The exhaustion of oil fields together with the undesirable effects of its use has turned sugarcane into an attractive crop for the biofuel market, increasing its economic and environmental importance. The position of Brazil as the world\'s major sugarcane producer and the need to expand the planted area to soil with less favorable conditions makes the study of drought, one of the abiotic stresses affecting the most of this crop yield, essential for the future of Brazil as the main exporter of this commodity. This work has the aim of providing a comprehensive analysis of sugarcane drought responses in the physiological and molecular levels. In order to do that we followed four strategies. First, we performed the analysis of physiology and transcriptome (microarray) of drought stressed sugarcane plants in three time points (4 days of stress, 6 days of stress and re-watering) of a greenhouse experiment. The plant material analyzed was leaves and roots. Second, aiming to identify different genes and new patterns of expression it was done the analysis of RNA-Seq from the most discrepant condition, from both leaves and roots, found by the microarray, third, we performed the analysis of a drought progression experiment through physiology and qRT-PCR of selected candidate genes and forth we built co-expression networks to detect interesting patterns. Physiology analysis showed that plants were under moderate to severe water stress with decreases of up to 97% in photosynthesis. Microarray data indentified 7,867 unique SAS with a fold change of more than 2 or less that 0.5, and 575 unique SAS differentially expressed. The analysis of the identified sequences allowed the observation that in leaves after 4 days of stress, the plant is mostly transducing the signal from the environment, while after 6 days and after rehydration there is a more functional response of the plant, with re-watering leading the metabolism back to homeostase. In the case of roots, it was observed a similar response, however roots take longer to go back to the initial condition, since several genes are still being down-regulated even after re-watering. There are also pathways presenting an opposite pattern in the analyzed tissues, being activated in one tissue but repressed in the other, such as Phenylpropanoid Biosynthesis pathway. Furthermore, while in leaves there is a restriction on photosynthesis, on roots it seems to be a restriction on growth. RNA-Seq de novo assembly showed 28,240 differentially expressed features in leaves and 7,435 in roots, while using the reference genome (unpublished data) it was possible to identify 38,317 differentially expressed genes in leaves and 7,649 in roots, and the analysis of KEGG pathways indicate that ABA has a major role in both leaves and roots responses to drought, but in leaves there is an interplay of phytohormones. Drought progression experiment confirms the results obtained from microarray and shows that when stress is extreme, gene expression starts to decrease, suggesting the plant might be entering in senescence. Co-expression analysis allowed the determination of three modules correlated with physiological parameters altered during water stress, and lead to the identification of some possible hub genes that may be important for sugarcane responses to drought. Furthermore, it was possible to identify genes that through both co-expression and qRT-PCR analysis had similar patterns of expression. Altogether, these results give us a comprehensive view of the alterations in sugarcane responses to water stress and helped us gain insight for defining better suited candidate genes for plant breeding. / A exaustão dos combustíveis fósseis juntamente com os efeitos não desejáveis de seu uso, tonaram a cana-de-açúcar uma cultura atrativa para o mercado de biocombustíveis, aumentando a sua importância econômica e ambiental. A posição do Brasil como o principal produtor de cana-de-açúcar e a necessidade de expandir a área plantada para regiões com condições menos favoráveis, tornam o estudo da seca, um dos principais estresses abióticos que afetam a produtividade da cultura, essencial para o futuro do Brasil como o principal exportador dessa comoditie. Este trabalho tem o objetivo de fornecer uma análise global das respostas da cana-de-açúcar à seca, tanto em nível fisiológico quanto molecular. Para isso, foram seguidas quatro estratégias. Primeiro foi realizada uma análise da fisiologia e do transcriptoma (microarranjo) de plantas de cana-de-açúcar cultivadas em casa de vegetação e estressadas por três períodos diferentes (4 dias de estresse, 6 dias de estresse e reidratação). Os tecidos analisados foram folha e raiz. Segundo, com o objetivo de identificar diferentes genes e novos padrões de expressão, foi realizada a análise de RNA-Seq em tecidos de folha e raiz utilizando a condição mais discrepante identificada pelo microarray; terceiro, foi feita a análise de um experimento de progressão da seca por meio da fisiologia e qRT-PCR usando genes candidatos selecionados. A quarta estratégia foi a construção de redes de co-expressão objetivando detectar módulos de genes relacionados à resposta a seca. As análises de fisiologia mostraram que as plantas estavam sob estresse moderado a severo com diminuição de até 97% na fotossíntese. Os dados de microarray levaram à identificação de 7.867 SAS únicos com diferença de razão de expressão maior que 2 ou menor que 0,5, e 575 SAS únicos diferencialmente expressos. A análise das sequencias identificadas permitiu a observação de que em folhas, depois de 4 dias de estresse, há basicamente a transdução dos sinais obtidos a partir do ambiente, enquanto depois de 6 dias e após a reidratação há uma resposta mais funcional da planta, com a última conduzindo o metabolismo de volta à homeostase. No caso das raízes foi observado uma resposta similar, porém, as raízes demoram mais tempo para voltar à condição inicial, de forma diversos genes continuam reprimidos mesmo após a reidratação. Há ainda rotas metabólicas, como o Biosíntese de Fenilpropanoides, que apresentam perfis opostos nos tecidos analisados, sendo ativada em um e reprimida no outro. Além disso, enquanto em folhas há uma restrição na fotossíntese, em raízes parece existir uma restrição no crescimento. A análise de novo do RNA-Seq mostrou 28.240 \"features\" diferencialmente expressos em folhas e 7.435 em raízes, enquanto a utilização do genoma de referência (dados não publicados) identificou 38.317 genes diferencialmente expressos em folha e 7.649 em raiz, sendo que a análise das rotas do KEGG indicam que o ABA tem um papel principal nas respostas à seca em ambos os tecidos, no entanto em folhas existe uma interação entre fitohormônios. O experimento de progressão da seca confirma os resultados obtidos a partir do microarranjo e mostram que quando o estresse é severo, a expressão gênica começa à diminuir, sugerindo que a planta pode estar entrando em senescência. As análises de coexpressão permitiram a determinação de três módulos correlacionados com parâmetros de fisiologia alterados durante o estresse hídrico, e conduziram à identificação de alguns genes centrais que podem ser importantes para as respostas da cana à seca. Além disso, foi possível identificar genes que tanto pela análise de co-expressão quanto pelo qRT-PCR apresentam padrões similar de expressão. Juntos, esses resultados forneceram uma visão global das alterações que ocorrem na cana-de-açúcar em resposta ao estresse hídrico e ajudaram a obter conhecimento para seleção de genes candidatos adequados para o melhoramento genético de plantas.

