<p>Evolution by natural selection can occur at multiple biological scales. This is particularly the case for host-pathogen systems, where selection occurs both within each infected host as well as through transmission between hosts. Despite there being established mathematical models for understanding evolution at a single biological scale, fewer tractable models exist for multiscale evolutionary dynamics. Here I present mathematical approaches using tools from probability and stochastic processes as well as dynamical systems to handle multiscale evolutionary systems. The first problem I address concerns the antigenic evolution of influenza. Using a combination of ordinary differential equations and inhomogeneous Poisson processes, I study how immune selection pressures at the within-host level impact population-level evolutionary dynamics. The second problem involves the more general question of evolutionary dynamics when selection occurs antagonistically at two biological scales. In addition to host-pathogen systems, such situations arise naturally in the evolution of traits such as the production of a public good and the use of a common resource. I introduce a model for this general phenomenon that is intuitively visualized as a a stochastic ball-and-urn system and can be used to systematically obtain general properties of antagonistic multiscale evolution. Lastly, this ball-and-urn framework is in itself an interesting mathematical object which can studied as either a measure-valued process or an interacting particle system. In this mathematical context, I show that under different scalings, the measure-valued process can have either a propagation of chaos or Fleming-Viot limit.</p> / Dissertation

O processo de Fleming-Viot é um processo de Markov cujo espaço de estado é um conjunto de medidas de propabilidade. As funções-amostras do processo representam as prováveis possibilidades de transformação das freqüencias de tipos genéticos presentes numa população ao longo do tempo. Obtido como solução de um problema de martingala bem posto para um operador linear construído de forma a modelar diversas características importantes no estudo da genética populacional, como mutação, seleção, deriva genética, entre outras, o processo de Fleming-Viot permite, por meio de uma abordagem matemática unificadora, tratar problemas de complexidade variada. No presente trabalho, estudamos s processs de Fleming-Viot com saltos, introduzidos por Hiraba. Interpretamos biologicamente esse saltos como mudanças abruptas que podem ocorrer, num curto espaço de tempo, durante a evolução de uma população de indivíduos, causadas por epidemias, desastres naturais ou outras catástrofes, e que levam a descontinuidades nas frequências dos tipos gênicos. Apresentamos uma forma de incluir um fator de seleção no processo com saltos, através da aplicação de uma transformação de medida do tipo Girsanov. Em seguida, fazemos uma análise do comportamento assintótico do processo utilizando técnicas de dualidade e acoplamento.

Le cliquet de Muller est un modèle mathématiques illustrant l'accumulation de mutations délétères dans une population asexuée. L'idée principale est que l'absence de recombinaison oblige les enfants à avoir au moins autant de mutations nocives que leurs parents, et au bout d'un certain temps, le nombre minimum de mutations délétères de la population, qui est donc un processus croissant, augmente : on dit alors que le cliquet clique. Le modèle du cliquet de Muller qui est étudié dans cette thèse est un système infini d'équations différentielles stochastiques de Fleming-Viot couplées. On montre dans une première partie d'abord que le cliquet s'actionne en temps fini p.s., puis que l'espérance du temps mis pour cliquer est également finie. On utilise pour cela des comparaisons d'équations stochastiques et des changements de temps. Dans une deuxième partie, on démontre que ce modèle est équivalent à un modèle du look-down modifié auquel on a ajouté des mutations et des morts. Puis dans la troisième partie on généralise le résultat de la deuxième à un cadre plus large de systèmes d'équations différentielles stochastiques. / Muller's Ratchet is a model from evolutionary theory describing the accumulation of deleterious mutations in asexually reproducing population. The lack of recombination implies that children have all the deleterious mutations of his parent. The minimal number of deleterious mutations carried in the population is an non-decreasing process, and if it increases we say that the Muller's ratchet clicks. The model studied in this thesis is an infinite system of stochastic differential equations. In the first chapter, we first prove that the ratchet clicks in finite time a.s., then that the clicking time has finite expectation. For this we use comparison arguments and time changes. In the second chapter, we prove that this model is equivalent to a modified look-down model with mutation and selection. In the third chapter we generalize the results of chapter 2 to a more general model.

