand which features evolution actively selects for, and thus truly constitute evolutionary design principles. Here, we compare published Boolean biological network models with different ensembles of null models and show that the abundance of canalization in biological networks can almost completely explain their recently postulated high approximability. Moreover, an analysis of random N-K Kauffman models reveals a strong dependence of approximability on the dynamical robustness of a network.
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Registered trials
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