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Plusieurs souches cliniques de Candida albicans résistantes aux médicaments antifongiques azolés surexpriment des gènes encodant des effecteurs de la résistance appartenant à deux classes fonctionnelles : i) des transporteurs expulsant les azoles, CDR1, CDR2 et MDR1 et ii) la cible des azoles 14-lanostérol déméthylase encodée par ERG11. La surexpression de ces gènes est due à la sélection de mutations activatrices dans des facteurs de transcription à doigts de zinc de la famille zinc cluster (Zn2Cys6) qui contrôlent leur expression : Tac1p (Transcriptional activator of CDR genes 1) contrôlant l’expression de CDR1 et CDR2, Mrr1p (Multidrug resistance regulator 1), régulant celle de MDR1 et Upc2p (Uptake control 2), contrôlant celle d’ERG11. Un autre effecteur de la résistance clinique aux azoles est PDR16, encodant une transférase de phospholipides, dont la surexpression accompagne souvent celle de CDR1 et CDR2, suggérant que les trois gènes appartiennent au même régulon, potentiellement celui de Tac1p. De plus, la régulation transcriptionnelle du gène MDR1 ne dépend pas seulement de Mrr1p, mais aussi du facteur de transcription de la famille basic-leucine zipper Cap1p (Candida activator protein 1), un régulateur majeur de la réponse au stress oxydatif chez C. albicans qui, lorsque muté, induit une surexpression constitutive de MDR1 conférant la résistance aux azoles. Ces observations suggèrent qu’un réseau de régulation transcriptionnelle complexe contrôle le processus de résistance aux antifongiques azolés chez C. albicans. L’objectif de mon projet au doctorat était d’identifier les cibles transcriptionnelles directes des facteurs de transcription Tac1p, Upc2p et Cap1p, en me servant d’approches génétiques et de génomique fonctionnelle, afin de i) caractériser leur réseau transcriptionnel et les modules transcriptionnels qui sont sous leur contrôle direct, et ii) d’inférer leurs fonctions biologiques et ainsi mieux comprendre leur rôle dans la résistance aux azoles. Dans un premier volet, j’ai démontré, par des expériences de génétique, que Tac1p contrôle non seulement la surexpression de CDR1 et CDR2 mais aussi celle de PDR16. Mes résultats ont identifié une nouvelle mutation activatrice de Tac1p (N972D) et ont révélé la participation d’un autre régulateur dans le contrôle transcriptionnel de CDR1 et PDR16 dont l’identité est encore inconnue. Une combinaison d’expériences de transcriptomique et d’immunoprécipitation de la chromatine couplée à l’hybridation sur des biopuces à ADN (ChIP-chip) m’a permis d’identifier plusieurs gènes dont l’expression est contrôlée in vivo et directement par Tac1p (PDR16, CDR1, CDR2, ERG2, autres), Upc2p (ERG11, ERG2, MDR1, CDR1, autres) et Cap1p (MDR1, GCY1, GLR1, autres). Ces expériences ont révélé qu’Upc2p ne contrôle pas seulement l’expression d’ERG11, mais aussi celle de MDR1 et CDR1. Plusieurs nouvelles propriétés fonctionnelles de ces régulateurs ont été caractérisées, notamment la liaison in vivo de Tac1p aux promoteurs de ses cibles de façon constitutive et indépendamment de son état d’activation, et la liaison de Cap1p non seulement à la région du promoteur de ses cibles, mais aussi celle couvrant le cadre de lecture ouvert et le terminateur transcriptionnel putatif, suggérant une interaction physique avec la machinerie de la transcription. La caractérisation du réseau transcriptionnel a révélé une interaction fonctionnnelle entre ces différents facteurs, notamment Cap1p et Mrr1p, et a permis d’inférer des fonctions biologiques potentielles pour Tac1p (trafic et la mobilisation des lipides, réponse au stress oxydatif et osmotique) et confirmer ou proposer d’autres fonctions pour Upc2p (métabolisme des stérols) et Cap1p (réponse au stress oxydatif, métabolisme des sources d’azote, transport des phospholipides). Mes études suggèrent que la résistance aux antifongiques azolés chez C. albicans est intimement liée au métabolisme des lipides membranaires et à la réponse au stress oxydatif. / Many azole resistant Candida albicans clinical isolates overexpress genes encoding azole resistance effectors that belong to two functional categories: i) CDR1, CDR2 and MDR1, encoding azole-efflux transporters and ii) ERG11, encoding the target of azoles 14-lanosterol demethylase. The constitutive overexpression of these genes is due to activating mutations in transcription factors of the zinc cluster family (Zn2Cys6) which control their expression. Tac1p (Transcriptional activator of CDR genes 1), controlling the expression of CDR1 and CDR2, Mrr1p (Multidrug resistance regulator 1), regulating MDR1 expression and Upc2p (Uptake control 2), controlling the expression of ERG11. Another determinant of clinical azole resistance is PDR16, encoding a phospholipid transferase, whose overexpression often accompanies that of CDR1 and CDR2 in clinical isolates, suggesting that the three genes belong to the same regulon, potentially that of Tac1p. Further, MDR1 expression is not only regulated by Mrr1p, but also by the basic-leucine zipper transcription factor Cap1p (Candida activator protein 1), which controls the oxidative stress response in C. albicans and whose mutation confers azole resistance via MDR1 overexpression. These observations suggest that a complex transcriptional regulatory network controls azole resistance in C. albicans. My Ph.D. studies are aimed at identifying the direct transcriptional targets of Tac1p, Upc2p and Cap1p using genetics and functional genomics approches in order to i) characterize their regulatory network and the transcriptional modules under their direct control and ii) infer their biological functions and better understand their roles in azole resistance. In the first part of my studies, I showed that Tac1p does not only control the expression of CDR1 and CDR2, but also that of PDR16. My results also identified a new activating mutation in Tac1p (N972D) and revealed that the expression of CDR1 and PDR16 is under the control of another yet unknown regulator. The combination of