Genetic networks encode secrets of their past

dc.contributor.authorCrawford-Kahrl, Peter
dc.contributor.authorNerem, Robert R.
dc.contributor.authorCummins, Bree
dc.contributor.authorGedeon, Tomas
dc.date.accessioned2022-08-30T20:19:51Z
dc.date.available2022-08-30T20:19:51Z
dc.date.issued2022-03
dc.description© This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.description.abstractResearch shows that gene duplication followed by either repurposing or removal of duplicated genes is an important contributor to evolution of gene and protein interaction networks. We aim to identify which characteristics of a network can arise through this process, and which must have been produced in a different way. To model the network evolution, we postulate vertex duplication and edge deletion as evolutionary operations on graphs. Using the novel concept of an ancestrally distinguished subgraph, we show how features of present-day networks require certain features of their ancestors. In particular, ancestrally distinguished subgraphs cannot be introduced by vertex duplication. Additionally, if vertex duplication and edge deletion are the only evolutionary mechanisms, then a graph’s ancestrally distinguished subgraphs must be contained in all of the graph’s ancestors. We analyze two experimentally derived genetic networks and show that our results accurately predict lack of large ancestrally distinguished subgraphs, despite this feature being statistically improbable in associated random networks. This observation is consistent with the hypothesis that these networks evolved primarily via vertex duplication. The tools we provide open the door for analyzing ancestral networks using current networks. Our results apply to edge-labeled (e.g. signed) graphs which are either undirected or directed.en_US
dc.identifier.citationCrawford-Kahrl, P., Nerem, R. R., Cummins, B., & Gedeon, T. (2022). Genetic networks encode secrets of their past. Journal of Theoretical Biology, 541, 111092.en_US
dc.identifier.issn0022-5193
dc.identifier.urihttps://scholarworks.montana.edu/handle/1/17028
dc.language.isoen_USen_US
dc.publisherElsevier BVen_US
dc.rightscc-by-nc-nden_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.subjectgenetic networksen_US
dc.titleGenetic networks encode secrets of their pasten_US
dc.typeArticleen_US
mus.citation.extentfirstpage1en_US
mus.citation.extentlastpage7en_US
mus.citation.journaltitleJournal of Theoretical Biologyen_US
mus.citation.volume541en_US
mus.identifier.doi10.1016/j.jtbi.2022.111092en_US
mus.relation.collegeCollege of Letters & Scienceen_US
mus.relation.departmentMathematical Sciences.en_US
mus.relation.universityMontana State University - Bozemanen_US

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