David P. Parsons

In silico experimental evolution: a tool to test evolutionary scenarios

Comparative genomics has revealed that some species have exceptional genomes, compared to their closest relatives. For instance, some species have undergone a strong reduction of their genome with a drastic reduction of their genic repertoire. Deciphering the causes of these atypical trajectories can be very difficult because of the many phenomena that are intertwined during their evolution (e.g. changes of population size, environment structure and dynamics, selection strength, mutation rates...). Here we propose a methodology based on synthetic experiments to test the individual effect of these phenomena on a population of simulated organisms. We developed an evolutionary model - aevol - in which evolutionary conditions can be changed one at a time to test their effects on genome size and organization (e.g. coding ratio). To illustrate the proposed approach, we used aevol to test the effects of a strong reduction in the selection strength on a population of (simulated) bacteria. Our results show that this reduction of selection strength leads to a genome reduction of ~35% with a slight loss of coding sequences (~15% of the genes are lost - mainly those for which the contribution to fitness is the lowest). More surprisingly, under a low selection strength, genomes undergo a strong reduction of the noncoding compartment (~55% of the noncoding sequences being lost). These results are consistent with what is observed in reduced Prochlorococcus strains (marine cyanobacteria) when compared to close relatives.

Scaling Laws in Bacterial Genomes: A Side-Effect of Selection of Mutational Robustness

In the past few years, numerous research projects have focused on identifying and understanding scaling properties in the gene content of prokaryote genomes and the intricacy of their regulation networks. Yet, and despite the increasing amount of data available, the origins of these scalings remain an open question. The RAevol model, a digital genetics model, provides us with an insight into the mechanisms involved in an evolutionary process. The results we present here show that (i) our model reproduces qualitatively these scaling laws and that (ii) these laws are not due to differences in lifestyles but to differences in the spontaneous rates of mutations and rearrangements. We argue that this is due to an indirect selective pressure for robustness that constrains the genome size.

Effects of public good properties on the evolution of cooperation

Cooperation is a still unsolved and ever-controversial topic in evolutionary biology. Why do organisms engage in activities with long-term communal benefits but short-term individual cost? A general answer remains elusive, suggesting many important factors must still be examined and better understood. Here we study cooperation based on the secretion of a public good molecule using Aevol, a digital platform inspired by microbial cooperation systems. Specifically, we focus on the environmental and physical properties of the public good itself, its mobility, durability, and cost. The intensity of cooperation that evolves in our digital populations, as measured by the amount of the public good molecule organisms secrete, strongly depends on the properties of such a molecule. Specifically, and somewhat counter intuitively, digital organisms evolve to secrete more when public good degrades or diffuses quickly. The evolution of secretion also depends on the interactions between the population structure and public good properties, not just their individual values. Environmental factors affecting population diversity have been extensively studied in the past, but here we show that physical aspects of the cooperation mechanism itself may be equally if not more important. Given the wide range of substrates and environments that support microbial cooperation in nature, our results highlight the need for careful consideration of public good properties when studying the evolution of cooperation in bacterial or computational models.

From digital genetics to knowledge discovery: Perspectives in genetic network understanding

In this paper, we propose an original computational approach to assist knowledge discovery in complex biological networks. First, we present an integrated model of the evolution of regulation networks that can be used to uncover organization principles of such networks. Then, we propose to use the results of our model as a benchmark for knowledge discovery algorithms. We describe a first experiment of such benchmarking by using gene knock-out data generated from the modeled organisms.