transcriptomics and genome-wide location (ChIP-chip) approaches allowed me to identify the in vivo direct targets of Tac1p (PDR16, CDR1, CDR2, ERG2, others), Upc2p (ERG11, ERG2, MDR1, CDR1, others) and Cap1p (MDR1, GCY1, GLR1, others). These results also revealed that Upc2p does not only control the expression of ERG11 but also that of MDR1 and CDR1. Many new functional features of these transcription factors were found, including the constitutive binding of Tac1p to its targets under both activating and non-activating conditions, and the binding of Cap1p which extends beyond the promoter region of its target genes, to cover the open reading frame and the putative transcription termination regions, suggesting a physical interaction with the transcriptional machinery. The characterization of the transcriptional regulatory network revealed a functional interaction between these factors, notably between Cap1p and Mrr1p, and inferred potential biological functions for Tac1p (lipid mobilization and traffic, response to oxidative and osmotic stress) and confirmed or suggested other functions for Upc2p (sterol metabolism) and Cap1p (oxidative stress response, regulation of nitrogen utilization and phospholipids transport). Taken together, my results suggest that azole resistance in C. albicans is tightly linked to membrane lipid metabolism and oxidative stress response.

Os avanços na pesquisa em biologia molecular e bioquímica permitiram o desenvolvimento de técnicas capazes de extrair informações moleculares de milhares de genes simultaneamente, como DNA Microarrays, SAGE e, mais recentemente RNA-Seq, gerando um volume massivo de dados biológicos. O mapeamento dos níveis de transcrição dos genes em larga escala é motivado pela proposição de que o estado funcional de um organismo é amplamente determinado pela expressão de seus genes. No entanto, o grande desafio enfrentado é o pequeno número de amostras (experimentos) com enorme dimensionalidade (genes). Dessa forma, se faz necessário o desenvolvimento de novas técnicas computacionais e estatísticas que reduzam o erro de estimação intrínseco cometido na presença de um pequeno número de amostras com enorme dimensionalidade. Neste contexto, um foco importante de pesquisa é a modelagem e identificação de redes de regulação gênica (GRNs) a partir desses dados de expressão. O objetivo central nesta pesquisa é inferir como os genes estão regulados, trazendo conhecimento sobre as interações moleculares e atividades metabólicas de um organismo. Tal conhecimento é fundamental para muitas aplicações, tais como o tratamento de doenças, estratégias de intervenção terapêutica e criação de novas drogas, bem como para o planejamento de novos experimentos. Nessa direção, este trabalho apresenta algumas contribuições: (1) software de seleção de características; (2) nova abordagem para a geração de Redes Gênicas Artificiais (AGNs); (3) função critério baseada na entropia de Tsallis; (4) estratégias alternativas de busca para a inferência de GRNs: SFFS-MR e SFFS-BA; (5) investigação biológica das redes gênicas envolvidas na biossíntese de tiamina, usando a Arabidopsis thaliana como planta modelo. O software de seleção de características consiste de um ambiente de código livre, gráfico e multiplataforma para problemas de bioinformática, que disponibiliza alguns algoritmos de seleção de características, funções critério e ferramentas de visualização gráfica. Em particular, implementa um método de inferência de GRNs baseado em seleção de características. Embora existam vários métodos propostos na literatura para a modelagem e identificação de GRNs, ainda há um problema muito importante em aberto: como validar as redes identificadas por esses métodos computacionais? Este trabalho apresenta uma nova abordagem para validação de tais algoritmos, considerando três aspectos principais: (a) Modelo para geração de Redes Gênicas Artificiais (AGNs), baseada em modelos teóricos de redes complexas, os quais são usados para simular perfis temporais de expressão gênica; (b) Método computacional para identificação de redes gênicas a partir de dados temporais de expressão; e (c) Validação das redes identificadas por meio do modelo AGN. O desenvolvimento do modelo AGN permitiu a análise e investigação das características de métodos de inferência de GRNs, levando ao desenvolvimento de um estudo comparativo entre quatro métodos disponíveis na literatura. A avaliação dos métodos de inferência levou ao desenvolvimento de novas metodologias para essa tarefa: (a) uma função critério, baseada na entropia de Tsallis, com objetivo de inferir os inter-relacionamentos gênicos com maior precisão; (b) uma estratégia alternativa de busca para a inferência de GRNs, chamada SFFS-MR, a qual tenta explorar uma característica local das interdependências regulatórias dos genes, conhecida como predição intrinsecamente multivariada; e (c) uma estratégia de busca, interativa e flutuante, que baseia-se na topologia de redes scale-free, como uma característica global das GRNs, considerada como uma informação a priori, com objetivo de oferecer um método mais adequado para essa classe de problemas e, com isso, obter resultados com maior precisão. Também é objetivo deste trabalho aplicar a metodologia desenvolvida em dados biológicos, em particular na identificação de GRNs relacionadas a funções específicas de Arabidopsis thaliana. Os resultados experimentais, obtidos a partir da aplicação das metodologias propostas, mostraram que os respectivos ganhos de desempenho foram significativos e adequados para os problemas a que foram propostos. / Thanks to recent advances in molecular biology and biochemistry, allied to an ever increasing amount of experimental data, the functional state of thousands of genes can now be extracted simultaneously by using methods such as DNA microarrays, SAGE, and more recently RNA-Seq, generating a massive volume of biological data. The mapping of gene transcription levels at large scale is motivated by the proposition that information of the functional state of an organism is broadly