Aevol
Evolution is difficult to study in nature, or even in the lab, especially while it is still ongoing. The many possible environmental conditions lead to a great diversity of evolutionary outcomes in terms of content and structure of both phenotype and genotype.
For example, endosymbiotic bacteria have genomes with up to 10 times fewer genes than their free-living counterparts. Bacterial social genes, such as some antibiotic resistance genes, preferentially reside on mobile genetic elements, plasmids, rather than on the chromosome.
One powerful way to investigate and quantify such complex evolutionary processes is through digital modeling approaches.
Aevol is an open-source computational platform designed to study populations of digital organisms evolving under different controlled conditions. It combines genetic algorithms with individual-based modeling to simulate reproduction, competition and mutations, across hundreds of thousands of generations. Individuals interact both on ecological and evolutionary timescales, allowing us to investigate scenarios typically experimentally unattainable, as we can directly control and vary the characteristics of selection (e.g. population size, type of environment, environmental variations) or variation (e.g. types and rates of mutations and rearrangements).
Inspired by bacterial genomics and experimental microbial evolution research, Aevol models gnomes with varying number of genes, genetic architecture, and coding/non-coding sequence proportion. The platform also includes tools for phylogenetic reconstruction, as well as visualization, and quantitative analysis of population and organismal properties throughout evolutionary time.
Using Aevol simulations, one therefore can better understand evolutionary forces and mechanisms leading to specific genome and transcriptome structures, as well as indirect selection pressures involved in the evolution of cooperation and genetic information transfer.
An extension of the model (R-Aevol), incorporates an explicit model of the regulation of gene expression, enabling the study of the evolution of gene regulatory networks.