Genes encode proteins, some of which in turn regulate other genes. Such interactions make up gene regulatory relationships or (dynamic) gene regulatory networks. With advances in the measurement technology for gene expression and in genome sequencing, it has become possible to measure the expression level of thousands of genes simultaneously in a cell at a series of time points over a specific biological process. Such time-course gene expression data may provide a snapshot of most (if not all) of the interesting genes and may lead to a better understanding gene regulatory relationships and networks. However, inferring either gene regulatory relationships or networks puts a high demand on powerful computational methods that are capable of sufficiently mining the large quantities of time-course gene expression data, while reducing the complexity of the data to make them comprehensible. This dissertation presents several computational methods for inferring gene regulatory relationships and gene regulatory networks from time-course gene expression. These methods are the result of the authors doctoral study. Cluster analysis plays an important role for inferring gene regulatory relationships, for example, uncovering new regulons (sets of co-regulated genes) and their putative cis-regulatory elements. Two dynamic model-based clustering methods, namely the Markov chain model (MCM)-based clustering and the autoregressive model (ARM)-based clustering, are developed for time-course gene expression data. However, gene regulatory relationships based on cluster analysis are static and thus do not describe the dynamic evolution of gene expression over an observation period. The gene regulatory network is believed to be a time-varying system. Consequently, a state-space model for dynamic gene regulatory networks from time-course gene expression data is developed. To account for the complex time-delayed relationships in gene regulatory networks, the state space model is extended to be the one with time delays. Finally, a method based on genetic algorithms is developed to infer the time-delayed relationships in gene regulatory networks. Validations of all these developed methods are based on the experimental data available from well-cited public databases.

To understand how the components of a complex system like the biological cell interact and regulate each other, we need to collect data for how the components respond to system perturbations. Such data can then be used to solve the inverse problem of inferring a network that describes how the pieces influence each other. The work in this thesis deals with modelling the cell regulatory system, often represented as a network, with tools and concepts derived from systems biology. The first investigation focuses on network sparsity and algorithmic biases introduced by penalised network inference procedures. Many contemporary network inference methods rely on a sparsity parameter such as the L1 penalty term used in the LASSO. However, a poor choice of the sparsity parameter can give highly incorrect network estimates. In order to avoid such poor choices, we devised a method to optimise the sparsity parameter, which maximises the accuracy of the inferred network. We showed that it is effective on in silico data sets with a reasonable level of informativeness and demonstrated that accurate prediction of network sparsity is key to elucidate the correct network parameters. The second investigation focuses on how knowledge from association networks can be transferred to regulatory network inference procedures. It is common that the quality of expression data is inadequate for reliable gene regulatory network inference. Therefore, we constructed an algorithm to incorporate prior knowledge and demonstrated that it increases the accuracy of network inference when the quality of the data is low. The third investigation aimed to understand the influence of system and data properties on network inference accuracy. L1 regularisation methods commonly produce poor network estimates when the data used for inference is ill-conditioned, even when the signal to noise ratio is so high that all links in the network can be proven to exist for the given significance. In this study we elucidated some general principles for under what conditions we expect strongly degraded accuracy. Moreover, it allowed us to estimate expected accuracy from conditions of simulated data, which was used to predict the performance of inference algorithms on biological data. Finally, we built a software package GeneSPIDER for solving problems encountered during previous investigations. The software package supports highly controllable network and data generation as well as data analysis and exploration in the context of network inference. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 4: Manuscript.</p><p> </p>

In biology, regulatory networks are sets of macromolecules, mostly proteins and RNAs that interact to execute task. The main players in regulatory networks are DNAbinding proteins, also called transcription factors as they modulate the first step in gene expression. A gene regulatory network (GRN) is a set of genes or proteins that interact with each other to control a specific cell function. Gene regulatory networks are important in development, differentiation and to respond to environmental cues. Gene regulatory networks (GRNs) are the on-off switches of a cell operating at the gene and/or protein level. The modeling methods can be broadly categorized into continuous and discrete. In this work , we dedicate attention to discrete models on cell senescence models for Astrocyte [35], the modelling of drug synergies to control gastric cancer [38], and we also wrote a paper about Discrete and Continuous Model, advantage or disadvantage of these models and a list of available softwares for using these kind of approaches. / Em biologia, redes regulatórias são conjuntos de macromoléculas, principalmente proteínas e RNAs que interagem para executar uma tarefa. As proteínas de ligação de DNA, também chamadas de fatores de transcrição, são as principais executoras nas redes regulatórias, visto que modulam o primeiro passo na expressão gênica. Uma rede genética regulatória (RRG) é um conjunto de genes ou proteínas que interagem uns com os outros para controlar uma função celular específica. Redes regulatórias são importantes no desenvolvimento, diferenciação e para responder aos sinais ambientais. Elas são os botões de liga/desliga de uma célula operando no nível do gene e/ou proteína. Seus métodos de modelagem podem ser geralmente classificados em contínuos e discretos. Neste trabalho, dedicamos atenção aos modelos discretos em senescência celular para astrócitos [35], a modelagem de sinergias de drogas para controle do câncer gástrico [38] e também escrevemos um artigo sobre Modelos Discretos e Contínuos, vantagens e desvantagens desses modelos e listagem dos softwares disponíveis para uso nesse tipo de abordagem.