Dans cette thèse nous étudions le comportement asymptotique de systèmes de particules en interaction de type champ moyen en espace discret, systèmes pour lesquels l'interaction a lieu par l'intermédiaire de la mesure empirique. Dans la première partie de ce mémoire, nous nous intéressons aux systèmes de particules de type Fleming-Viot: les particules se déplacent indépendamment suivant une dynamique markovienne jusqu'au moment où l'une d'entre elles touche un état absorbant. A cet instant, la particule absorbée choisit uniformément une autre particule et saute sur sa position. L'ergodicité du processus est établie dans le cadre de marches aléatoires sur N avec dérive vers l'origine et pour une dynamique proche de celle du graphe complet. Pour ce dernier, nous obtenons une estimation quantitative de la convergence en temps long à l'aide de la courbure de Wasserstein. Nous montrons de plus la convergence de la distribution empirique stationnaire vers une unique distribution quasi-stationnaire, quand le nombre de particules tend vers l'infini. Dans la deuxième partie de ce mémoire, nous nous intéressons au comportement en temps long et quand le nombre de particules devient grand, d'un système de processus de naissance et mort pour lequel les particules interagissent à chaque instant par le biais de la moyenne de leurs positions. Nous établissons l'existence d'une limite macroscopique, solution d'une équation non linéaire ainsi que le phénomène de propagation du chaos avec une estimation quantitative et uniforme en temps / In this thesis we study the asymptotic behavior of particle systems in mean field type interaction in discrete space, where the system acts over one fixed particle through the empirical measure of the system. In the first part of this thesis, we are interested in Fleming-Viot particle systems: the particles move independently of each other until one of them reaches an absorbing state. At this time, the absorbed particle jumps instantly to the position of one of the other particles, chosen uniformly at random. The ergodicity of the process is established in the case of random walks on N with a dirft towards the origin and on complete graph dynamics. For the latter, we obtain a quantitative estimate of the convergence described by the Wasserstein curvature. Moreover, under the invariant measure, we show the convergence of the empirical measure towards the unique quasi-stationary distribution as the size of the system tends to infinity. In the second part of this thesis, we study the behavior in large time and when the number of particles is large of a system of birth and death processes where at each time a particle interacts with the others through the mean of theirs positions. We establish the existence of a macroscopic limit, solution of a non linear equation and the propagation of chaos phenomenon with quantitative and uniform in time estimate

Made available in DSpace on 2015-03-04T18:50:48Z (GMT). No. of bitstreams: 1 Apresentacao.pdf: 199708 bytes, checksum: ce3c2b5e9db17dddd7a2684d9e5cac8b (MD5) Previous issue date: 2006-07-14 / O processo de Fleming-Viot é um processo de Markov cujo espaço de estado é um conjunto de medidas de propabilidade. As funções-amostras do processo representam as prováveis possibilidades de transformação das freqüencias de tipos genéticos presentes numa população ao longo do tempo. Obtido como solução de um problema de martingala bem posto para um operador linear construído de forma a modelar diversas características importantes no estudo da genética populacional, como mutação, seleção, deriva genética, entre outras, o processo de Fleming-Viot permite, por meio de uma abordagem matemática unificadora, tratar problemas de complexidade variada. No presente trabalho, estudamos s processs de Fleming-Viot com saltos, introduzidos por Hiraba. Interpretamos biologicamente esse saltos como mudanças abruptas que podem ocorrer, num curto espaço de tempo, durante a evolução de uma população de indivíduos, causadas por epidemias, desastres naturais ou outras catástrofes, e que levam a descontinuidades nas frequências dos tipos gênicos. Apresentamos uma forma de incluir um fator de seleção no processo com saltos, através da aplicação de uma transformação de medida do tipo Girsanov. Em seguida, fazemos uma análise do comportamento assintótico do processo utilizando técnicas de dualidade e acoplamento.

This thesis focus on the effect that selection has on the ancestry of a spatially structured population. In the absence of selection, the ancestry of a sample from the population behaves as a system of random walks that coalesce upon meeting. Backwards in time, each ancestral lineage jumps, at the time of its birth, to the location of its parent, and whenever two ancestral lineages have the same parent they jump to the same location and coalesce. Introducing selective forces to the evolution of a population translates into branching when we follow ancestral lineages, a by-product of biased sampling forwards in time. We study populations that evolve according to the Spatial Lambda-Fleming-Viot process with selection. In order to assess whether the picture under selection differs from the neutral case we must consider the timescale dictated by the neutral mutation rate Theta. Thus we look at the rescaled dual process with n=1/Theta. Our goal is to find a non-trivial rescaling limit for the system of branching and coalescing random walks that describe the ancestral process of a population. We show that the strength of selection (relative to the mutation rate) required to do so depends on the dimension; in one and two dimensions selection needs to be stronger in order to leave a detectable trace in the population. The main results in this thesis can be summarised as follows. In dimensions three and higher we take the selection coefficient to be proportional to 1/n, in dimension two we take it to be proportional to log(n)/n and finally, in dimension one we take the selection coefficient to be proportional to 1/sqrt(n). We then proceed to prove that in two and higher dimensions the ancestral process of a sample of the population converges to branching Brownian motion. In one dimension, provided we do not allow ancestral lineages to jump over each other, the ancestral process converges to a subset of the Brownian net. We also provide numerical results that show that the non-crossing restriction in one dimension cannot be lifted without a qualitative change in the behaviour of the process. Finally, through simulations, we study the rate of convergence in the two-dimensional case.

The subject of this thesis is the study of certain stochastic models arising in Population Genetics. The study of biological evolution naturally motivates the construction and use of sometimes sophisticated mathematical models. We contribute to the study of the so-called Lambda models. Our work is divided into two parts. In Part I, we study non-spatial models, introduced in 1999. Although there is a very rich literature concerning the description of genetic diversity thanks to the genealogies arising in these models, we obtain new results by considering the dynamics of the full population. We also contribute by presenting the first Bayesian method that allows us to reconstruct the genealogies generated by these models from data. In Part II, we study a recent extension of these models to the spatial setting. In particular, we prove a non trivial result concerning the geographical dispersal of a new mutant under this model.