Cellular networks are required for a variety of processes in complex organisms. Caenorhabditis elegans is a useful model to gain insight into the gene regulatory networks that assemble cellular networks. Mutations in a variety of genes can affect the sinusoidal locomotive pattern of C. elegans. We isolated the mutant jd1500 from a standard genetic screen looking for mutants in C. elegans that exhibit asymmetric locomotive patterns. The two aims of this study were to: 1) identify the gene and characterize its role in the gene regulatory network and 2) characterize the cells affected by the mutation. We reasoned that jd1500 likely disrupts the proper balance between dorsal and ventral body wall muscle contractions. By using three-point genetic mapping, we predicted the locus of jd1500 between -9.42 and -11.73 centimorgans of the X chromosome. Our results implicate the embryonic, cholinergic DB motor neurons as likely cellular targets of the mutation.

Many cell types stochastically switch phenotypes under some conditions, so that genetically identical sister cells may behave quite differently in a common environment. This non-genetic variability likely arises from noise in gene expression, which can be co-opted to allow random fate determination. This thesis examines both phenomena from experimental and theoretical perspectives, starting with a phenotypic switch. Cells of Bacillus subtilis grow either as individual, motile cells, or as connected groups of sessile cells called chains. We constructed an array of microfluidic channels in which we could capture and observe single cells in a constant environment over hundreds of generations of growth. These conditions allow unperturbed observation of decision-making driven only by factors internal to the cell. We observe that switching is asymmetric: transitions from motility to chaining occur with constant probability (memorylessly), but the reverse transition is tightly timed (exhibits memory). These properties are explained by dissecting the genetic circuit underlying switching, which can be quantitatively separated into components responsible for initiation and maintenance of the state. We propose that memory enables transgenerational cooperation between a cell founding a biofilm and its progeny, and that a stochastic sequestration mechanism is the source of random switching. Next, we introduce an exact framework for analyzing noise in gene networks that phrases results in terms of compounded parameters with simple interpretations. We uncover a basic identity that relates fluctuations in the production and degradation rates of one component to those of any other component within the cell. Since the result is exact, it applies to whole classes of gene networks. We identify basic constraints on the ability of negative feedback to suppress noise, and show that suppressing noise in one species generally requires introducing it elsewhere. When applied to the most common model of gene expression, the identity reveals a simple connection between the statistics of proteins and their cognate mRNAs. We reanalyze a recent experimental study of stochastic gene expression and show that the data are inconsistent with this prediction. Thus in contrast to early studies of single genes, there is currently discord between models and measurements of stochastic gene expression.

Dans cette thèse nous nous intéressons à la manière dont la structure des plantes émerge du fonctionnement de leur méristème apical. Pour cela, nous étudions la structure du méristème apical d'Arabidopsis thaliana à différentes échelles. La thèse commence par étudier les plantes à l'échelle macroscopique dont la phyllotaxie a été perturbée et par le développement d'outils mathématiques pour quantifier et analyser ces perturbations. Ensuite, nous étudions à une échelle plus microscopiques quelles peuvent être les raisons de telles perturbations. Pour cela, nous avons testé une version étendue d'un modèle proposé par Douady et Couder (1996) dans lequel plusieurs paramètres clés sont modifiés par différentes sources de bruit. Cette étude de modélisation suggère que la stabilité de la taille de la zone de la zone centrale peut être un facteur clé dans la robustesse phyllotaxie. Alors que des modèles 3D réalistes des champs d'inhibition autour des primordia ont été développés récemment, une telle étude est toujours manquante pour les tissus réalistes en 3D dans le cas de la zone centrale. Cela nous conduit finalement à analyser en profondeur le réseau de régulation génétique qui contrôle la taille de la zone centrale dans le méristème. Nous avons implémenté une version 3D d'un modèle de la littérature de la zone centrale et testé ce modèle sur des méristèmes 3D obtenues à partir des images 3D de la microscopie laser. / In this dissertation we are interested in how shoot structure emerges from the functioning of their apical meristem. For this, we investigate the structure of Arabidopsis thaliana shoot apical meristem at different scales. The thesis starts by studying at macroscopic scale plants in which the regularity of phyllotaxis has been perturbed and developing mathematical tools to quantify and analyze such complex patterns. Then we try to investigate at more microscopic scales what can be the reasons for such perturbations. For this we tested an extended version of Douady and Couder's model (1996) in which several key parameters are varied by adding different sources of noise. This modeling study enables us to hypothesize that the stability in size of both the primordia inhibition zone and the central zone may be key factors in phyllotaxis robustness. While realistic 3D models of primordia inhibitory fields have been developed recently, such a study is still missing for realistic 3D tissues in the case of the central zone. This lead us finally to analyze in depth the gene regulatory network that controls the size of the central zone in the meristem. We implemented a 3D version of a model in literature modulating the size of the central zone and tested this model on 3D meristem cellular structures obtained from 3D laser microscope images.

