JOURNAL ARTICLE

The effect of mutational robustness on the evolvability of multicellular organisms and eukaryotic cells.

  • Published In: Journal of Evolutionary Biology, 2023, v. 36, n. 6. P. 906 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Jiang, Pengyao; Kreitman, Martin; Reinitz, John 3 of 3

Abstract

This article focuses on the theoretical relationship between canalization—defined as mutational robustness where phenotypes remain stable despite genetic mutations—and evolvability in multicellular and hierarchically organized organisms. Using a Boolean population genetic model that explicitly represents genotype, hierarchical phenotype (cell types), and environment, the study finds that high robustness is favored in constant environments, while lower robustness enhances adaptation following environmental change. Notably, multicellularity imposes strong constraints on robustness, shifting the peak of evolvability to very high robustness levels (~0.99), much higher than previously reported for simpler systems, and creating a sharp, discontinuous peak due to fixation dynamics. When robustness is genetically controlled, populations initially favor lower robustness after environmental shifts but eventually select for higher robustness in stable conditions; however, maximal long-term fitness requires recombination between robustness loci and other genes. These findings highlight the complex interplay between genetic architecture, hierarchical phenotype organization, and evolutionary dynamics, offering insights relevant to evolutionary biology and the study of developmental robustness across diverse eukaryotic organisms.

Additional Information

  • Source:Journal of Evolutionary Biology. 2023/06, Vol. 36, Issue 6, p906
  • Document Type:Article
  • Subject Area:Earth and Atmospheric Sciences
  • Publication Date:2023
  • ISSN:1010-061X
  • DOI:10.1111/jeb.14180
  • Accession Number:164136673
  • Copyright Statement:Copyright of Journal of Evolutionary Biology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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