A Model for Genome Size Evolution

International audienceWe present a model for genome size evolution that takes into account both local mutations such as small insertions and small deletions, and large chromosomal rearrangements such as duplications and large deletions. We introduce the possibility of undergoing several mutations within one generation. The model, albeit minimalist, reveals a non-trivial spontaneous dynamics of genome size: in the absence of selection, an arbitrary large part of genomes remains beneath a finite size, even for a duplication rate 2.6-fold higher than the rate of large deletions, and even if there is also a systematic bias toward small insertions compared to small deletions. Specifically, we show that the condition of existence of an asymptotic stationary distribution for genome size non-trivially depends on the rates and mean sizes of the different mutation types. We also give upper bounds for the median and other quantiles of the genome size distribution, and argue that these bounds cannot be overcome by selection. Taken together, our results show that the spontaneous dynamics of genome size naturally prevents it from growing infinitely, even in cases where intuition would suggest an infinite growth. Using quantitative numerical examples, we show that, in practice, a shrinkage bias appears very quickly in genomes undergoing mutation accumulation, even though DNA gains and losses appear to be perfectly symmetrical at first sight. We discuss this spontaneous dynamics in the light of the other evolutionary forces 123 2250 S. Fischer et al. proposed in the literature and argue that it provides them a stability-related size limit below which they can act

EvoEvo Deliverable 2.1: Specifications of the genome-network model

Specifications of the genome-network model: Description of the modeling choices for the genome-network integrated model. This model should include a realistic genomic structure as well as a metabolic network translated from the genome

Aevol-4b: Toward a new simulation platform to benchmark phylogenetic tools

National audienceAevol (www.aevol.fr) is a computational platform that allows for the study and manipulation of populations of digital organisms evolving under different conditions. Using Aevol simulations, one 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. Recently, we used aevol as a benchmarking tool. Indeed, Molecular evolutionary methods and tools are difficult to validate, as we have almost no direct access to ancient molecules. Inference methods may be tested with simulated data but this requires that the inference methods and the simulation be design independently (Biller et al., Computation in Europe 2016; Biller et al., Jobim 2016). Using aevol we can simulate perfectly characterized phylogenies and obtain a final population that evolved accordingly. Then we can use this final population to try to recover the initial phylogeny using various tools and assess their efficiency in doing so. This approach has recently been applied to test various estimators of inversion distance, revealing their limits and suggesting important improvement directions (Biller et al., Genome Biology and Evolution 2016). However, current aevol structure – more specifically the use of a binary representation for the genomic sequence – strongly limits its usability as a benchmarking tool. That is why we recently started the development of a new version of the software in which the genome sequence will use a four-nucleotides code and the translation from genetic sequence to polypeptide sequences will use the extant genetic code to map the 4-bases alphabet to the 20-amino-acids one. Although the development of this new version is in its infancy a first prototype has been developed and we would like to discuss the main modelling choices with the Alphy community that will be the potential users of the generated benchmarks. In particular, in this prototype the genotype-to-phenotype map is be based a mathematical description of traits under selection and on A.D. Solis (Proteins, 2015) classification of amino-acids, two crucial modelling choices that deserve discussion before we start final software implementation

Breaking Good: Accounting for Fragility of Genomic Regions in Rearrangement Distance Estimation

International audienceModels of evolution by genome rearrangements are prone to two types of flaws: One is to ignore the diversity of susceptibility tobreakage across genomic regions, and the other is to suppose that susceptibility values are given. Without necessarily supposing theirprecise localization,we call “solid” the regions that are improbably broken by rearrangements and “fragile” the regions outside solidones.We propose a model of evolution by inversions where breakage probabilities vary across fragile regions and over time. It containsas a particular case the uniform breakage model on the nucleotidic sequence,where breakage probabilities are proportional to fragileregion lengths. This is very different from the frequently used pseudo uniform model where all fragile regions have the same probabilityto break. Estimations of rearrangement distances based on the pseudo uniform model completely fail on simulations with thetruly uniform model. On pairs of amniote genomes, we show that identifying coding genes with solid regions yields incoherentdistance estimations, especially with the pseudo uniform model, and to a lesser extent with the truly uniform model. This incoherenceis solved when we coestimate the number of fragile regions with the rearrangement distance. The estimated number of fragileregions is surprisingly small, suggesting that a minority of regions are recurrently used by rearrangements. Estimations for several pairsof genomes at different divergence times are in agreement with a slowly evolvable colocalization of active genomic regions in the cell