Les plantes sont des organismes sessiles se développant dans un environnement hétérogène et fluctuant. La capacité d'acquisition des nutriments par le système racinaire est donc un caractère important pour leur croissance et leur développement.L'azote (N), notamment sous forme nitrate (NO3-), fait partie de ces éléments qui sont limitant pour la croissance des plantes mais aussi très mobiles dans le sol donc fréquemment distribués de façon hétérogène. Les plantes s'adaptent à cette contrainte en modulant le développement racinaire ainsi que la capacité de transport de ce nutriment dans les différentes parties du système racinaire en fonction de la disponibilité en NO3- et du besoin en azote (N) de la plante entière. Cette adaptation repose donc sur la combinaison de deux voies de signalisation, i) une signalisation locale dépendante de la disponibilité en NO3- dans le milieu extérieur ii) une signalisation longue distance (ou systémique) racines-feuilles-racines relative au besoin en N de la plante entière.Toutefois, les bases moléculaires de la signalisation longue distance comme les mécanismes de régulation qui y sont associés ne sont pas totalement connus. Ils reposent sur l'intégration au niveau des parties aériennes de signaux d'origine racinaire, provenant des racines exposées au NO3- mais aussi de celles qui en sont privées. Les parties aériennes jouent alors un rôle majeur dans la modulation de la physiologie et du développement racinaire en condition de disponibilité hétérogène en NO3-. Des études précédentes ont montré que la biosynthèse de cytokinines est essentielle pour la mise en place de cette réponse adaptative. De plus, il est connu qu'après un apport de NO3-, la biosynthèse de cette hormone dans les racines puis son accumulation dans les parties aériennes est augmentée. Dans ce contexte, nous avons émis l'hypothèse que les cytokinines pourraient correspondre à un messager racines/feuilles important pour la signalisation systémique NO3--dépendante.L'objectif de mon projet de thèse consistait à comprendre comment les parties aériennes contrôlent l'acquisition racinaire du NO3- en condition de disponibilité hétérogène en NO3-. Pour reproduire cette condition en laboratoire, le système de 'split-root', permettant de séparer le système racinaire en deux parties isolées pouvant être traitées différemment, a été utilisé pour exposer les plantes à différentes conditions de disponibilité en NO3-. Dans ces différentes conditions, les réponses moléculaires, métaboliques et physiologiques ont été caractérisées chez des plantes sauvages d'Arabidopsis et comparées à celles de mutants affectés dans la biosynthèse, le transport acropetal ou encore dans la perception des cytokinines. La combinaison de ces différentes approches m'a ainsi permis de démontrer que les cytokinines, et plus précisément les trans-zéatines, sont effectivement un messager racines-feuilles crucial pour la mise en place des réponses de la racine à une disponibilité hétérogène en NO3-. De plus, j'ai montré que l'apport hétérogène en NO3- comparé à l'apport homogène entraîne une importante reprogrammation de l'expression génique dans les parties aériennes qui est largement dépendante de ce transport de trans-zéatines vers les feuilles. Enfin, l'intégration de ces données transcriptomiques au sein de réseaux géniques a permis d'identifier des gènes candidats intéressants comme acteurs possibles de la signalisation feuilles-racines. / Plants are sessile organisms growing in a heterogeneous and fluctuating environment. Thus, foraging for nutrients is an important trait for plant growth and development. Nitrogen (N), especially as nitrate (NO3-) form, is one limiting element for plant growth but is also highly mobile in the soil leading to frequent heterogeneity distribution. Plants are managing this constraint through the regulation of root development and NO3- uptake in the different parts of the root system according to the spatial NO3- availability and the N needs of the whole plant. This adaptation relies on a dual signaling pathway involving i) a local signaling related to external NO3- supply and ii) a root-shoot-root long-distance (systemic) signaling related to the plant N needs..However, the molecular basis of the long-distance signaling as well as the regulatory mechanisms associated with, are not fully understood. They rely on the integration at the shoot level of signals originating from both NO3--supplied and N-deprived root parts. Therefore, the shoots have a key role for an efficient adaptation to heterogeneous NO3- environment through the adjustment of root physiology and development. Previously, cytokinin biosynthesis has been shown to be essential for both molecular and morphological root responses to NO3- heterogeneous environment. Moreover, it is known that upon NO3- supply, de novo biosynthesis of this hormone in the roots is increased along with its accumulation in the shoots. In this context, we hypothesized that cytokinins could correspond to an important root to shoot signal involved in NO3--dependent systemic signaling.The main objective