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An extension to the NK fitness landscape model to study pleiotropy, epistasis, and ruggedness independently
Dalarna University, School of Information and Engineering, Microdata Analysis.ORCID iD: 0000-0001-9523-6689
Dalarna University, School of Information and Engineering, Microdata Analysis. BEACON Center for the Study of Evolution in Action Michigan State University East Lansing, USA.ORCID iD: 0000-0002-4872-1961
2022 (English)In: Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022, Institute of Electrical and Electronics Engineers Inc. , 2022, p. 1259-1267Conference paper, Published paper (Refereed)
Abstract [en]

The NK model is designed to study evolutionary adaptation in rugged fitness landscapes. The factor K determines the number of interacting genes, their degree of pleiotropy and epistasis, and consequently the ruggedness of the fitness landscape. However, in natural organisms, the degree of epistatic interactions and the number of functions a gene can have are to a certain degree determining the ruggedness of the landscape. Still, pleiotropy and epistasis can evolve independently from each other, and are to some degree independent of the ruggedness of the landscape. Here, we propose an extension to the standard NK model to investigate these factors independently of each other. Over the course of evolution the computational model organisms can now change how their genes interact and how they control phenotypic traits. Further, the degree of epistasis and pleiotropy is affected by the ruggedness of the landscape and becomes reduced with increasing ruggedness. While this proves that the extension of the model performs as expected, the adaptations are minor, presumably because only relatively short periods of adaptations with few mutations can be studied. © 2022 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2022. p. 1259-1267
Keywords [en]
Bioinformatics, Epistasi, Evolution, Evolutionary adaptation, Fitness land-scape, Fitness landscape, Interacting genes, Landscape model, NK-models, Pleiotropy, Ruggedness, Genes, epistasis
National Category
Evolutionary Biology
Identifiers
URN: urn:nbn:se:du-45563DOI: 10.1109/SSCI51031.2022.10022166ISI: 000971973800168Scopus ID: 2-s2.0-85147794543ISBN: 9781665487689 (print)OAI: oai:DiVA.org:du-45563DiVA, id: diva2:1741141
Conference
2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022, Singapore, 4-7 December 2022
Available from: 2023-03-03 Created: 2023-03-03 Last updated: 2023-06-12Bibliographically approved

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Mehra, PriyankaHintze, Arend

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf