Adding levels of complexity enhances robustness and evolvability in a multilevel genotype-phenotype map

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Robustness and evolvability are the main properties that account for the stability andaccessibility of phenotypes. They have been studied in a number of computationalgenotype- phenotype maps. In this contribution we study a metabolic genotypephenotypemap defined in toyLIFE, a multi-level computational model that representsa simplified cellular biology. toyLIFE includes several levels of phenotypic expression,from proteins to regulatory networks to metabolism. Our results show that toyLIFEshares many similarities with other seemingly unrelated computational genotypephenotypemaps. Thus, toyLIFE shows a high degeneracy in the mapping fromgenotypes to phenotypes, as well as a highly skewed distribution of phenotypicabundances. The neutral networks associated with abundant phenotypes are highlynavigable, and common phenotypes are close to each other in genotype space. Allof these properties are remarkable, as toyLIFE is built on a version of the HP proteinfolding model that is neither robust nor evolvable: phenotypes cannot be mutuallyaccessed through point mutations. In addition, both robustness and evolvabilityincrease with the number of genes in a genotype. Therefore, our results suggest thatadding levels of complexity to the mapping of genotypes to phenotypes and increasinggenome size enhances both these properties.
This paper has been presented at: 6th Meeting of the Spanish Society for Evolutionary Biology (SESBE): Palma,17th – 19th January 2018
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