of my PhD project was to decipher and understand how the shoots control root NO3- acquisition in response to spatial NO3- heterogeneity. To do so, we used the 'split-root' system, in which physically isolated roots of a same plant are challenged with different NO3- environments. In this framework, we characterized physiological, metabolic and molecular responses of Arabidopsis wild-type plants that we compared to responses of mutants impaired in cytokinin biosynthesis, acropetal transport or perception. The combination of these different approaches allowed me to demonstrate that cytokinins, and especially trans-zeatin species are indeed a root to shoot messenger that is crucial for root responses to spatial NO3- heterogeneity. Moreover, I have shown that NO3- heterogeneous supply compared to homogeneous supply triggers a substantial reprogramming of gene expression in aerial part, which largely depends on this trans-zeatin transport toward the shoots. Finally, the integration of these transcriptomic modifications into gene networks led to the identification of interesting candidate genes to characterize the shoot-to-root signaling.

Os microRNAs estão envolvidos no processo de regulação da expressão gênica da célula, onde a molécula de microRNA se liga com o RNA mensageiro interrompendo, assim, a expressão do respectivo gene pela interrupção da tradução. A bioinformática tem auxiliado na identificação de vários genes codificadores de microRNAs em plantas e animais, incluindo mamíferos, por meio de analises de dados de microarray; assim como na predição de estruturas. Os dados de expressão de microRNAs e RNAs mensageiros foram obtidos por meio de cooperação firmada entre o Laboratório de Bioinformática do Departamento de Genética da Faculdade de Medicina de Ribeirão Preto - USP, coordenado pela orientadora desse projeto, e o Laboratório de Imunogenética Molecular do mesmo departamento, coordenado pelo Professor Doutor Geraldo A. S. Passos. Durante o desenvolvimento e os testes realizados, foram utilizados dados (valores numéricos de dados de expressão coletados por microarrays) provenientes da comparação da expressão de microRNAs e RNAs do timo de camundongos non obese diabetic que reproduzem diabetes melitus do tipo 1, e dados provenientes da comparação da expressão de microRNAs e RNAs de outros experimentos. O presente projeto teve como objetivo o desenvolvimento de um pipeline para a análise in silico de dados de expressão gênica de microRNAs e mRNAs obtidos por microarray. Com base em dados de expressão de microRNAs e RNAs mensageiros, foi possível a análise de diversas ferramentas e o desenvolvimento e ajuste de scripts para que seja possível a análise sequencial de tais dados. Dessa forma, o pipeline desenvolvido inclui a quantificação dos dados de expressão gênica a partir das lâminas de microarray, a normalização dos dados, as análises estatísticas das sequências diferencialmente expressas utilizando o Multi Experiment Viewer, a construção de redes de interação microRNAs-RNAs mensageiros e a busca de alvos de microRNAs baseada nesta rede, ambos pelo GenMir++. O pipeline desenvolvido é executado com facilidade e possibilitou a correta análise dos dados, evitando desperdício de tempo em análises de bancada. A partir dos resultados obtidos, novos alvos de miRNA foram encontrados com o uso do pipeline e comprovados em bancada. Tais resultados apresentados no 55º Congresso Brasileiro de Genética com o resumo intitulado MicroRNA-mRNA Network Controlling the Promiscuous Gene Expression in the Thymus of NOD (Non Obese Diabetic) Mice: Implications in the Emergence of Type 1 Diabetes Mellitus. / The microRNAs are involved in the regulation of gene expression of the cell. The miRNA molecule binds to the messenger RNA and interrupts the gene expression by disrupting the translation. Through microarray data analysis, bioinformatics is a valuable aid for the identification of several genes that encode miRNAs in plants and animals, including mammals. It is also very useful for predicting structures. Data of miRNA and mRNA expression were obtained by the collaboration the Bioinformatics Laboratory and the Molecular Immunogenetics Laboratory of the Department of Genetics of the Faculty of Medicine of Ribeirão Preto - USP, coordinated by professors Silvana Giuliatti and Geraldo A. S. Passos, respectively. During the development and tests of the research, microarrays data (numerical values os the expression) were obtained from the comparison between the expression of miRNA and mRNA of the thymus of non obese diabetic mice with diabetes mellitus type 1, as well as from comparisons of their expression in other experiments. The present study is aimed at the development of a pipeline for in silico analysis of the data of miRNAs and mRNA gene expression obtained by microarray. Based on miRNAs and mRNA expression, it was possible to analyze several tools, develop and adjust scripts that allowed the sequential analysis of such data. The pipeline includes the quantification of gene expression data from microarray, the normalization of the data, the statistical analysis of differentially expressed sequences using Multi Experiment Viewer, the construction of networks of interaction of miRNA-mRNAs, and the search for targets of miRNAs based on such network using GenMir++. The pipeline was performed easily and allowed the correct analysis of the data, avoiding waste of time in bench analysis. From the results, new targets of miRNA were found using the pipeline and were verified further in bench analysis. The results were presented in the 55 th Brazilian Genetics Congress in the paper entitled \"MicroRNA-mRNA Network Controlling the Promiscuous Gene Expression in the Thymus of NOD (Non Obese Diabetic) Mice: Implications in the Emergence of Type 1 Diabetes Mellitus\".

Análise de expressão gênica em larga escala é de fundamental importância para a biologia molecular atual pois possibilita a medida dos níveis de expressão de milhares de genes simultaneamente, o que torna viável a realização de trabalhos voltados para biologia de sistemas (systems biology). Dentre as principais técnicas experimentais disponíveis para esta finalidade, a tecnologia de microarray tem sido amplamente utilizada. Este procedimento para medida de expressão gênica é bastante complexo e os dados obtidos são freqüentemente observacionais, o que dificulta a modelagem estatística. Não existe um protocolo padrão para a geração e avaliação desses dados, sendo portanto necessário buscar procedimentos de análise que sejam adequados para cada caso. Assim, os principais métodos matemáticos e estatísticos aplicados para a análise desses dados deveriam estar disponíveis de uma forma organizada, coerente e simples em um ambiente computacional que confira robustez, confiabilidade e reprodutibilidade às análises realizadas. Uma forma de garantir estas características é através da representação (e documentação) de todos os algoritmos utilizados na forma de um grafo direcionado e acíclico que descreva todo o conjunto de transformações, ou operações, aplicadas seqüencialmente ao conjunto de dados. De acordo com esta filosofia, um ambiente foi implementado neste trabalho incorporando diversos procedimentos disponíveis na literatura atual, além de outros que foram aprimorados ou propostos nesta tese. Dentre os métodos de análise já disponíveis que foram incorporados destacam-se aqueles para a construção de agrupamentos, busca de genes diferencialmente expressos e classificadores, construção de redes de relevância e classificação funcional de grupos gênicos. Além disso, o método de construção de redes de relevância foi revisto e aprimorado e um modelo estatístico para a classificação funcional de redes de regulação gênica foi proposto e implementado. Esses dois últimos métodos surgiram a partir de problemas biológicos para os quais não existiam procedimentos de análise adequados na literatura. Finalmente, são apresentados dois conjuntos de dados que foram analisados utilizando diversas ferramentas disponíveis neste ambiente computacional. / High throughput gene expression analysis has a great importance to molecular biology nowadays because it can measure expression profiles for hundreds of genes, and this turn possible studies focused in systems biology. Between the main experimental techniques available in this direction, the microarray technology has been widely used. This experimental procedure to quantify gene expression profiles is very complex and the data obtained is frequently observational, what difficult the statistical modelling. There is not a standard protocol for the generation and evaluation of microarray data, therefore it is necessary to search by adequate analysis methods for each case. Thus, the main mathematical and statistical methods applied to microarray data analysis would have to be available in an organized, coherent and simple way in a computational environment that confer robustness, reliability and reproducibility to the data analysis. One way to guarantee these characteristics is through the representation (and documentation) of all used algorithms as a directed and acyclic graph that describes the set of transformations, or operations, applied sequentially to the dataset. According to this philosophy, an environment was implemented in this work aggregating several data analysis procedures already available in the literature, beyond other methods that were improved or proposed in this thesis. Between the procedures already available that were incorporated we can distinguish that ones for cluster analysis, differentially expressed genes and classifiers search, construction of relevance networks and functional classification of gene groups. Moreover, the method for construction of relevance networks was revised and improved and an statistical model was proposed and implemented for the functional classification of gene regulation networks. The last two procedures was born from biological problems for which adequate data analysis methods didn?t exist in the literature. Finally, we presented two datasets that were evaluated using several data analysis procedures available in this computational environment.