determined by its gene expression. However, the main limitation faced is the small number of samples (experiments) with huge dimensionalities (genes). Thus, it is necessary to develop new computational and statistics techniques to reduce the inherent estimation error committed in the presence of a small number of samples with large dimensionality. In this context, particularly important related investigations are the modeling and identification of gene regulatory networks from expression data sets. The main objective of this research is to infer how genes are regulated, bringing knowledge about the molecular interactions and metabolic activities of an organism. Such a knowledge is fundamental for many applications, such as disease treatment, therapeutic intervention strategies and drugs design, as well as for planning high-throughput new experiments. In this direction, this work presents some contributions: (1) feature selection software; (2) new approach for the generation of artificial gene networks (AGN); (3) criterion function based on Tsallis entropy; (4) alternative search strategies for GRNs inference: SFFS-MR and SFFS-BA; (5) biological investigation of GRNs involved in the thiamine biosynthesis by adopting the Arabidopsis thaliana as a model plant. The feature selection software is an open-source multiplataform graphical environment for bioinformatics problems, which supports many feature selection algorithms, criterion functions and graphic visualization tools. In particular, a feature selection method for GRNs inference is also implemented in the software. Although there are several methods proposed in the literature for the modeling and identification of GRNs, an important open problem regards: how to validate such methods and its results? This work presents a new approach for validation of such algorithms by considering three main aspects: (a) Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data; (b) computational method for GRNs identification from temporal expression data; and (c) Validation of the identified AGN-based network through comparison with the original network. Through the development of the AGN model was possible the analysis and investigation of the characteristics of GRNs inference methods, leading to the development of a comparative study of four inference methods available in literature. The evaluation of inference methods led to the development of new methodologies for this task: (a) a new criterion function based on Tsallis entropy, in order to infer the genetic inter-relationships with better precision; (b) an alternative search strategy for the GRNs inference, called SFFS-MR, which tries to exploit a local property of the regulatory gene interdependencies, which is known as intrinsically multivariate prediction; and (c) a search strategy, interactive and floating, which is based on scale-free network topology, as a global property of the GRNs, which is considered as a priori information, in order to provide a more appropriate method for this class of problems and thereby achieve results with better precision. It is also an objective of this work, to apply the developed methodology in biological data, particularly in identifying GRNs related to specific functions of the Arabidopsis thaliana. The experimental results, obtained from the application of the proposed methodologies, indicate that the respective performances of each methodology were significant and adequate to the problems that have been proposed.

O câncer configura uma das maiores causas de mortalidade no mundo, caracterizando-se como uma doença complexa orquestrada por alterações genômicas e epigenômicas capazes de alterar a expressão gênica e a identidade celular. Nova evidência obtida por meio de um estudo genômico em larga escala e cujos dados encontram-se disponíveis no banco público do TCGA sugere que um em cada dez pacientes portadores de câncer pode ser classificado com maior eficácia tendo como base a taxonomia molecular quando comparada à histologia. Dessa maneira, nós hipotetizamos que o estabelecimento de mapas genômicos exibindo a localização de sítios de ligação de fatores de transcrição combinada à identificação de regiões diferencialmente metiladas e perfis alterados de expressão gênica possa nos auxiliar a caracterizar e explorar, ao nível molecular, fenótipos associados ao câncer. Avanços tecnológicos e bancos de dados públicos a exemplo do The Cancer Genome Atlas (TCGA), The Encyclopedia of DNA Elements (ENCODE) e o NIH Roadmap Epigenomics Mapping Consortium (Roadmap) têm proporcionado um recurso inestimável para interrogar o (epi)genoma de linhagens de células tumorais em cultura, bem como de tecidos normais e tumorais em alta resolução. Todavia, a informação biológica encontra-se armazenada em diferentes formatos e não há ferramentas computacionais para integrar esses dados, evidenciando um cenário atual que requer, com urgência, o desenvolvimento de ferramentas de bioinformática e softwares capazes de direcionar a solução deste obstáculo. Nesse contexto, o objetivo principal deste estudo consiste em implementar o desenvolvimento de ferramentas de bioinformática, na linguagem de programação R que, ao final do estudo, será submetido à comunidade científica do projeto Bioconductor sob a licença de código aberto GNU GPL versão 3. Além disso, ajudaremos nossos colaboradores com o aperfeiçoamento do ELMER, um pacote R/Bioconductor que identifica elementos reguladores usando dados de expressão gênica, de metilação do DNA e análise de motivo. Nossa expectativa é que essas ferramentas possam automatizar com acurácia a pesquisa, o download e a análise dos dados (epi)genômicos que se encontram atualmente disponíveis nas bases de dados públicas dos consórcios internacionais TCGA, ENCODE e Roadmap, além de integrá-los facilmente aos dados genômicos e epigenômicos gerados por pesquisadores por meio de experimentos em larga escala. Além disso, realizaremos também o processamento e a análise manual dos dados que serão automatizados pelas ferramentas, visando validar sua capacidade