Le processus ancestral est appliqué pour étudier la variabilité génétique et la mesure de déséquilibre de liaison de séquences d’ADN, et faire de l’inférence statistique sur les divers facteurs responsables de cette variabilité. En tenant compte, en premier lieu, des facteurs de dérive génétique, de mutation, et de recombinaison, les calculs exacts de la mesure de déséquilibre de liaison de deux loci sont retrouvés. De plus, une approximation du processus exact, SMC (sequentially Markov chain), est utilisée pour trouver la mesure d’association à deux loci, et une formule de covariance pour calculer cette mesure est corrigée. En intégrant le facteur de conversion dans le modèle de Moran, on trouve l’espérance des mesures de polymorphisme exprimées par les espérances des mesures de variation intra-locus et inter-locus. Celles-ci sont calculées à l’aide de temps espérés dans les états ancestraux. De plus, l’espérance du déséquilibre de liaison est trouvée et il est montré qu’elle diminue quand le taux de recombinaison augmente. En utilisant ces résultats théoriques, on présente une méthode pour estimer les paramètres de mutation, de recombinaison, et de conversion. / The ancestral process is applied to investigate the amount of DNA variation and the amount of linkage disequilibrium ; it is also applied to make statistical inference about the multiple factors responsible for this variation. Considering genetic drift, mutation, and recombination events, the exact solutions for linkage disequilibrium between two loci are obtained. Furthermore, the association measure between two loci is obtained by using an approximation of the exact process, SMC (sequentially Markov chain), and correcting a covariance formula. After introducing intrachromosomal gene conversion under the Moran model, the expected amounts of variation within and between two loci are obtained using expected times spent in the ancestral states. Furthermore, the expectation of linkage disequilibrium is obtained and it is shown to decrease as the recombination rate is increased. Using these theoretical results, a method for estimating the mutation, recombination and gene conversion parameters is presented. / Les diagrammes de transitions d'états ont été réalisés avec le logiciel Latex.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal.

Les diagrammes de transitions d'états ont été réalisés avec le logiciel Latex. / Le processus ancestral est appliqué pour étudier la variabilité génétique et la mesure de déséquilibre de liaison de séquences d’ADN, et faire de l’inférence statistique sur les divers facteurs responsables de cette variabilité. En tenant compte, en premier lieu, des facteurs de dérive génétique, de mutation, et de recombinaison, les calculs exacts de la mesure de déséquilibre de liaison de deux loci sont retrouvés. De plus, une approximation du processus exact, SMC (sequentially Markov chain), est utilisée pour trouver la mesure d’association à deux loci, et une formule de covariance pour calculer cette mesure est corrigée. En intégrant le facteur de conversion dans le modèle de Moran, on trouve l’espérance des mesures de polymorphisme exprimées par les espérances des mesures de variation intra-locus et inter-locus. Celles-ci sont calculées à l’aide de temps espérés dans les états ancestraux. De plus, l’espérance du déséquilibre de liaison est trouvée et il est montré qu’elle diminue quand le taux de recombinaison augmente. En utilisant ces résultats théoriques, on présente une méthode pour estimer les paramètres de mutation, de recombinaison, et de conversion. / The ancestral process is applied to investigate the amount of DNA variation and the amount of linkage disequilibrium ; it is also applied to make statistical inference about the multiple factors responsible for this variation. Considering genetic drift, mutation, and recombination events, the exact solutions for linkage disequilibrium between two loci are obtained. Furthermore, the association measure between two loci is obtained by using an approximation of the exact process, SMC (sequentially Markov chain), and correcting a covariance formula. After introducing intrachromosomal gene conversion under the Moran model, the expected amounts of variation within and between two loci are obtained using expected times spent in the ancestral states. Furthermore, the expectation of linkage disequilibrium is obtained and it is shown to decrease as the recombination rate is increased. Using these theoretical results, a method for estimating the mutation, recombination and gene conversion parameters is presented.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

Cancer as a disease causes about 8.8 million deaths worldwide per year, a number that will largely increase in the next decades. Although the cellular processes involved in tumor emergence are more and more understood, the implications of specific changes at the cellular scale on tumor emergence at the tissue scale remain elusive. Main reasons for this lack of understanding are that the cellular processes are often hardly observable especially in the early phase of tumor development and that the interplay between cellular and tissue scale is difficult to deduce. Cell-based mathematical models provide a valuable tool to investigate in which way observable phenomena on the tissue scale develop by cellular processes. The implications of these models can elucidate underlying mechanisms and generate quantitative predictions that can be experimentally validated. In this thesis, we infer the role of genetic and phenotypic cell changes on tumor development with the help of cell-based Markov chain models which are calibrated by tissue-scale data. In the first part, we utilize data on the diagnosed fractions of benign and malignant tumor subtypes to unravel the consequences of genetic cell changes on tumor development. We introduce extensions of Moran models to investigate two specific biological questions. First, we evaluate the tumor regression behavior of pilocytic astrocytoma which represents the most common brain tumor in children and young adults. We formulate a Moran model with two absorbing states representing different subtypes of this tumor, derive the absorption probabilities in these states and calculate the tumor regression probability within the model. This analysis allows to predict the chance for tumor regression in dependency of the remaining tumor size and implies a different clinical resection strategy for pilocytic astrocytoma compared to other brain tumors. Second, we shed light on the hardly observable early cellular dynamics of tumor development and its consequences on the emergence of different tumor subtypes on the tissue scale. For this purpose, we utilize spatial and non-spatial Moran models with two absorbing states which describe both benign and malignant tumor subtypes and estimate lower and upper bounds for the range of cellular competition in different tissues. Our results suggest the existence of small and tissue-specific tumor-originating niches in which the fate of tumor development is decided long before a tumor manifests. These findings might help to identify the tumor-originating cell types for different cancer types. From a theoretical point of view, the novel analytical results regarding the absorption behavior of our extended Moran models contribute to a better understanding of this model class and have several applications also beyond the scope of this thesis. The second part is devoted to the investigation of the role of phenotypic plasticity of cancer cells in tumor development. In order to understand how phenotypic heterogeneity in tumors arises we describe cell state changes by a Markov chain model. This model allows to quantify the cell state transitions leading to the observed heterogeneity from experimental tissue-scale data on the evolution of cell state proportions. In order to bridge the gap between mathematical modeling and the analysis of such data, we developed an R package called CellTrans which is freely available. This package automatizes the whole process of mathematical modeling and can be utilized to (i) infer the transition probabilities between different cell states, (ii) predict cell line compositions at a certain time, (iii) predict equilibrium cell state compositions and (iv) estimate the time needed to reach this equilibrium. We utilize publicly available data on the evolution of cell compositions to demonstrate the applicability of CellTrans. Moreover, we apply CellTrans to investigate the observed cellular phenotypic heterogeneity in glioblastoma. For this purpose, we use data on the evolution of glioblastoma cell line compositions to infer to which extent the heterogeneity in these tumors can be explained by hierarchical phenotypic transitions. We also demonstrate in which way our newly developed R package can be utilized to analyze the influence of different micro-environmental conditions on cell state proportions. Summarized, this thesis contributes to gain a better understanding of the consequences of both genetic and phenotypic cell changes on tumor development with the help of Markov chain models which are motivated by the specific underlying biological questions. Moreover, the analysis of the novel Moran models provides new theoretical results, in particular regarding the absorption behavior of the underlying stochastic processes.