Un des problèmes actuels en bio-informatique est de comprendre les mécanismes de régulation au sein d’une cellule ou d’un organisme. L’objectif de la thèse est d’étudier les réseaux de co-expression de gènes chez le pommier avec la particularité d’y intégrer les transcrits anti-sens. Les transcrits anti-sens sont des ARN généralement non-codants, dont les différents modes d’action sont encore mal connus. Dans notre étude exploratoire du rôle des anti-sens, nous proposons d’une part une analyse fonctionnelle différentielle qui met en évidence l’intérêt de l’intégration des données anti-sens en transcriptomique. D’autre part, concernant les réseaux de gènes, nous proposons de limiter l’inférence à un cœur de réseau et nous introduisons alors une méthode d’analyse différentielle permettant de comparer un réseau obtenu à partir de données sens avec un réseau contenant des données sens et anti-sens. Nous introduisons ainsi la notion de gènes AS-impacté, qui permet d’identifier des gènes dont les interactions au sein d’un réseau de co-expression sont fortement impactées par la prise en compte de transcrits anti-sens. Pour les données pommier que nous avons étudiées et qui concerne la maturation des fruits et leur conservation à basse température, l’interprétation biologique des résultats de notre analyse différentielle fournit des pistes pertinentes pour une étude expérimentale plus ciblée de gènes ou de voies de signalisation dont l’importance pourrait être sous-estimée sans la prise en compte des données anti-sens. / A challenging task in bioinformatics is to decipher cell regulation mechanisms. The objective of this thesis is to study gene networks from apple data with the particularity to integrate anti-sense transcription data. Anti-sense transcripts are mostly non coding RNAs and their different roles in the cell are still not well known. In our study, to explore the role of anti-sense transcripts, we first propose a differential functional analysis that highlights the interest of integrating anti-sense data into a transcriptomic analysis. Then, regarding gene networks, we propose to focus on inference of a core network and we introduce a new differential analysis method that allows to compare a sense network with a sense and anti-sense network. We thus introduce the notion of AS-impacted genes, that allows to identify genes that are highly co-expressed with anti-sense transcripts. We analysed apple data related to ripening of fruits stored in cold storage; biological interpretation of the results of our differential analysisprovides some promising leads to a more targeted experimental study of genes or pathways, which role could be underestimated without integration of anti-sense data.

As large-scale techniques for studying and measuring gene expressions have been developed, automatically inferring gene interaction networks from expression data has emerged as a popular technique to advance our understanding of cellular systems. Accurate prediction of gene interactions, especially in multicellular organisms such as Drosophila or humans, requires temporal and spatial analysis of gene expressions, which is not easily obtainable from microarray data. New image based techniques using in-sit hybridization(ISH) have recently been developed to allowlarge-scale spatial-temporal profiling of whole body mRNA expression. However, analysis of such data for discovering new gene interactions still remains an open challenge. This thesis studies the question of predicting gene interaction networks from ISH data in three parts. First, we present SPEX2, a computer vision pipeline to extract informative features from ISH data. Next, we present an algorithm, GINI, for learning spatial gene interaction networks from embryonic ISH images at a single time step. GINI combines multi-instance kernels with recent work in learning sparse undirected graphical models to predict interactions between genes. Finally, we propose NP-MuScL (nonparanormal multi source learning) to estimate a gene interaction network that is consistent with multiple sources of data, having the same underlying relationships between the nodes. NP-MuScL casts the network estimation problem as estimating the structure of a sparse undirected graphical model. We use the semiparametric Gaussian copula to model the distribution of the different data sources, with the different copulas sharing the same covariance matrix, and show how to estimate such a model in the high dimensional scenario. We apply our algorithms on more than 100,000 Drosophila embryonic ISH images from the Berkeley Drosophila Genome Project. Each of the 6 time steps in Drosophila embryonic development is treated as a separate data source. With spatial gene interactions predicted via GINI, and temporal predictions combined via NP-MuScL, we are finally able to predict spatiotemporal gene networks from these images.