em descobrir assinaturas epigenômicas que possam redefinir subtipos de câncer. Por xi fim, as usaremos para investigar as diferenças moleculares entre dois subgrupos de gliomas recentemente descobertos por nosso laboratório. / Cancer, which is one of the major causes of mortality worldwide, is a complex disease orchestrated by aberrant genomic and epigenomic changes that can modify gene regulatory circuits and cellular identity. Emerging evidence obtained through high-throughput genomic data deposited within the public TCGA international consortium suggests that one in ten cancer patients would be more accurately classified by molecular taxonomy versus histology. Therefore, we have hypothesized that the establishment of genome-wide maps of the de novo DNA binding motifs localization coupled with differentially methylated regions and gene expression changes might help to characterize and exploit cancer phenotypes at the molecular level. Technological advances and public databases like The Cancer Genome Atlas (TCGA), The Encyclopedia of DNA Elements (ENCODE), and The NIH Roadmap Epigenomics Mapping Consortium (roadmap) have provided unprecedented opportunities to interrogate the epigenome of cultured cancer cell lines as well as normal and tumor tissues with high resolution. Markedly however, biological information is stored in different formats and there is no current tool to integrate the data, highlighting an urgent need to develop bioinformatic tools and/or computational softwares to overcome this challenge. In this context, the main purpose of this study is the development of bioinformatics tools in R programming language that will be submitted to the larger open-source Bioconductor community project under the GNU GPL3 (General Public License version 3). Also, we will help our collaborators improve of the R/Bioconductor ELMER package that identifies regulatory enhancers using gene expression, DNA methylation data and motif analysis. Our expectation is that these tools can effectively automate search, retrieve, and analyze the vast (epi)genomic data currently available from TCGA, ENCODE, and Roadmap, and integrate genomics and epigenomics features with researchers own high-throughput data. Furthermore, we will also navigate through these data manually in order to validate the capacity of these tools in discovering epigenomic signatures able to redefine subtypes of cancer. Finally, we will use them to investigate the molecular differences between two subgroups of gliomas, one of the most aggressive primary brain cancer, recently discovered by our laboratory.

As Escherichia coli produtoras da toxina Shiga (STEC) são importantes patógenos humanos, causando desde diarréias até a síndrome hemolítica urêmica (SHU). Há diversos sorotipos associados a SHU, tais como O157:H7 e O113:H21. No Brasil o sorotipo O113:H21 ainda não aparece associado a SHU, embora seja frequentemente isolado de carcaças e fezes bovinas. Nosso grupo já investigou comparativamente as redes de coexpressão gênica (RCG) de STEC EH41 (associado à SHU) e Ec472/01 (isolado de fezes bovinas). A análise comparativa do perfil transcricional de EH41 e Ec472/01 revelou que somente EH41 expressa um conjunto de genes que inclui o regulador transcricional dicA. A maioria destes genes está situada em um único módulo transcricional e podem estar associados a fatores de virulência. Assim, este trabalho centrou-se numa abordagem de biologia de sistemas, integrando análises genômica e fenotípica da resposta de enterócitos Caco-2 à EH41 e Ec472/01. A análise genômica baseou-se no estudo temporal de RCG para compreender os mecanismos moleculares envolvidos na patogenicidade desses dois isolados. As alterações fenotípicas ocorridas nas células Caco-2 ao longo da exposição a cada um dos isolados de STEC foram visualizadas através de MEV. A análise genômica mostrou que o mecanismo molecular da resposta de Caco-2 durante a interação com EH41 ou Ec472/01 é claramente distinto. Nas redes do grupo Caco-2/EH41 as alterações topológicas incluíram a perda do status scale free e a sua recuperação, com o estabelecimento de uma nova hierarquia de genes na rede. Esses resultados se enquadram no modelo de redes para transição saúde-doença: a nova rede representa a resposta adaptativa da célula ao patógeno, o que não significa um retorno à normalidade. Já no grupo Caco-2/Ec472 as redes, após a perda do status scale free, não recuperam esse status até o final do período estudado, o que sugere um estado de transição mais prolongado para reorganização da hierarquia da rede. Mais ainda, através da caracterização dos módulos transcricionais, foi possível compreender dinamicamente os mecanismos moleculares envolvidos na resposta diferencial de Caco-2 aos dois isolados aqui estudados. STEC EH41 induz rapidamente a resposta inflamatória e apoptótica a partir da primeira hora de interação enterócito-bactéria. Por outro lado, células Caco-2 em contato com Ec472/01 ativam, a partir de uma hora, a fagocitose e, a partir da segunda hora, expressam moduladores da homeostase imune. A análise fenotípica das células Caco-2 mostrou, de forma nítida, uma maior destruição dos microvilos dos enterócitos em contato com EH41 do que com Ec472/01. Integrando os resultados genômicos e fenotípicos pode-se concluir que EH41 induz em Caco-2 - em comparação com Ec472/01 - maiores e mais rápidas alterações na expressão gênica global, além de uma resposta inflamatória e apoptótica excessiva, levando assim a alterações morfológicas mais pronunciadas nas células Caco-2. Em seu conjunto, esses resultados contribuem para uma melhor compreensão dos mecanismos moleculares envolvidos na patogenicidade das STECs associadas à SHU. Assim, as perspectivas de desenvolvimento deste trabalho deverão incluir a investigação de fatores de virulência e vias moleculares envolvidas na indução das respostas imunes que podem conduzir à SHU / Shiga toxin-producing Escherichia coli (STEC) O113:H21 strains are associated with human diarrhea and some of these strains may cause hemolytic uremic syndrome (HUS). In Brazil O113:H21 strains are commonly found in cattle but, so far, were not isolated from HUS patients. Previously, our group conducted comparative gene co-expression network (GCN) analyses of two O113:H21 STEC strains: EH41, isolated from a HUS patient in Australia, and Ec472/01, isolated from bovine feces in Brazil. Differential transcriptome profiles for EH41 and Ec472/01 revealed a gene set exclusively expressed in EH41, which includes the dicA putative virulence factor regulator. GCN analysis showed that this set of genes constitutes an EH41 specific transcriptional module which may be associated to virulence factors. Therefore, in the present work a system biology approach was conducted to investigate the differential Caco-2 response - genomic and phenotypic - to EH41 (Caco-2/EH41) or to Ec472/01 (Caco- 2/Ec472) along enterocyte-bacteria interaction. The genomic analysis was based on temporal GCN data in order to gain a better understanding on the molecular mechanisms underlying the capacity to cause HUS. The phenotypic alterations in Caco-2 during enterocyte-bacteria interaction were assessed by scanning electronic microscopy (SEM). The genomic analysis showed that the molecular mechanism of Caco-2 response to EH41 or to Ec472/01 during enterocyte-bacteria interaction is clearly different. The GCN topological analyses for Caco-2/EH41 group revealed loss of the scale-free status after one hour of interaction, persistence of this condition along the second hour and establishment of a new gene hierarchy thereafter. These events resemble the network mechanism of health-disease transition. The new established network represents an adaptive cell response to the pathogen and not the return to a \"normal\" state. Conversely, the networks for Caco-2/Ec472 group showed a slow and progressive loss of the scale-free status without its restoration at the end of the time interval here studied. Through transcriptional module characterization it was possible to reveal the dynamic of the molecular mechanism involved in the Caco-2 differential responses to the STEC isolates. EH41 induces a rapid inflammatory and apoptotic response just after the first hour of enterocyte-bacteria interaction. Instead, the Caco-2 response to Ec472/01 is characterized by phagocytosis activation at the first hour, followed by the expression of immune response modulators after the second hour. SEM phenotypic analysis of Caco-2 cells along enterocyte-bacteria interaction showed more intense microvilli destruction in cells exposed to EH41, when compared to cells exposed to Ec472/01. The integration of genomic and phenotypic data allowed us to conclude that EH41, comparatively to Ec472/01, induces greater and precocious global gene expression alterations in Caco-2, what is related to excessive inflammatory and apoptotic responses. These responses are associated with the pronounced morphological alterations observed by SEM in Caco-2 cells exposed to EH41. Altogether, these results contribute for a better understanding of the molecular mechanism involved in STEC pathogenicity associated to HUS. Therefore, the future perspectives for the development of the present work should include the investigation of virulence factors and molecular pathways involved in the induction of immune responses leading to HUS

L'expression des gènes dans une cellule a longtemps été observable uniquement à travers des quantités moyennes mesurées sur des populations. L'arrivée des techniques «single-cell» permet aujourd'hui d'observer des niveaux d'ARN et de protéines dans des cellules individuelles : il s'avère que même dans une population de génome identique, la variabilité entre les cellules est parfois très forte. En particulier, une description moyenne est clairement insuffisante étudier la différenciation cellulaire, c'est-à-dire la façon dont les cellules souches effectuent des choix de spécialisation. Dans cette thèse, on s'intéresse à l'émergence de tels choix à partir de réseaux de régulation sous-jacents entre les gènes, que l'on souhaiterait pouvoir inférer à partir de données. Le point de départ est la construction d'un modèle stochastique de réseaux de gènes capable de reproduire les observations à partir d'arguments physiques. Les gènes sont alors décrits comme un système de particules en interaction qui se trouve être un processus de Markov déterministe par morceaux, et l'on cherche à obtenir un modèle statistique à partir de sa loi invariante. Nous présentons deux approches : la première correspond à une approximation de champ assez populaire en physique, pour laquelle nous obtenons un résultat de concentration, et la deuxième se base sur un cas particulier que l'on sait résoudre explicitement, ce qui aboutit à un champ de Markov caché aux propriétés intéressantes / Gene expression in a cell has long been only observable through averaged quantities over cell populations. The recent development of single-cell transcriptomics has enabled gene expression to be measured in individual cells: it turns out that even in an isogenic population, the molecular variability can be very important. In particular, an averaged description is not sufficient to account for cell differentiation. In this thesis, we are interested in the emergence of such cell decision-making from underlying gene regulatory networks, which we would like to infer from data. The starting point is the construction of a stochastic gene network model that is able to explain the data using physical arguments. Genes are then seen as an interacting particle system that happens to be a piecewise-deterministic Markov process, and our aim is to derive a tractable statistical model from its stationary distribution. We present two approaches: the first one is a popular field approximation, for which we obtain a concentration result, and the second one is based on an analytically tractable particular case, which provides a hidden Markov random field with interesting properties

