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dc.contributor.authorKnabe, J.
dc.contributor.authorNehaniv, C.L.
dc.contributor.authorSchilstra, M.
dc.identifier.citationKnabe , J , Nehaniv , C L & Schilstra , M 2008 , ' Do Motifs Reflect Evolved Function? – No Convergent Evolution of Genetic Regulatory Network Subgraph Topologies ' , Biosystems , vol. 94 , no. 1-2 , pp. 68-74 .
dc.identifier.otherPURE: 87418
dc.identifier.otherPURE UUID: 3f8621e1-f58c-4b57-bb79-298f03c11934
dc.identifier.otherdspace: 2299/5195
dc.identifier.otherScopus: 53749107637
dc.descriptionOriginal article can be found at: Copyright Elsevier Ireland Ltd. [Full text of this article is not available in the UHRA]
dc.description.abstractMethods that analyse the topological structure of networks have recently become quite popular. Whether motifs (subgraph patterns that occur more often than in randomized networks) have specific functions as elementary computational circuits has been cause for debate. As the question is difficult to resolve with currently available biological data, we approach the issue using networks that abstractly model natural genetic regulatory networks (GRNs) which are evolved to show dynamical behaviors. Specifically one group of networks was evolved to be capable of exhibiting two different behaviors (“differentiation”) in contrast to a group with a single target behavior. In both groups we find motif distribution differences within the groups to be larger than differences between them, indicating that evolutionary niches (target functions) do not necessarily mold network structure uniquely. These results show that variability operators can have a stronger influence on network topologies than selection pressures, especially when many topologies can create similar dynamics. Moreover, analysis of motif functional relevance by lesioning did not suggest that motifs were of greater importance to the functioning of the network than arbitrary subgraph patterns.en
dc.titleDo Motifs Reflect Evolved Function? – No Convergent Evolution of Genetic Regulatory Network Subgraph Topologiesen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.description.statusPeer reviewed
rioxxterms.typeJournal Article/Review

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