Os microRNAs estão envolvidos no processo de regulação da expressão gênica da célula, onde a molécula de microRNA se liga com o RNA mensageiro interrompendo, assim, a expressão do respectivo gene pela interrupção da tradução. A bioinformática tem auxiliado na identificação de vários genes codificadores de microRNAs em plantas e animais, incluindo mamíferos, por meio de analises de dados de microarray; assim como na predição de estruturas. Os dados de expressão de microRNAs e RNAs mensageiros foram obtidos por meio de cooperação firmada entre o Laboratório de Bioinformática do Departamento de Genética da Faculdade de Medicina de Ribeirão Preto - USP, coordenado pela orientadora desse projeto, e o Laboratório de Imunogenética Molecular do mesmo departamento, coordenado pelo Professor Doutor Geraldo A. S. Passos. Durante o desenvolvimento e os testes realizados, foram utilizados dados (valores numéricos de dados de expressão coletados por microarrays) provenientes da comparação da expressão de microRNAs e RNAs do timo de camundongos non obese diabetic que reproduzem diabetes melitus do tipo 1, e dados provenientes da comparação da expressão de microRNAs e RNAs de outros experimentos. O presente projeto teve como objetivo o desenvolvimento de um pipeline para a análise in silico de dados de expressão gênica de microRNAs e mRNAs obtidos por microarray. Com base em dados de expressão de microRNAs e RNAs mensageiros, foi possível a análise de diversas ferramentas e o desenvolvimento e ajuste de scripts para que seja possível a análise sequencial de tais dados. Dessa forma, o pipeline desenvolvido inclui a quantificação dos dados de expressão gênica a partir das lâminas de microarray, a normalização dos dados, as análises estatísticas das sequências diferencialmente expressas utilizando o Multi Experiment Viewer, a construção de redes de interação microRNAs-RNAs mensageiros e a busca de alvos de microRNAs baseada nesta rede, ambos pelo GenMir++. O pipeline desenvolvido é executado com facilidade e possibilitou a correta análise dos dados, evitando desperdício de tempo em análises de bancada. A partir dos resultados obtidos, novos alvos de miRNA foram encontrados com o uso do pipeline e comprovados em bancada. Tais resultados apresentados no 55º Congresso Brasileiro de Genética com o resumo intitulado MicroRNA-mRNA Network Controlling the Promiscuous Gene Expression in the Thymus of NOD (Non Obese Diabetic) Mice: Implications in the Emergence of Type 1 Diabetes Mellitus. / The microRNAs are involved in the regulation of gene expression of the cell. The miRNA molecule binds to the messenger RNA and interrupts the gene expression by disrupting the translation. Through microarray data analysis, bioinformatics is a valuable aid for the identification of several genes that encode miRNAs in plants and animals, including mammals. It is also very useful for predicting structures. Data of miRNA and mRNA expression were obtained by the collaboration the Bioinformatics Laboratory and the Molecular Immunogenetics Laboratory of the Department of Genetics of the Faculty of Medicine of Ribeirão Preto - USP, coordinated by professors Silvana Giuliatti and Geraldo A. S. Passos, respectively. During the development and tests of the research, microarrays data (numerical values os the expression) were obtained from the comparison between the expression of miRNA and mRNA of the thymus of non obese diabetic mice with diabetes mellitus type 1, as well as from comparisons of their expression in other experiments. The present study is aimed at the development of a pipeline for in silico analysis of the data of miRNAs and mRNA gene expression obtained by microarray. Based on miRNAs and mRNA expression, it was possible to analyze several tools, develop and adjust scripts that allowed the sequential analysis of such data. The pipeline includes the quantification of gene expression data from microarray, the normalization of the data, the statistical analysis of differentially expressed sequences using Multi Experiment Viewer, the construction of networks of interaction of miRNA-mRNAs, and the search for targets of miRNAs based on such network using GenMir++. The pipeline was performed easily and allowed the correct analysis of the data, avoiding waste of time in bench analysis. From the results, new targets of miRNA were found using the pipeline and were verified further in bench analysis. The results were presented in the 55 th Brazilian Genetics Congress in the paper entitled \"MicroRNA-mRNA Network Controlling the Promiscuous Gene Expression in the Thymus of NOD (Non Obese Diabetic) Mice: Implications in the Emergence of Type 1 Diabetes Mellitus\".