Uptake of fatty acids by the enterocyte: New insights gained from mathematical modeling

Oral communicationInternational audienc

EvoEvo Deliverable 2.3: Specifications of the population model

Specifications of the population model: Description of the modelling choices for the population model. This model should include a realistic population structure enabling niche construction, inter-individual interactions and open-endedness

In silico experimental evolution: a tool to test evolutionary scenarios

International audienceComparative 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

L'évolution expérimentale in silico

National audienceIn silico experimental evolution is a method aimed at studying the mechanisms of evolution and the constraints these mechanisms may impose on cellular organization. Researchers create virtual organisms and let them compete for reproduction and mutate inside the computer. To do so, one must choose a formalism to represent the genome of an organism, define an artificial biochemistry to compute the organism's form or behavior from its genome, set a task that the organisms must fulfill to survive and reproduce in their abstract environment, and finally define the mutation mechanisms that will happen when an organism will replicate its genome. Although this technique is algorithmically close to the genetic algorithms used in evolutionary computation, it is actually closer to the wet evolutionary experiments carried out by biologists on microorganisms, both in terms of scientific objectives and in terms of experimental design. In silico experimental evolution is used to stimulate the intuition, to generate plausible hypotheses on the mechanisms and effects of evolution. The strengths of this simulation approach are the easy way the parameters can be controlled, the possibility to run many repetitions, and the perfect fossil record. However, because simplifications are inevitably made when designing the virtual organisms and their biochemistry, the mechanisms discovered in the simulations are merely hypotheses as far as real organisms are concerned, and these hypotheses must be tested by wet evolutionary experiments. After having positioned in silico experimental evolution with respect to related approaches, this chapter describes the main families of formalisms used in the field. Finally, to illustrate more concretely the methods currently used, an example is given based on the aevol model, the way it is used and the scientific results it allowed for.L'évolution expérimentale in silico est une méthode d'étude des mécanismes de l'évolution et des contraintes que ces mécanismes imposent à l'organisation des cellules vivantes. Elle consiste à créer des organismes virtuels et à les laisser se reproduire et muter dans l'ordinateur. Il faut pour cela choisir une façon de représenter le génome d'un organisme, définir une biochimie artificielle permettant de déduire sa forme ou son comportement de son génome, fixer une tâche à réaliser pour survivre et se reproduire dans un environnement abstrait, et enfin déterminer les mécanismes de mutation susceptibles de se produire lorsqu'un individu réplique son génome. Cette technique, bien qu'algorithmiquement proche des algorithmes génétiques utilisés en optimisation combinatoire, est en réalité plus proche de l'évolution expérimentale réalisée en laboratoire sur des microorganismes, à la fois en termes d'objectifs scientifiques et dans la façon dont les plans d'expérience sont conçus. L'évolution expérimentale in silico sert à stimuler l'intuition, à générer des hypothèses plausibles sur les mécanismes de l'évolution, en exploitant les forces de cette approche de simulation, à savoir le contrôle des paramètres et la connaissance exhaustive des relations de parenté et des mutations qui se sont produites dans les différentes lignées. Du fait des nécessaires simplifications et choix ad hoc effectués lors de la conception des organismes virtuels, les mécanismes découverts dans les simulations n'ont vis-à-vis des organismes réels que le statut d'hypothèses, qui devront ensuite être testées à la paillasse. Après avoir positionné l'évolution expérimentale in silico par rapport aux approches voisines, ce chapitre décrit les principales familles de formalismes utilisées dans le domaine. Enfin, pour illustrer plus concrètement les méthodes employées, il décrit un exemple, le modèle aevol, la façon dont il est utilisé et les résultats scientifiques qu'il a permis d'obtenir

Souvenirs from ECAL 2017: create, play, experiment, discover – revealing the experimental power of virtual worlds

International audienceThis report presents some highlights from ECAL 2017, the Fourteenth European Conference on Artificial Life, which was held on 4–8 September 2017 in Lyon, France