Periodic Reporting for period 2 - OptimHist (Optimization and historical contingency in living systems: a biophysical approach)
Période du rapport: 2021-09-01 au 2023-02-28
At the molecular scale, we are focusing on how functional optimization and evolutionary history, i.e. phylogeny, shape protein sequences. We are showing that correlations arising from phylogeny are a double-edged sword, often confounding signal from functional optimization, but sometimes providing useful complementary information. We are improving sequence-based predictions for protein-protein interactions by exploiting information both from phylogeny and from the required complementarity of interacting residues. We are showing how various statistical models, including some based on natural language processing, encode phylogeny and constraints, and how they can generate new protein sequences. We are proposing methods to disentangle correlations in protein sequences due to optimization from those due to phylogeny, and investigating the importance of functional sectors of collectively correlated amino acids as an organizing principle of proteins. These contributions are improving our understanding of the sequence-function relationship of proteins.
At the scale of populations, we are analyzing the impact of optimization and contingency on the evolution of microbial populations. Natural microbial populations are not homogeneous and well-mixed, but possess complex spatial structures. We are working on building a general model of structured populations. We are also focusing on microorganisms with a rugged fitness landscape presenting several optima. In these realistic cases, populations tend to remain trapped in local optima. However, some spatial structures may help these populations to explore their fitness landscapes more efficiently than others. We are studying these effects quantitatively, to better understand the impact of spatial structure on evolution. We are also studying applications to the evolution of antimicrobial resistance, which is a major public health concern.
At the scale of microbial populations, we introduced a model for structured populations on graphs that generalizes previous ones by making migration events independent of birth and death. We demonstrated that by tuning migration asymmetry, some graphs transition from amplifying to suppressing natural selection. Our results do not hinge on a modeling choice of microscopic dynamics or update rules. Instead, they depend on migration asymmetry, which can be experimentally tuned and measured. We also quantified the exploration of rugged fitness landscapes by finite populations, which is a starting point toward studying subdivided populations. We investigated how finite populations explore model and experimental fitness landscapes, both with stochastic simulations and with analytical calculations based on Markov chains. We found that the height of the first fitness peak reached by a population, which characterizes early adaptation, is affected by strong finite-size effects. Furthermore, we rationalized these results by considering the accessibility of fitness peaks.
At the scale of microbial populations, we are further generalizing our model for structured populations on graphs by going beyond the limit of rare migrations. With this more general model, we aim to bridge two different lines of research, one from traditional population genetics, and the other from applied mathematics and to reconcile their results. We are also expecting general results about the impact of population structure on mutant fate, specifically mutant fixation probability and fixation time. Furthermore, we are building on our study of the impact of finite-size effects on the exploration of rugged fitness landscapes by microbial populations to now address the impact of spatial structure on the exploration of rugged fitness landscapes by microbial populations. This line combines our models of spatially structured populations together with our study of rugged fitness landscapes. Finally, we are starting to study the impact of population spatial structure on the evolution of antimicrobial resistance.