Un des défis fondamentaux de la biologie moderne est une meilleure compréhension des mécanismes de régulation de l'expression des gènes, dont dépendent notamment le fonctionnement et la différentiation des cellules. En outre, leurs dérèglements peuvent être à l'origine de pathologies comme par exemple les cancers. Les technologies haut-débit de l'ère post-génomique permettent la production massive de données concernant notamment l'expression des gènes, les sites de fixation des facteurs de transcription et l'état de la chromatine. Ces données sont une mine d'informations pour l'étude des mécanismes de régulation. Cependant, la quantité et l'hétérogénéité de ces données soulèvent de nombreuses problématiques bioinformatiques liées à l'accès, la visualisation, l'analyse et l'intégration de celles-ci.Cette thèse aborde un certain nombre de ces aspects, à travers plusieurs projets :- la caractérisation bioinformatique de transcrits anti-sens produits par des promoteurs bidirectionnels durant le développement thymocytaire- le développement et l'intégration d'un compendium d'interactions géniques de natures diverses (interactions physiques, régulations, etc), ainsi qu'un outil de visualisation de graphes adapté - l'étude d'un système de transdifférentiation de lymphocytes pre-B en macrophages par induction de CEBPa, et la construction d'un modèle de régulation, grâce à l'analyse intégrée de données de puces à ADN, de ChIP-seq et de séquence / One of the fundamental challenges of modern biology is to better understand the mechanisms regulating gene expression, on which the functioning and differentiation of cells depend. In particular, disorders in these mechanisms may be the cause of diseases such as cancer. High throughput technologies of the post-genomic era allow mass production of data including gene expression, binding sites of transcription factors and chromatin state. These data a wealth of information for the study of regulatory mechanisms. However, the amount and heterogeneity of these data raise many bioinformatics issues related to access, visualization, analysis and integration of these.This thesis addresses a number of these aspects, through several projects:- bioinformatics characterization of antisense transcripts produced by bidirectional promoters during thymocyte development,- development and integration of a compendium of gene interactions of various kinds (physical interactions, regulations, etc.), and a graph visualization tool,- the study of a transdifferentiation system of pre-B lymphocytes into macrophages by induction of CEBPa, and the construction of a regulation model, thanks to the integrated analysis of DNA microarrays, ChIP-seq and sequence data.This work provides an illustration of some of the bioinformatics issues related to the exploitation of these data and methodologies to efficiently extract biological information, particularly to answer questions regarding the mechanisms of transcription and its regulation in the hematopoietic system.

It is increasingly becoming clear in the post-genomic era that proteins in a cell do not work in isolation but rather work in the context of other proteins and cellular entities during their life time. This has lead to the notion that cellular components can be visualized as wiring diagrams composed of different molecules like proteins, DNA, RNA and metabolites. These systems-approaches for quantitatively and qualitatively studying the dynamic biological systems have provided us unprecedented insights at varying levels of detail into the cellular organization and the interplay between different processes. The work in this thesis attempts to use these systems or network-based approaches to understand the design principles governing different cellular processes and to elucidate the functional and evolutionary consequences of the observed principles. Chapter 1 is an introduction to the concepts of networks and graph theory summarizing the various properties which are frequently studied in biological networks along with an overview of different kinds of cellular networks that are amenable for graph-theoretical analysis, emphasizing in particular on transcriptional, post-transcriptional and functional networks. In Chapter 2, I address the questions, how and why are genes organized on a particular fashion on bacterial genomes and what are the constraints bacterial transcriptional regulatory networks impose on their genomic organization. I then extend this one step further to unravel the constraints imposed on the network of TF-TF interactions and relate it to the numerous phenotypes they can impart to growing bacterial populations. Chapter 3 presents an overview of our current understanding of eukaryotic gene regulation at different levels and then shows evidence for the existence of a higher-order organization of genes across and within chromosomes that is constrained by transcriptional regulation. The results emphasize that specific organization of genes across and within chromosomes that allowed for efficient control of transcription within the nuclear space has been selected during evolution. Chapter 4 first summarizes different computational approaches for inferring the function of uncharacterized genes and then discusses network-based approaches currently employed for predicting function. I then present an overview of a recent high-throughput study performed to provide a 'systems-wide' functional blueprint of the bacterial model, Escherichia coli K-12, with insights into the biological and evolutionary significance of previously uncharacterized proteins. In Chapter 5, I focus on post-transcriptional regulatory networks formed by RBPs. I discuss the sequence attributes and functional processes associated with RBPs, methods used for the construction of the networks formed by them and finally examine the structure and dynamics of these networks based on recent publicly available data. The results obtained here show that RBPs exhibit distinct gene expression dynamics compared to other class of proteins in a eukaryotic cell. Chapter 6 provides a summary of the important aspects of the findings presented in this thesis and their practical implications. Overall, this dissertation presents a framework which can be exploited for the investigation of interactions between different cellular entities to understand biological processes at different levels of resolution.

