toyLIFE: a computational framework to study the multi-level organization of the genotype-phenotype map

TL;DR

toyLIFE is a multi-level computational model that simulates the genotype-phenotype relationship, allowing exploration of how mutations influence phenotypes and how different biological levels are interconnected.

Contribution

It introduces toyLIFE, a novel multi-level framework that models the genotype-phenotype map using simple genomes and interaction rules, enabling study of complex emergent behaviors.

Findings

Emergent gene regulatory networks and metabolism in toyLIFE

Mutations impact phenotypes through multi-level interactions

Framework adaptable for evolutionary simulations

Abstract

The genotype-phenotype map is an essential object in our understanding of organismal complexity and adaptive properties, determining at once genomic plasticity and those constraints that may limit the ability of genomes to attain evolutionary innovations. An exhaustive experimental characterization of the relationship between genotypes and phenotypes is at present out of reach. Therefore, several models mimicking that map have been proposed and investigated, leading to the identification of a number of general features: genotypes differ in their robustness to mutations, phenotypes are represented by a broadly varying number of genotypes, and simple point mutations seem to suffice to navigate the space of genotypes while maintaining a phenotype. However, most current models address only one level of the map (sequences and folded structures in RNA or proteins; networks of genes and their dynamical attractors; sets of chemical reactions and their ability to undergo molecular catalysis), such that many relevant questions cannot be addressed. Here we introduce toyLIFE, a multi-level model for the genotype-phenotype map based on simple genomes and interaction rules from which a complex behavior at upper levels emerges, remarkably plastic gene regulatory networks and metabolism. toyLIFE is a tool that permits the investigation of how different levels are coupled, in particular how and where do mutations affect phenotype or how the presence of certain metabolites determines the dynamics of toyLIFE gene regulatory networks. The possibilities of this model are not exhausted by the results presented in this contribution. It can be easily generalized to incorporate evolution through mutations that change genome length or through recombination, to consider gene duplication or deletion, and therefore to explore further properties of extended genotype-phenotype maps.