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dc.contributor.authorEgri-Nagy, Attila
dc.contributor.authorNehaniv, C.L.
dc.date.accessioned2016-03-03T10:20:13Z
dc.date.available2016-03-03T10:20:13Z
dc.date.issued2008
dc.identifier.citationEgri-Nagy , A & Nehaniv , C L 2008 , ' Hierarchical coordinate systems for understanding complexity and its evolution with applications to genetic regulatory networks ' , Artificial Life , vol. 14 , no. 3 , pp. 299-312 . https://doi.org/10.1162/artl.2008.14.3.14305
dc.identifier.issn1064-5462
dc.identifier.otherPURE: 302411
dc.identifier.otherPURE UUID: c96d8258-7103-4271-aaeb-1a16c31b6bab
dc.identifier.otherScopus: 50649115542
dc.identifier.urihttp://hdl.handle.net/2299/16600
dc.descriptionOriginal article can be found at : http://www.mitpressjournals.org/ Copyright MIT Press
dc.description.abstractBeyond complexity measures, sometimes it is worth in addition investigating how complexity changes structurally, especially in artificial systems where we have complete knowledge about the evolutionary process. Hierarchical decomposition is a useful way of assessing structural complexity changes of organisms modeled as automata, and we show how recently developed computational tools can be used for this purpose, by computing holonomy decompositions and holonomy complexity. To gain insight into the evolution of complexity, we investigate the smoothness of the landscape structure of complexity under minimal transitions. As a proof of concept, we illustrate how the hierarchical complexity analysis reveals symmetries and irreversible structure in biological networks by applying the methods to the lac operon mechanism in the genetic regulatory network of Escherichia coli.en
dc.language.isoeng
dc.relation.ispartofArtificial Life
dc.titleHierarchical coordinate systems for understanding complexity and its evolution with applications to genetic regulatory networksen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionAdaptive Systems
dc.description.statusPeer reviewed
rioxxterms.versionAM
rioxxterms.versionofrecordhttps://doi.org/10.1162/artl.2008.14.3.14305
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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