Cette thèse propose une méthode pour construire des réseaux de régulation causales réalistes, qui a une taux de faux positifs inférieur aux méthodes traditionnelles. Cette approche consiste à intégrer des informa- tions hétérogènes à partir de deux types de prédictions de réseau pour déterminer une explication causale du gène observé co-expression. Ce processus d'intégration se modélise comme un problème d'optimisation combinatoire, de complexité NP-difficile. Nous introduisons une approche heuristique pour déterminer une solution approchée en un temps d'exécution pratique. Notre évaluation montre que, pour l'espèce modèle E. coli, le réseau de régulation résultant de l'application de cette méthode a une précision supérieure à celle construite avec des outils traditionnels. La bactérie Acidithiobacillus ferrooxidans présente des défis particu- liers pour la détermination expérimentale de son réseau de régulation. En utilisant les outils que nous avons développés, nous proposons un réseau de régulation putatif et analysons la pertinence de ces régulateurs centraux. Il s'agit de la quatrième contribution de cette thèse. Dans une deuxième partie de cette thèse, nous explorons la façon dont ces relations réglementaires se manifestent, en développant une méthode pour compléter un réseau de signalisation lié à la maladie d'Alzheimer. Enfin, nous abordons le problème ma- thématique de la conception de la sonde de puces à ADN. Nous concluons que, pour prévoir pleinement les dynamiques d'hybridation, nous avons besoin d' une fonction de l'énergie modifiée pour les structures secondaires des molécules d'ADN attaché surface et proposons un schéma pour la détermination de cette fonction.

O ciclo de divisão celular compreende uma seqüência de fenômenos controlados por una complexa rede de regulação gênica muito estável e robusta. Aplicamos as Redes Genéticas Probabilísticas (PGNs) para construir um modelo cuja dinâmica e robustez se assemelham às observadas no ciclo celular biológico. A estrutura de nosso modelo PGN foi inspirada em fatos biológicos bem estabelecidos tais como a existência de subsistemas integradores, realimentação negativa e positiva e caminhos de sinalização redundantes. Nosso modelo representa as interações entre genes como processos estocásticos e apresenta uma forte robustez na presença de ruido e variações moderadas dos parâmetros. Um modelo determinístico recentemente publicado do ciclo celular da levedura não resiste a condições de ruido que nosso modelo suporta bem. A adição de mecanismos de auto excitação, permite a nosso modelo apresentar uma atividade oscilatória similar à observada no ciclo celular embrionário. Nossa abordagem de modelar e simular o comportamento observado usando mecanismos de controle biológico conhecidos fornece hipóteses plausíveis de como a regulação subjacente pode ser realizada na célula. A pesquisa atualmente em curso procura identificar tais mecanismos de regulação no ciclo celular da levedura, usando dados de expressão gênica provenientes de medições seqüenciais de microarray. / The cell division cycle comprises a sequence of phenomena controlled by a stable and robust genetic network. We applied a Probabilistic Genetic Network (PGN) to construct an hypothetical model with dynamical behaviour and robustness typical of the biological cell-cycle. The structure of our PGN model was inspired in well established biological facts such as the existence of integrator subsystems, negative and positive feedback loops and redundant signaling pathways. Our model represents genes\' interactions as stochastic processes and presents strong robustness in the presence of moderate noise and parameters fluctuations. A recently published deterministic yeast cell-cycle model collapses upon noise conditions that our PGN model supports well. In addition, self stimulatory mechanisms can give our PGN model the possibility of having a pacemaker activity similar to the observed in the oscillatory embryonic cell cycle. Our approach of modeling and simulating the observed behavior by known biological control mechanisms provides plausible hypotheses of how the underlying regulation may be performed in the cell. The ongoing research is lead to identify such regulation mechanisms in the yeast cell-cycle from time-series microarray gene expression data.

O ciclo de divisão celular compreende uma seqüência de fenômenos controlados por una complexa rede de regulação gênica muito estável e robusta. Aplicamos as Redes Genéticas Probabilísticas (PGNs) para construir um modelo cuja dinâmica e robustez se assemelham às observadas no ciclo celular biológico. A estrutura de nosso modelo PGN foi inspirada em fatos biológicos bem estabelecidos tais como a existência de subsistemas integradores, realimentação negativa e positiva e caminhos de sinalização redundantes. Nosso modelo representa as interações entre genes como processos estocásticos e apresenta uma forte robustez na presença de ruido e variações moderadas dos parâmetros. Um modelo determinístico recentemente publicado do ciclo celular da levedura não resiste a condições de ruido que nosso modelo suporta bem. A adição de mecanismos de auto excitação, permite a nosso modelo apresentar uma atividade oscilatória similar à observada no ciclo celular embrionário. Nossa abordagem de modelar e simular o comportamento observado usando mecanismos de controle biológico conhecidos fornece hipóteses plausíveis de como a regulação subjacente pode ser realizada na célula. A pesquisa atualmente em curso procura identificar tais mecanismos de regulação no ciclo celular da levedura, usando dados de expressão gênica provenientes de medições seqüenciais de microarray. / The cell division cycle comprises a sequence of phenomena controlled by a stable and robust genetic network. We applied a Probabilistic Genetic Network (PGN) to construct an hypothetical model with dynamical behaviour and robustness typical of the biological cell-cycle. The structure of our PGN model was inspired in well established biological facts such as the existence of integrator subsystems, negative and positive feedback loops and redundant signaling pathways. Our model represents genes\' interactions as stochastic processes and presents strong robustness in the presence of moderate noise and parameters fluctuations. A recently published deterministic yeast cell-cycle model collapses upon noise conditions that our PGN model supports well. In addition, self stimulatory mechanisms can give our PGN model the possibility of having a pacemaker activity similar to the observed in the oscillatory embryonic cell cycle. Our approach of modeling and simulating the observed behavior by known biological control mechanisms provides plausible hypotheses of how the underlying regulation may be performed in the cell. The ongoing research is lead to identify such regulation mechanisms in the yeast cell-cycle from time-series microarray gene expression data.

No description available.