Afin de faire face à différents types de stress et s'adapter à de nouveaux environnements, les bactéries ont développé de nombreux mécanismes génétiquement régulés. La compétence pour la transformation naturelle est un processus qui favorise le transfert horizontal de gènes. Si les espèces phylogénétiquement éloignées partagent des mécanismes conservés d'intégration et de remaniement de l'ADN, les circuits de régulation de la compétence ne sont toutefois pas universels mais adaptés au mode de vie de chaque espèce. Chez les bactéries Gram-positives, les cascades de régulation de Streptococcus pneumoniae et Bacillus subtilis sont les mieux documentées. Si de nombreux modèles mathématiques ont été établis pour étudier différents aspects de la régulation des compétences chez B. subtilis, un seul modèle à échelle de population a été développé pour S. pneumoniae, il y a plus de dix ans, sur la base d'hypothèses contestées par de nouvelles données expérimentales. Nous avons développé, chez S. pneumoniae, un modèle fondé sur la connaissance de la régulation de la compétence qui intègre les éléments biologiques essentiels connus à ce jour. La cohérence structurelle de la topologie du réseau est confirmée par le formalisme des réseaux de Petri. Le réseau est ensuite transformé en un ensemble d'équations différentielles ordinaires pour étudier son comportement dynamique. La cinétique des protéines a été estimée en utilisant des données de luminescence et l'estimation des paramètres a été contrainte à partir des connaissances disponibles. Après avoir testé des modèles alternatifs, nous avons proposé l'existence d'un produit de gène tardif supplémentaire pouvant inhiber l'action de ComW, l'activateur du facteur sx. Nous apportons également un nouvel éclairage sur cette cascade de régulation en prédisant la cinétique de composantes du système qui pourraient être impliquées dans des comportements spécifiques. Ce modèle consolide les connaissances expérimentales acquises sur la régulation de la compétence chez S. pneumoniae. De plus, il peut être appliqué aux autres espèces de streptocoques appartenant aux groupes mitis et anginosus puisqu'ils partagent le même circuit régulateur. À l'échelle populationnelle, la transition vers l'état de compétence se produit d'abord dans une sous-population de cellules et se propage ensuite dans toute la population par contact physique cellule à cellule. En permettant la simulation du comportement d'une cellule individuelle, le modèle pourra servir de module dans la conception d'un modèle d'une population bactérienne composée de cellules hétérogènes. / Bacteria have evolved many types of genetically induced mechanisms to face different types of stresses and to adapt to new environments. Competence for natural transformation is one such process that promotes horizontal gene transfer. If phylogenetically distant species share conserved uptake and processing apparatus, competence regulatory circuits are not universal but adapted to every species' lifestyle. In Gram-positive bacteria, Streptococcus pneumoniae and Bacillus subtilis regulatory cascades are the best documented. If many mathematical models have been established to study different aspects of competence regulation in B. subtilis, only one population-scaled model has been developed for S. pneumoniae, a decade ago, based on hypotheses that are challenged by new experimental data. We develop, in S. pneumoniae, a knowledge-based model of the competence regulation at cell level that integrates the enriched biological knowledge acquired to date. The structural consistency of the network topology is confirmed using Petri net formalism. The network is further turned into a set of ordinary differential equations to study its dynamics behavior. Protein kinetics are estimated using time-series luminescence data and other parameter estimations are constrained according to available knowledge. We point out some gap in competence shut-off knowledge, and, after testing alternative models, we predict the requirement of a yet unknown late com gene product inhibiting the action of ComW, the ?x factor activator. We also bring new insights into this regulatory cascade by predicting the system components that might be involved in specific experimental behavior. Our model consolidates the experimental knowledge acquired on competence regulation in S. pneumoniae. Moreover, it can be applied to the other streptococci species belonging to the mitis and anginosus groups since they shared the same regulatory circuit. In the population, the competence shift happens first in a subpopulation of cells and spreads into the whole population through cell to cell contact. Allowing simulation of individual cell behavior, our model will provide a brick for the design of a population-scale model composed of heterogeneous cells.

