Wilson, S.P. orcid.org/0000-0001-8125-5133, James, S.S. orcid.org/0000-0003-0208-0588, Whiteley, D.J. et al. (1 more author) (2019) Limit cycle dynamics can guide the evolution of gene regulatory networks towards point attractors. Scientific Reports, 9 (1). 16750.
Abstract
Developmental dynamics in Boolean models of gene networks self-organize, either into point attractors (stable repeating patterns of gene expression) or limit cycles (stable repeating sequences of patterns), depending on the network interactions specified by a genome of evolvable bits. Genome specifications for dynamics that can map specific gene expression patterns in early development onto specific point attractor patterns in later development are essentially impossible to discover by chance mutation alone, even for small networks. We show that selection for approximate mappings, dynamically maintained in the states comprising limit cycles, can accelerate evolution by at least an order of magnitude. These results suggest that self-organizing dynamics that occur within lifetimes can, in principle, guide natural selection across lifetimes.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 The Authors. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
Keywords: | Evolutionary theory; Gene regulatory networks |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > Department of Psychology (Sheffield) |
Funding Information: | Funder Grant number JAMES S MCDONNELL FOUNDATION UNSPECIFIED |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 18 Nov 2019 12:29 |
Last Modified: | 18 Nov 2019 12:37 |
Status: | Published |
Publisher: | Springer Science and Business Media LLC |
Refereed: | Yes |
Identification Number: | 10.1038/s41598-019-53251-w |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:153502 |