Lorsque l'on évoque Darwin et la théorie de l'évolution, on ne pense pas aux mathématiques. Pourtant dès que l'on s'intéresse aux mécanismes de la sélection naturelle, au hasard de la reproduction et au rôle des mutations, il est indispensable de les utiliser.
Après une introduction historique aux idées de Darwin sur l'évolution des espèces, nous expliquons l'impact de sa théorie et de ses réflexions sur la communauté scientifique et l'influence qu'il a eue sur la modélisation mathématique des dynamiques de population ou de la génétique des populations. Nous développons quelques exemples d'objets mathématiques, tels les processus de branchement, qui permettent de prédire le futur d'une population (son extinction, sa diversité…) ou au contraire d'en connaître le passé biologique (l'ancêtre commun d'un groupe d'individus par exemple). L'introduction du hasard dans la modélisation des questions liées à la biodiversité et à l'évolution est fondamentale. Elle permet de prendre en compte les variabilités individuelles et de mieux comprendre l'impact des facteurs écologiques et génétiques sur l'évolution des espèces.
Ces idées seront illustrées par des exemples issus de travaux récents développés entre mathématiciens et biologistes.[-]

Horizontal transfer of information is recognized as a major process in the evolution and adaptation of population, especially micro-organisms. There is a large literature but the previous models are either based on epidemiological models or population genetics stochastic models with constant population size. We propose a general stochastic eco-evolutionary model of population dynamics with horizontal and vertical transfers, inspired by the transfer of plasmids in bacteria. The transfer rates are either density-dependent (DD) or frequency-dependent (FD) or of Michaelis-Menten form (MM). Our model allows eco-evolutionary feedbacks. In the first part we present a two-traits (alleles or kinds of plasmids, etc.) model with horizontal transfer without mutation and study a large population limit. It's a ODEs system. We show that the phase diagrams are different in the (DD), (FD) and (MM) cases. We interpret the results for the impact of horizontal transfer on the maintenance of polymorphism and the invasion or elimination of pathogens strains. We also propose a diffusive approximation of adaptation with transfer. In a second part, we study the impact of the horizontal transfer on the evolution. We explain why it can drastically affect the evolutionary outcomes. Joint work with S. Billiard,P. Collet, R. Ferrière, C.V. Tran.[-]

Understanding the adaptation and evolution of populations is a huge challenge, in particular for microorganisms since it plays a main role in the virulence evolution or in bacterial antibiotics resistances. We propose a general stochastic model of population dynamics with clonal reproduction and mutations. Moreover the individuals compete for resources and exchange genes. We show that the horizontal gene transfer can have a major impact on the distribution of the successive mutational fixations, leading to dramatically different behaviors, from expected evolution scenarios to evolutionary suicide, including cyclic behaviours. We present different approaches to capture mathematically these scenarii.[-]

We consider a stochastic model for the evolution of a discrete population structured by a trait taking finitely many values on a grid of [0, 1], with mutation and selection. We study of the dynamics of the population in logarithm size and time scales, under a large population assumption. In the first part of the talk, individual mutations are rare but the global mutation rate tends to infinity. Then negligible sub-populations may have a strong contribution to evolution. The traits can also be horizontally transferred, leading to a trade-off between natural evolution to higher birth rates and transfer which drives the population towards lower birth rates. We prove that the stochastic discrete exponent process converges to a piecewise affine continuous function, which can be described along successive phases determined by dominant traits. In the second part of the talk, the individual mutations are small but not rare, we don't have any transfer and we assume the grid mesh for the trait values becoming smaller and smaller. We establish that under our rescaling, the stochastic discrete exponent process converges to the viscosity solution of a Hamilton-Jacobi equation, filling the gap between individual-based evolutionary models and Hamilton-Jacobi equations.
Joint works with N. Champagnat and V.C. Tran, and S. Mirrahimi for the second part.[-]