En biologie synthétique, il existe plusieurs manières d’adresser les problèmes soulevés dans plusieurs domaines comme la thérapeutique, les biofuels, les biomatériaux ou encore les biocapteurs. Nous avons choisi de nous concentrer sur l’une d’entre elles : les réseaux de régulation génétique (RRG). Un constat peut être fait : la diversité des problèmes résolus grâce aux RRGs est bridée par la complexité de ces RRGs, qui a atteint une limite. Quelles solutions s’offrent aux biologistes, pour repousser cette limite et continuer d’augmenter la complexité de leur système ? Cette thèse a pour but de fournir aux biologistes les outils nécessaires à la conception et à la simulation de RRGs complexes. Un examen de l’état de l’art en la matière nous a mené à adapter les outils de la micro-électronique à la biologie ainsi qu’à créer un algorithme de programmation génétique pour la conception des RRGs. D’une part, nous avons élaboré les modèles Verilog A de différents systèmes biologiques (passe-bande, proie-prédateur, repressilator, XOR) ainsi que de la diffusion spatiotemporelle d’une molécule. Ces modèles fonctionnent très bien avec plusieurs simulateurs électroniques (Spectre et NgSpice). D’autre part, les premières marches vers l’automatisation de la conception de RRGs ont été gravies. En effet, nous avons développé un algorithme capable d’optimiser les paramètres d’un RRG pour remplir un cahier des charges donné. De plus, la programmation génétique a été utilisée pour optimiser non seulement les paramètres d’un RRG mais aussi sa topologie. Ces outils ont su prouver leur utilité en apportant des réponses pertinentes à des problèmes soulevés lors du développement de systèmes biologiques. Ce travail a permis de montrer que notre approche, à savoir adapter les outils de la micro-électronique et utiliser des algorithmes de programmation génétique, est valide dans le contexte de la biologie synthétique. L’assistance que notre environnement de développement fournit au biologiste devrait encourager l’émergence de systèmes plus complexes. / In synthetic biology, Gene Regulatory Networks (GRN) are one of the main ways to create new biological functions to solve problems in various areas (therapeutics, biofuels, biomaterials, biosensing). However, the complexity of the designed networks has reached a limit, thereby restraining the variety of problems they can address. How can biologists overcome this limit and further increase the complexity of their systems? The goal of this thesis is to provide the biologists with tools to assist them in the design and simulation of complex GRNs. To this aim, the current state of the art was examined and it was decided to adapt tools from the micro-electronic field to biology, as well as to create a Genetic Programming algorithm for GRN design. On the one hand, models of diffusion and of other various systems (band-pass, prey-predator, repressilator, XOR) were created and written in Verilog A. They are already implemented and well-functioning on the Spectre solver as well as a free solver, namely NgSpice. On the other hand, the first steps of automatic GRN design were achieved. Indeed, an algorithm able to optimize the parameters of a given GRN according to a specification was developed. Moreover, Genetic Programming was applied to GRN design, allowing the optimization of both the topology and the parameters of a GRN. These tools proved their usefulness for the biologists’ community by efficiently answering relevant biological questions arising in the development of a system. With this work, we were able to show that adapting microelectronics and Genetic Programming tools to biology is doable and useful. By assisting design and simulation, such tools should promote the emergence of more complex systems.

Seleção de características é um tópico muito importante em aplicações de reconhecimento de padrões, especialmente em bioinformática, cujos problemas são geralmente tratados sobre um conjunto de dados envolvendo muitas variáveis e poucas observações. Este trabalho analisa aspectos de seleção de características no problema de identificação de redes de regulação gênica a partir de sinais de expressão gênica. Particularmente, propusemos um modelo de redes gênicas probabilísticas (PGN) que devolve uma rede construída a partir da aplicação recorrente de algoritmos de seleção de características orientados por uma função critério baseada em entropia condicional. Tal critério embute a estimação do erro por penalização de amostras raramente observadas. Resultados desse modelo aplicado a dados sintéticos e a conjuntos de dados de microarray de Plasmodium falciparum, um agente causador da malária, demonstram a validade dessa técnica, tendo sido capaz não apenas de reproduzir conhecimentos já produzidos anteriormente, como também de produzir novos resultados. Outro aspecto investigado nesta tese é o fenômeno da predição intrinsecamente multivariada (IMP), ou seja, o fato de um conjunto de características ser um ótimo caracterizador dos objetos em questão, mas qualquer de seus subconjuntos propriamente contidos não conseguirem representá-los de forma satisfatória. Neste trabalho, as condições para o surgimento desse fenômeno foram obtidas de forma analítica para conjuntos de 2 e 3 características em relação a uma variável alvo. No contexto de redes de regulação gênica, foram obtidas evidências de que genes alvo de conjuntos IMP possuem um enorme potencial para exercerem funções vitais em sistemas biológicos. O fenômeno conhecido como canalização é particularmente importante nesse contexto. Em dados de microarray de melanoma, constatamos que o gene DUSP1, conhecido por exercer função canalizadora, foi aquele que obteve o maior número de conjuntos de genes IMP, sendo que todos eles possuem lógicas de predição canalizadoras. Além disso, simulações computacionais para construção de redes com 3 ou mais genes mostram que o tamanho do território de um gene alvo pode ter um impacto positivo em seu teor de IMP com relação a seus preditores. Esta pode ser uma evidência que confirma a hipótese de que genes alvo de conjuntos IMP possuem a tendência de controlar diversas vias metabólicas