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CANA v1.0.0: efficient quantification of canalization in automata networks

2025-10-02Research article Open access
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dc.contributor.authorMarcus, Austin M.
dc.contributor.authorRozum, Jordan
dc.contributor.authorSizek, Herbert
dc.contributor.authorRocha, Luis M.
dc.date.accessioned2025-10-20T11:44:02Z
dc.date.available2025-10-20T11:44:02Z
dc.date.issued2025-10-02
dc.description.abstractThe biomolecular networks underpinning cell function exhibit canalization, or the buffering of fluctuations required to function in a noisy environment. We present a new major release of CANA, v1.0.0, an open-source Python package for understanding canalization in automata network models, discrete dynamical systems in which activation of biomolecular entities (e.g. transcription of genes) is modeled as the activity of coupled automata. One understudied putative mechanism for canalization is the functional equivalence of biomolecular regulators (e.g. among the transcription factors for a gene). We study this mechanism using the theory of symmetry in discrete functions. We present a new exact method, schematodes, for finding maximal symmetry groups among the inputs to discrete functions, and integrate it into CANA. The schematodes method substantially outperforms the inexact method of previous CANA versions both in speed and accuracy. We apply CANA v1.0.0 to study symmetry in 74 experimentally supported automata network models from the Cell Collective (CC) repository. The symmetry distribution is significantly different in the CC than in random automata with the same in-degree (connectivity) and bias (average output) (Kolmogorov-Smirnov test, P???.001). Its spread is much wider than in a null model (IQR 0.31 versus IQR 0.20 with equal medians), demonstrating that the CC is enriched in functions with extreme symmetry or asymmetry.eng
dc.identifier.citationMarcus, A. M., Rozum, J., Sizek, H., & Rocha, L. M. (2025). CANA v1.0.0: efficient quantification of canalization in automata networks. Bioinformatics (Oxford, England), 41(10), Article btaf461. https://doi.org/10.1093/bioinformatics/btaf461
dc.identifier.doi10.1093/bioinformatics/btaf461
dc.identifier.eid105018331041
dc.identifier.issn1367-4803
dc.identifier.other56728a70-0dce-4349-9408-3ce1bfdc65e5
dc.identifier.pmcPMC12512137
dc.identifier.pmid40848247
dc.identifier.urihttp://hdl.handle.net/10400.14/55409
dc.identifier.wos001590558700001
dc.language.isoeng
dc.peerreviewedyes
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleCANA v1.0.0: efficient quantification of canalization in automata networkseng
dc.typeresearch article
dspace.entity.typePublication
oaire.citation.issue10
oaire.citation.titleBioinformatics (Oxford, England)
oaire.citation.volume41
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85

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