User:Lunlosh/Aevol

Aevol is a simulation platform enabling the evolution of digital organisms in different conditions. It is used to study the mechanisms responsible for the structuration of the genome and the transcriptome. . Its development started in 2006 and is still ongoing.

Scientific context
All bacterial species share a common evolutionary history. However, depending on their lifestyle, their environment or on evolutionary conditions, bacteria can have very different genomic and transcriptomic structures. For instance, all endosymbionts present very compact genomes while "free" bacteria usually have much larger genomes, containing up to ten times as many genes. Aevol proposes a framework where the

Design principles
Aevol is a digital genetics model: populations of digital organisms are subjected to a process of selection and variation, which creates a Darwinian dynamics. It aims at imitating a biologicial genome structure (including coding and non-coding compartments) and a multiscale genotype-to-phenotype decoding. By modifying the characteristics of selection (e.g. population size, type of environment, environmental variations) or variation (e.g. mutation rates, chromosomal rearrangement rates, types of rearrangements, horizontal transfer), one can study experimentally the impact of these parameters on the structure of the evolved organisms. In particular, it allows for the study of structural variations of the genome (e.g. number of genes, synteny, proportion of coding sequences, genome size).

The simulation platform comes along with a set of tools for analysing phylogenies and measuring many characteristics of the organisms and populations along evolution.

An extension of the model (R-Aevol) integrates an explicit model of the regulation of gene expression, thus allowing for the study of the evolution of gene regulation networks.

Main results
Aevol is particularly adapted to the study of second-order evolutionary pressures. These pressures do not directly depend on the fitness of the individuals (i.e. on their capacity of producing many offspring) but rather on their capacity of producing many viable offspring, themselves able to produce likewise capable offspring. These indirect selective pressures are particularly related to robustness, variability and evolvability constraints.

Aevol allowed us to show that second-order selection imposes very strong constraints on the structure of the genome, of the regulation network and of the transcriptome. Besides, the R-Aevol model allowed us to show that gene regulation network can present topological structure (structure of the connection graph) that are significantly different from their functional structure (functionally interacting genes).