cruciais para a manutenção das funções vitais de um organismo. / Feature selection is a crucial topic in pattern recognition applications, especially in bioinformatics, where problems usually involve data with a large number of variables and small number of observations. The present work addresses feature selection aspects in the problem of gene regulatory network identification from expression profiles. Particularly, we proposed a probabilistic genetic network model (PGN) that recovers a network constructed from the recurrent application of feature selection algorithms guided by a conditional entropy based criterion function. Such criterion embeds error estimation by penalization of rarely observed patterns. Results from this model applied to synthetic and real data sets obtained from Plasmodium falciparum microarrays, a malaria agent, demonstrate the validity of this technique. This method was able to not only reproduce previously produced knowledge, but also to produce other potentially relevant results. The intrinsically multivariate prediction (IMP) phenomenon has been also investigated. This phenomenon is related to the fact of a feature set being a nice predictor of the objects in study, but all of its properly contained subsets cannot predict such objects satisfactorily. In this work, the conditions for the rising of this phenomenon were analitically obtained for sets of 2 and 3 features regarding a target variable. In the gene regulatory networks context, evidences have been achieved in which target genes of IMP sets possess a great potential to execute vital functions in biological systems. The phenomenon known as canalization is particularly important in this context. In melanoma microarray data, we verified that DUSP1 gene, known by having canalization function, was the one which composed the largest number of IMP gene sets. It was also verified that all these sets have canalizing predictive logics. Moreover, computational simulations for generation of networks with 3 or more genes show that the territory size of a target gene can contribute positively to its IMP score with regard to its predictors. This could be an evidence that confirms the hypothesis stating that target genes of IMP sets are inclined to control several metabolic pathways essential to the maintenance of the vital functions of an organism.

As Escherichia coli produtoras da toxina Shiga (STEC) são importantes patógenos humanos, causando desde diarréias até a síndrome hemolítica urêmica (SHU). Há diversos sorotipos associados a SHU, tais como O157:H7 e O113:H21. No Brasil o sorotipo O113:H21 ainda não aparece associado a SHU, embora seja frequentemente isolado de carcaças e fezes bovinas. Nosso grupo já investigou comparativamente as redes de coexpressão gênica (RCG) de STEC EH41 (associado à SHU) e Ec472/01 (isolado de fezes bovinas). A análise comparativa do perfil transcricional de EH41 e Ec472/01 revelou que somente EH41 expressa um conjunto de genes que inclui o regulador transcricional dicA. A maioria destes genes está situada em um único módulo transcricional e podem estar associados a fatores de virulência. Assim, este trabalho centrou-se numa abordagem de biologia de sistemas, integrando análises genômica e fenotípica da resposta de enterócitos Caco-2 à EH41 e Ec472/01. A análise genômica baseou-se no estudo temporal de RCG para compreender os mecanismos moleculares envolvidos na patogenicidade desses dois isolados. As alterações fenotípicas ocorridas nas células Caco-2 ao longo da exposição a cada um dos isolados de STEC foram visualizadas através de MEV. A análise genômica mostrou que o mecanismo molecular da resposta de Caco-2 durante a interação com EH41 ou Ec472/01 é claramente distinto. Nas redes do grupo Caco-2/EH41 as alterações topológicas incluíram a perda do status scale free e a sua recuperação, com o estabelecimento de uma nova hierarquia de genes na rede. Esses resultados se enquadram no modelo de redes para transição saúde-doença: a nova rede representa a resposta adaptativa da célula ao patógeno, o que não significa um retorno à normalidade. Já no grupo Caco-2/Ec472 as redes, após a perda do status scale free, não recuperam esse status até o final do período estudado, o que sugere um estado de transição mais prolongado para reorganização da hierarquia da rede. Mais ainda, através da caracterização dos módulos transcricionais, foi possível compreender dinamicamente os mecanismos moleculares envolvidos na resposta diferencial de Caco-2 aos dois isolados aqui estudados. STEC EH41 induz rapidamente a resposta inflamatória e apoptótica a partir da primeira hora de interação enterócito-bactéria. Por outro lado, células Caco-2 em contato com Ec472/01 ativam, a partir de uma hora, a fagocitose e, a partir da segunda hora, expressam moduladores da homeostase imune. A análise fenotípica das células Caco-2 mostrou, de forma nítida, uma maior destruição dos microvilos dos enterócitos em contato com EH41 do que com Ec472/01. Integrando os resultados genômicos e fenotípicos pode-se concluir que EH41 induz em Caco-2 - em comparação com Ec472/01 - maiores e mais rápidas alterações na expressão gênica global, além de uma resposta inflamatória e apoptótica excessiva, levando assim a alterações morfológicas mais pronunciadas nas células Caco-2. Em seu conjunto, esses resultados contribuem para uma melhor compreensão dos mecanismos moleculares envolvidos na patogenicidade das STECs associadas à SHU. Assim, as perspectivas de desenvolvimento deste trabalho deverão incluir a investigação de fatores de virulência e vias moleculares envolvidas na indução das respostas imunes que podem conduzir à SHU / Shiga toxin-producing Escherichia coli (STEC) O113:H21 strains are associated with human diarrhea and some of these strains may cause hemolytic uremic syndrome (HUS). In Brazil O113:H21 strains are commonly found in cattle but, so far, were not isolated from HUS patients. Previously, our group conducted comparative gene co-expression network (GCN) analyses of two O113:H21 STEC strains: EH41, isolated from a