Análise de expressão gênica em larga escala é de fundamental importância para a biologia molecular atual pois possibilita a medida dos níveis de expressão de milhares de genes simultaneamente, o que torna viável a realização de trabalhos voltados para biologia de sistemas (systems biology). Dentre as principais técnicas experimentais disponíveis para esta finalidade, a tecnologia de microarray tem sido amplamente utilizada. Este procedimento para medida de expressão gênica é bastante complexo e os dados obtidos são freqüentemente observacionais, o que dificulta a modelagem estatística. Não existe um protocolo padrão para a geração e avaliação desses dados, sendo portanto necessário buscar procedimentos de análise que sejam adequados para cada caso. Assim, os principais métodos matemáticos e estatísticos aplicados para a análise desses dados deveriam estar disponíveis de uma forma organizada, coerente e simples em um ambiente computacional que confira robustez, confiabilidade e reprodutibilidade às análises realizadas. Uma forma de garantir estas características é através da representação (e documentação) de todos os algoritmos utilizados na forma de um grafo direcionado e acíclico que descreva todo o conjunto de transformações, ou operações, aplicadas seqüencialmente ao conjunto de dados. De acordo com esta filosofia, um ambiente foi implementado neste trabalho incorporando diversos procedimentos disponíveis na literatura atual, além de outros que foram aprimorados ou propostos nesta tese. Dentre os métodos de análise já disponíveis que foram incorporados destacam-se aqueles para a construção de agrupamentos, busca de genes diferencialmente expressos e classificadores, construção de redes de relevância e classificação funcional de grupos gênicos. Além disso, o método de construção de redes de relevância foi revisto e aprimorado e um modelo estatístico para a classificação funcional de redes de regulação gênica foi proposto e implementado. Esses dois últimos métodos surgiram a partir de problemas biológicos para os quais não existiam procedimentos de análise adequados na literatura. Finalmente, são apresentados dois conjuntos de dados que foram analisados utilizando diversas ferramentas disponíveis neste ambiente computacional. / High throughput gene expression analysis has a great importance to molecular biology nowadays because it can measure expression profiles for hundreds of genes, and this turn possible studies focused in systems biology. Between the main experimental techniques available in this direction, the microarray technology has been widely used. This experimental procedure to quantify gene expression profiles is very complex and the data obtained is frequently observational, what difficult the statistical modelling. There is not a standard protocol for the generation and evaluation of microarray data, therefore it is necessary to search by adequate analysis methods for each case. Thus, the main mathematical and statistical methods applied to microarray data analysis would have to be available in an organized, coherent and simple way in a computational environment that confer robustness, reliability and reproducibility to the data analysis. One way to guarantee these characteristics is through the representation (and documentation) of all used algorithms as a directed and acyclic graph that describes the set of transformations, or operations, applied sequentially to the dataset. According to this philosophy, an environment was implemented in this work aggregating several data analysis procedures already available in the literature, beyond other methods that were improved or proposed in this thesis. Between the procedures already available that were incorporated we can distinguish that ones for cluster analysis, differentially expressed genes and classifiers search, construction of relevance networks and functional classification of gene groups. Moreover, the method for construction of relevance networks was revised and improved and an statistical model was proposed and implemented for the functional classification of gene regulation networks. The last two procedures was born from biological problems for which adequate data analysis methods didn?t exist in the literature. Finally, we presented two datasets that were evaluated using several data analysis procedures available in this computational environment.