ArticleJournal of the Royal Society, Interface2018
Adding levels of complexity enhances robustness and evolvability in a multilevel genotype-phenotype map.
Article in Journal of the Royal Society, Interface, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Mutational robustness and the role of buffer genes in evolvability.The EMBO journal · 2024Review
- Robustness and innovation in synthetic genotype networks.Nature communications · 2023Article
- Genetic characteristics influence the phenotype of marine macroalgaEcology and evolution · 2023Article
- Insertions and deletions in the RNA sequence-structure map.Journal of the Royal Society, Interface · 2021Article
- The Tiger Rattlesnake genome reveals a complex genotype underlying a simple venom phenotype.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Populations of genetic circuits are unable to find the fittest solution in a multilevel genotype-phenotype map.Journal of the Royal Society, Interface · 2020Article
- Model genotype-phenotype mappings and the algorithmic structure of evolution.Journal of the Royal Society, Interface · 2019Article
- On the networked architecture of genotype spaces and its critical effects on molecular evolution.Open biology · 2018Review
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Authors and funding
4 authors.
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Abstract
Robustness and evolvability are the main properties that account for the stability and accessibility of phenotypes. They have been studied in a number of computational genotype-phenotype maps. In this paper, we study a metabolic genotype-phenotype map defined in toyLIFE, a multilevel computational model that represents a simplified cellular biology. toyLIFE includes several levels of phenotypic expression, from proteins to regulatory networks to metabolism. Our results show that toyLIFE shares many similarities with other seemingly unrelated computational genotype-phenotype maps. Thus, toyLIFE shows a high degeneracy in the mapping from genotypes to phenotypes, as well as a highly skewed distribution of phenotypic abundances. The neutral networks associated with abundant phenotypes are highly navigable, and common phenotypes are close to each other in genotype space. All of these properties are remarkable, as toyLIFE is built on a version of the HP protein-folding model that is neither robust nor evolvable: phenotypes cannot be mutually accessed through point mutations. In addition, both robustness and evolvability increase with the number of genes in a genotype. Therefore, our results suggest that adding levels of complexity to the mapping of genotypes to phenotypes and increasing genome size enhances both these properties.
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