HUS patient in Australia, and Ec472/01, isolated from bovine feces in Brazil. Differential transcriptome profiles for EH41 and Ec472/01 revealed a gene set exclusively expressed in EH41, which includes the dicA putative virulence factor regulator. GCN analysis showed that this set of genes constitutes an EH41 specific transcriptional module which may be associated to virulence factors. Therefore, in the present work a system biology approach was conducted to investigate the differential Caco-2 response - genomic and phenotypic - to EH41 (Caco-2/EH41) or to Ec472/01 (Caco- 2/Ec472) along enterocyte-bacteria interaction. The genomic analysis was based on temporal GCN data in order to gain a better understanding on the molecular mechanisms underlying the capacity to cause HUS. The phenotypic alterations in Caco-2 during enterocyte-bacteria interaction were assessed by scanning electronic microscopy (SEM). The genomic analysis showed that the molecular mechanism of Caco-2 response to EH41 or to Ec472/01 during enterocyte-bacteria interaction is clearly different. The GCN topological analyses for Caco-2/EH41 group revealed loss of the scale-free status after one hour of interaction, persistence of this condition along the second hour and establishment of a new gene hierarchy thereafter. These events resemble the network mechanism of health-disease transition. The new established network represents an adaptive cell response to the pathogen and not the return to a \"normal\" state. Conversely, the networks for Caco-2/Ec472 group showed a slow and progressive loss of the scale-free status without its restoration at the end of the time interval here studied. Through transcriptional module characterization it was possible to reveal the dynamic of the molecular mechanism involved in the Caco-2 differential responses to the STEC isolates. EH41 induces a rapid inflammatory and apoptotic response just after the first hour of enterocyte-bacteria interaction. Instead, the Caco-2 response to Ec472/01 is characterized by phagocytosis activation at the first hour, followed by the expression of immune response modulators after the second hour. SEM phenotypic analysis of Caco-2 cells along enterocyte-bacteria interaction showed more intense microvilli destruction in cells exposed to EH41, when compared to cells exposed to Ec472/01. The integration of genomic and phenotypic data allowed us to conclude that EH41, comparatively to Ec472/01, induces greater and precocious global gene expression alterations in Caco-2, what is related to excessive inflammatory and apoptotic responses. These responses are associated with the pronounced morphological alterations observed by SEM in Caco-2 cells exposed to EH41. Altogether, these results contribute for a better understanding of the molecular mechanism involved in STEC pathogenicity associated to HUS. Therefore, the future perspectives for the development of the present work should include the investigation of virulence factors and molecular pathways involved in the induction of immune responses leading to HUS

In spite of remarkable progress in molecular biology, our understanding of the dynamics and functions of intra- and inter-cellular biological networks has been hampered by their complexity. Kinetics modelling, an important type of mathematical modelling, provides a rigorous and reliable way to reveal the complexity of biological networks. In this thesis, two genetic regulatory networks have been investigated via kinetic models. In the first part of the study, a model is developed to represent the transcriptional regulatory network essential for the circadian rhythms in Drosophila. The model incorporates the transcriptional feedback loops revealed so far in the network of the circadian clock (PER/TIM and VRI/PDP1 loops). Conventional Hill functions are not used to describe the regulation of genes, instead the explicit reactions of binding and unbinding processes of transcription factors to promoters are modelled. The model is described by a set of ordinary differential equations and the parameters are estimated from the in vitro experimental data of the clocks’ components. The simulation results show that the model reproduces sustained circadian oscillations in mRNA and protein concentrations that are in agreement with experimental observations. It also simulates the entrainment by light-dark cycles, the disappearance of the rhythmicity in constant light and the shape of phase response curves resembling that of experimental results. The model is robust over a wide range of parameter variations. In addition, the simulated E-box mutation, perS and perL mutants are similar to that observed in the experiments. The deficiency between the simulated mRNA levels and experimental observations in per01, tim01 and clkJrk mutants suggests some differences in the model from reality. Finally, a possible function of VRI/PDP1 loops is proposed to increase the robustness of the clock. In the second part of the study, the sources of intrinsic noise and the influence of extrinsic noise are investigated on an intracellular viral infection system. The contribution of the intrinsic noise from each reaction is measured by means of a special form of stochastic differential equation, the chemical Langevin equation. The intrinsic noise of the system is the linear sum of the noise in each of the reactions. The intrinsic noise arises mainly from the degradation of mRNA and the transcription processes. Then, the effects of extrinsic noise are studied by means of a general form of stochastic differential equation. It is found that the noise of the viral components grows logarithmically with increasing noise intensities. The system is most susceptible to noise in the virus assembly process. A high level of noise in this process can even inhibit the replication of the viruses. In summary, the success of this thesis demonstrates the usefulness of models for interpreting experimental data, developing hypotheses, as well as for understanding the design principles of genetic regulatory networks.