EvoEvo Deliverable 6.1 : Project Website

Project website: Public website of the project. Private website for collaborative work

Adapting the engine to the fuel: mutator populations can reduce the mutational load by reorganizing their genome structure

International audienc

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

Subspace Clustering for all Seasons

International audienceSubspace clustering is recognized as a more general and difficult task than standard clustering since it requires to identify not only objects sharing similar feature values but also the various subspaces where these similarities appear. Many approaches have been investigated for subspace clustering in the literature using various clustering paradigms. In this paper, we present Chameleoclust, an evolutionary subspace clustering algorithm that incorporates a genome having an evolvable structure. The genome is a coarse grained genome defined as a list of tuples (the "genes"),each tuple containing numbers. These tuples are mapped at the phenotype level to denote core point locations in different dimensions, which are then used to collectively build the subspace clusters, by grouping the data around the core points. The algorithm has been assessed using a reference framework for subspace clustering evaluation, and compared to state-of-the-art algorithms on both real and synthetic datasets. The results obtained with the Chameleoclust algorithm show that evolution of evolution, through an evolvable genome structure, is usefull to solve a difficult problem such as subspace clustering

Evolutionary escape from local fitness peaks through inversion mutations

CCS 2021 - Conference on Complex SystemsInternational audienc

Jump then Climb: can rearrangements predict the occurrence of mutational bursts?

International audienc

A Genome-Wide Evolutionary Simulation of the Transcription-Supercoiling Coupling

International audienceDNA supercoiling, the level of under-or overwinding of the DNA polymer around itself, is widely recognized as an ancestral regulation mechanism of gene expression in bacteria. Higher levels of negative supercoiling facilitate the opening of the DNA double helix at gene promoters, and thereby increase gene transcription rates. Different levels of supercoiling have been measured in bacteria exposed to different environments, leading to the hypothesis that variations in supercoiling could be a response to changes in the environment. Moreover, DNA transcription has been shown to generate local variations in the supercoiling level, and therefore to impact the transcription rate of neighboring genes. In this work, we study the coupled dynamics of DNA supercoiling and transcription at the genome scale. We implement a genome-wide model of gene expression based on the transcriptionsupercoiling coupling. We show that, in this model, a simple change in global DNA supercoiling is sufficient to trigger differentiated responses in gene expression levels via the transcription-1 supercoiling coupling. Then, studying our model in the light of evolution, we demonstrate that this non-linear response to different environments, mediated by the transcription-supercoiling coupling, can serve as the basis for the evolution of specialized phenotypes

Digital evolution: Insights for biologists

International audienc

Modeling the emergence of multi-protein dynamic structures by principles of self-organization through the use of 3DSpi, a multi-agent-based software

BACKGROUND: There is an increasing need for computer-generated models that can be used for explaining the emergence and predicting the behavior of multi-protein dynamic structures in cells. Multi-agent systems (MAS) have been proposed as good candidates to achieve this goal. RESULTS: We have created 3DSpi, a multi-agent based software that we used to explore the generation of multi-protein dynamic structures. Being based on a very restricted set of parameters, it is perfectly suited for exploring the minimal set of rules needed to generate large multi-protein structures. It can therefore be used to test the hypothesis that such structures are formed and maintained by principles of self-organization. We observed that multi-protein structures emerge and that the system behavior is very robust, in terms of the number and size of the structures generated. Furthermore, the generated structures very closely mimic spatial organization of real life multi-protein structures. CONCLUSION: The behavior of 3DSpi confirms the considerable potential of MAS for modeling subcellular structures. It demonstrates that robust multi-protein structures can emerge using a restricted set of parameters and allows the exploration of the dynamics of such structures. A number of easy-to-implement modifications should make 3DSpi the virtual simulator of choice for scientists wishing to explore how topology interacts with time, to regulate the function of interacting proteins in living cells

Testing evolution predictability using the aevol software

International audienceMotivated by RNA virus’ genome biology, we used the aevol software to simulate the evolution of compacted genomes under high mutation rates. 30 independent digital wild-type (WT) genomes were generated after 200,000 generations of evolution under similar conditions. Then, each of these WTs was cloned 30 times and we let evolution to continue for 30,000 extra generations. By comparing these clones, we aimed to reveal the extent of evolutionary predictability for such compacted genomes. Results show that: (i) WTs are not equivalent in terms of evolutionary potential: some WTs are more prone than the others to increase their fitness during the last 30,000 generations. (ii) Evolution frequently occurs in bursts which implies that the probability to fix a mutation is increased after fixation of another mutation. Moreover these bursts are often initiated by chromosomal rearrangements (mainly duplications) because these rearrangements open new evolutionary pathways in the fitness landscape. Indeed, we quantified the "evolvability potential" of every clone after each mutation and found that the bursts are triggered by a strong increase of evolvability that quickly leads to point substitutions and indels fixation