Comprendre comment l’expression des gènes est régulée spécifiquement dans chaque tissu et de manière dynamique au cours du temps demeure une étape centrale de notre compréhension de l’organogénèse. L’identification des éléments cis-régulateurs de la transcription de manière tissu-spécifique peut permettre de comprendre les règles logiques d’organisation du réseau de gènes régulateur et aussi d’identifier de nouveaux acteurs (facteurs de transcription notamment). L’analyse de marques de chromatine (H3K27ac et H3K4me3) spécifiquement dans les cardioblastes (104 cellules) au cours de la différentiation a permis l’identification en masse de régions cis-régulatrices de la transcription. Via une approche d’apprentissage, de nouvelles régions régulatrices spécifiques des cardiomyocytes ainsi que 2 nouveaux facteurs de transcription (bagpipe, hamlet) ont été identifiées. L’alignement multiple des régions régulatrices suggère que les régions associées à H3K27ac dans les cellules cardiaques durant ces étapes de l’organogénèse partagent une séquence consensus. Ces nouveaux éléments régulateurs viennent compléter le réseau de gène régulateur au cours des étapes tardives de la cardiogénèse. / Understanding how gene expression is spatio-temporally regulated remains a crucial step in our understanding of organogenesis. Identification of transciptional cis-regulatory elements in a tissu-specific manner could allow to understand logical rules leading regulatory network organisation and to identify new actors (in particular transcription factors). Analysis of chromatin marks (H3K27ac and H3K4me3) specifically in cardiac cells (104 cells) during differentiation allowed the identification of transcriptional cis-regulatory regions. Via a machine learning approach, new cardiac specific regulatory regions and two transcription factors (bagpipe and hamlet) have been identified. Multiple sequence alignment of regulatory regions suggests that regions associated to H3K27ac in cardiac cells during these steps of organogenesis share a consensus sequence. These new regulatory elements integrate and complete the gene regulatory network underlying late steps of cardiogenesis.

Le système musculo-squelettique se compose des muscles, du squelette et du tissu conjonctif qui comprend, entre autres, les tendons et le tissu conjonctif musculaire. Le tissu conjonctif musculaire contribue à l'élasticité et à la rigidité des muscles, alors que les tendons transmettent les forces musculaires à l'os nécessaires aux mouvements du corps. Contrairement au muscle et au squelette, la mise en place et la formation du tissu conjonctif restent à ce jour peu étudiées. Afin d'identifier les mécanismes moléculaires sous-jacents à la formation du tissu conjonctif au cours du développement du membre, cinq facteurs de transcription à doigt de zinc ont été examinés : OSR1, OSR2, EGR1, KLF2 et KLF4. Ces facteurs de transcription sont exprimés dans différents sous-compartiments du système musculo-squelettique et leur surexpression influence la différentiation des cellules mésenchymateuses du membre. Afin d'élucider leurs rôles au niveau de la régulation génique, plusieurs stratégies à haut-débit (RNA-seq, ChIP-seq) ont été mises en place. Ces stratégies ont permis : (i) d'identifier que les facteurs de transcription partagent des fonctions régulatrices communes liées à la transduction du signal, à la communication cellulaire et à l'adhésion cellulaire ; (ii) de révéler que les gènes différentiellement exprimés étaient enrichis pour des signatures d'activation et de répression chromatiniennes, suggérant qu'ils sont dynamiquement régulés ; (iii) de distinguer les gènes cibles directs des cibles indirectes. Ces résultats fournissent ainsi une base pour des travaux futurs visant à mieux comprendre l'inter-connectivité entre les différents composants de l'appareil locomoteur. / The musculoskeletal system is composed of muscles, skeletal elements and connective tissues such as tendon and muscle connective tissue. Muscle connective tissue contributes to the elasticity and rigidity of muscles, while tendons transmit forces generated by muscles to the bone to allow body motion. In contrast to muscle and skeleton, connective tissue patterning and formation remain poorly investigated. In order to identify molecular mechanisms underlying connective tissue formation during limb development, five zinc-finger transcription factors were investigated: OSR1, OSR2, EGR1, KLF2 and KLF4. These transcription factors are expressed in distinct subcompartments of the musculoskeletal system and influence the differentiation of limb mesenchymal cells upon overexpression. To further investigate their roles at the molecular level, several genome-wide strategies (RNA-seq, ChIP-seq) were employed. These strategies enabled: (i) to identify that the transcription factors share common regulatory functions and positively regulate biological processes related to signal transduction, cell communication and biological adhesion; (ii) to reveal that the differentially expressed genes were enriched for both active and repressive chromatin signatures at their promoters, suggesting that they are dynamically regulated; (iii) to distinguish between indirect and direct target genes. Altogether, these results provide a framework for future investigations to better understand the interconnectivity between components of the musculoskeletal system.

