Abstract: Variation and Selection are the two core processes of Darwinian Evolution. Yet, both are directly regulated by many processes that are themselves products of evolution (e.g. DNA repair, mutator genes, transposable elements, horizontal transfer, stochasticity of gene expression, sex, network modularity, niche construction…). This results in the ability of evolution to self-modify its operators, hence its dynamics. We call this process “Evolution of Evolution” orEvoEvo. Different EvoEvo strategies have been proposed in the literature, including regulation of variability, robustness/evolvability strategies and bet-hedging, but finding traces of these strategies in extant organisms is difficult. Moreover all these strategies are likely to interact one with the others, blurring their respective outcomes. However, new tools are now available that help understanding EvoEvo. On the one hand, large scale bioinformatic data analysis can be used to recognize signatures of evolution of evolution. On the other hand, large scale computational modelling of multi-level evolution is now becoming feasible, and promises to shed light on the conditions under which evolutionary mechanisms evolve as well as their consequences.

Abstract: The fitness landscape defines the relationship between genotypes and fitness in a given environment and underlies fundamental quantities such as the distribution of selection coefficient and the magnitude and type of epistasis. A better understanding of variation in landscape structure across species and environments is thus necessary to understand and predict how populations will adapt. An increasing number of experiments investigate the properties of fitness landscapes by identifying mutations, constructing genotypes with combinations of these mutations, and measuring the fitness of these genotypes. Yet these empirical landscapes represent a very small sample of the vast space of all possible genotypes, and this sample is often biased by the protocol used to identify mutations. Here we develop a rigorous statistical framework based on Approximate Bayesian Computation to address these concerns and use this flexible framework to fit a broad class of phenotypic fitness models (including Fisher’s model) to 26 empirical landscapes representing nine diverse biological systems. Despite uncertainty owing to the small size of most published empirical landscapes, the inferred landscapes have similar structure in similar biological systems. Surprisingly, goodness-of-fit tests reveal that this class of phenotypic models, which has been successful so far in interpreting experimental data, is a plausible in only three of nine biological systems. More precisely, although Fisher’s model was able to explain several statistical properties of the landscapes—including the mean and SD of selection and epistasis coefficients—it was often unable to explain the full structure of fitness landscapes.

Abstract: The heredity of the modern cell is provided by a small number of non-catalytic template molecules—the gene. This basic feature of modern-type heredity, however, is believed to have been absent at the earliest stages of evolution. The RNA world hypothesis posits that the heredity of the first, primitive cell (protocell, for short) was provided by a population of dual-functional molecules serving as both templates and catalysts. How could genes originate in protocells? Here, I will discuss the possibility that gene-like molecules emerge in protocells through spontaneous symmetry breaking between the complementary strands of replicating molecules. Our model assumes a population of primitive cells, each containing a population of replicating molecules. Protocells are selected towards maximizing the catalytic activity of internal molecules, whereas molecules tend to evolve towards minimizing it. This conflicting evolutionary tendencies at different levels induce symmetry breaking, whereby one strand of replicating molecules maintains catalytic activity and increases its copy number, whereas the other completely loses catalytic activity and decreases its copy number—like genes. The evolution of these gene-like molecules increases the equilibrium fitness of protocells. Our results implicate conflicting multilevel evolution as a key cause of the evolution of genetic complexity.

Abstract: http://www.evoevo.eu/download/evoevo_workshop_2016/EvoEvo2016_Beslon.pdf

Abstract: http://www.evoevo.eu/download/evoevo_workshop_2016/EvoEvo2016_Colizzi.pdf

Abstract: http://www.evoevo.eu/download/evoevo_workshop_2016/EvoEvo2016_Rutten.pdf

Abstract: http://www.evoevo.eu/download/evoevo_workshop_2016/EvoEvo2016_stepney.pdf