Le métabolisme définit l’ensemble des réactions biochimiques au sein d’un organisme, lui permettant de survivre et de s’adapter dans différents environnements. La régulation de ces réactions requiert un processus complexe impliquant de nombreux effecteurs interagissant ensemble à différentes échelles.Développer des modèles de ces réseaux de régulation est ainsi une étape indispensable pour mieux comprendre les mécanismes précis régissant les systèmes vivants et permettre, à terme, la conception de systèmes synthétiques, autorégulés et adaptatifs, à l'échelle du génome. Dans le cadre de ces travaux interdisciplinaires, nous proposons d’utiliser une approche itérative d’inférence de réseau et d’interrogation afin de guider l’ingénierie du métabolisme de la levure d’intérêt industriel Yarrowia lipolytica.À partir de données transcriptomiques, le premier réseau de régulation de l’adaptation à la limitation en azote et de la production de lipides a été inféré pour cette levure. L’interrogation de ce réseau a ensuite permis de mettre en avant et valider expérimentalement l’impact de régulateurs sur l'accumulation lipidique.Afin d’explorer davantage les liens entre régulation et métabolisme, une nouvelle méthode, CoRegFlux, a été proposée pour la prédiction de phénotype métabolique à partir des profils d’activités des régulateurs dans les conditions étudiées.Ce package R, disponible sur la plateforme Bioconductor, a ensuite été utilisé pour mieux comprendre l’adaptation à la limitation en azote et identifier des phénotypes d’intérêts en vue de l’ingénierie de cette levure, notamment pour la production de lipides et de violacéine.Ainsi, par une approche itérative, ces travaux apportent de nouvelles connaissances sur les interactions entre la régulation et le métabolisme chez Y. lipolytica, l’identification de motifs de régulation chez cette levure et contribue au développement de méthodes intégratives pour la conception de souches assistée par ordinateur. / Metabolism defines the set of biochemical reactions within an organism, allowing it to survive and adapt to different environments. Regulating these reactions requires complex processes involving many effectors interacting together at different scales.Developing models of these regulatory networks is therefore an essential step in better understanding the precise mechanisms governing living systems and ultimately enabling the design of synthetic, self-regulating and adaptive systems at the genome level. As part of this interdisciplinary work, we propose to use an iterative network inference and interrogation approach to guide the engineering of the metabolism of the yeast of industrial interest Yarrowia lipolytica.Based on transcriptomic data, the first network for the regulation of adaptation to nitrogen limitation and lipid production in this yeast was inferred.The interrogation of this network has then allowed to to highlight and experimentally validate the impact of several regulators on lipid accumulation. In order to further explore the relationships between regulation and metabolism, a new method, CoRegFlux, has been proposed for the prediction of metabolic phenotype based on the influence profiles of regulators in the studied conditions. This R package, available on the Bioconductor platform, was then used to better understand adaptation to nitrogen limitation and to identify phenotypes of interest for strain engineering, particularly for the production of lipids and amino acid derivatives such as violacein.Thus, through an iterative approach, this work provides new insights into the interactions between regulation and metabolism in Y. lipolytica, conserved regulatory module in this yeast and contributes to the development of innovative integrative methods for computer-assisted strain design.

Squamous cell carcinoma (SCC) and adenocarcinoma (ADC) are the most common histological types of cervical cancer (CC). The worse prognosis of ADC cases highlights the need for better molecular characterization regarding differences between these CC types. RNA-Seq analysis of seven SCC and three ADC human papillomavirus 16-positive samples and the comparison with public data from non-tumoral human papillomavirus-negative cervical tissue samples revealed pathways exclusive to each histological type, such as the epithelial maintenance in SCC and the maturity-onset diabetes of the young (MODY) pathway in ADC. The transcriptional regulatory network analysis of cervical SCC samples unveiled a set of six transcription factor (TF) genes with the potential to positively regulate long non-coding RNA genes DSG1-AS1, CALML3-AS1, IGFL2-AS1, and TINCR. Additional analysis revealed a set of MODY TFs regulated in the sequence predicted to be repressed bymiR-96-5p ormiR-28-3p in ADC. These microRNAs were previously described to target LINC02381, which was predicted to be positively regulated by two MODY TFs upregulated in cervical ADC. Therefore, we hypothesize LINC02381might act by decreasing the levels ofmiR-96-5p andmiR-28-3p, promoting the MODY activation in cervical ADC. The novel TF networks here described should be explored for the development of more efficient diagnostic tools.

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