Les nouvelles biotechnologies offrent aujourd'hui la possibilité de récolter une très grande variété et quantité de données biologiques (génomique, protéomique, métagénomique...), ouvrant ainsi de nouvelles perspectives de recherche pour la compréhension des processus biologiques. Dans cette thèse, nous nous sommes plus spécifiquement intéressés aux données transcriptomiques, celles-ci caractérisant l'activité ou le niveau d'expression de plusieurs dizaines de milliers de gènes dans une cellule donnée. L'objectif était alors de proposer des outils statistiques adaptés pour analyser ce type de données qui pose des problèmes de "grande dimension" (n<<p), car collectées sur des échantillons de tailles très limitées au regard du très grand nombre de variables (ici l'expression des gènes).La première partie de la thèse est consacrée à la présentation de méthodes d'apprentissage supervisé, telles que les forêts aléatoires de Breiman et les modèles de régressions pénalisées, utilisées dans le contexte de la grande dimension pour sélectionner les gènes (variables d'expression) qui sont les plus pertinents pour l'étude de la pathologie d'intérêt. Nous évoquons les limites de ces méthodes pour la sélection de gènes qui soient pertinents, non pas uniquement pour des considérations d'ordre statistique, mais qui le soient également sur le plan biologique, et notamment pour les sélections au sein des groupes de variables fortement corrélées, c'est à dire au sein des groupes de gènes co-exprimés. Les méthodes d'apprentissage classiques considèrent que chaque gène peut avoir une action isolée dans le modèle, ce qui est en pratique peu réaliste. Un caractère biologique observable est la résultante d'un ensemble de réactions au sein d'un système complexe faisant interagir les gènes les uns avec les autres, et les gènes impliqués dans une même fonction biologique ont tendance à être co-exprimés (expression corrélée). Ainsi, dans une deuxième partie, nous nous intéressons aux réseaux de co-expression de gènes sur lesquels deux gènes sont reliés si ils sont co-exprimés. Plus précisément, nous cherchons à mettre en évidence des communautés de gènes sur ces réseaux, c'est à dire des groupes de gènes co-exprimés, puis à sélectionner les communautés les plus pertinentes pour l'étude de la pathologie, ainsi que les "gènes clés" de ces communautés. Cela favorise les interprétations biologiques, car il est souvent possible d'associer une fonction biologique à une communauté de gènes. Nous proposons une approche originale et efficace permettant de traiter simultanément la problématique de la modélisation du réseau de co-expression de gènes et celle de la détection des communautés de gènes sur le réseau. Nous mettons en avant les performances de notre approche en la comparant à des méthodes existantes et populaires pour l'analyse des réseaux de co-expression de gènes (WGCNA et méthodes spectrales). Enfin, par l'analyse d'un jeu de données réelles, nous montrons dans la dernière partie de la thèse que l'approche que nous proposons permet d'obtenir des résultats convaincants sur le plan biologique, plus propices aux interprétations et plus robustes que ceux obtenus avec les méthodes d'apprentissage supervisé classiques. / Today's, new biotechnologies offer the opportunity to collect a large variety and volume of biological data (genomic, proteomic, metagenomic...), thus opening up new avenues for research into biological processes. In this thesis, what we are specifically interested is the transcriptomic data indicative of the activity or expression level of several thousands of genes in a given cell. The aim of this thesis was to propose proper statistical tools to analyse these high dimensional data (n<<p) collected from small samples with regard to the very large number of variables (gene expression variables). The first part of the thesis is devoted to a description of some supervised learning methods, such as random forest and penalized regression models. The following methods can be used for selecting the most relevant disease-related genes. However, the statistical relevance of the selections doesn't determine the biological relevance, and particularly when genes are selected within a group of highly correlated variables or co-expressed genes. Common supervised learning methods consider that every gene can have an isolated action in the model which is not so much realistic. An observable biological phenomenum is the result of a set of reactions inside a complex system which makes genes interact with each other, and genes that have a common biological function tend to be co-expressed (correlation between expression variables). Then, in a second part, we are interested in gene co-expression networks, where genes are linked if they are co-expressed. More precisely, we aim to identify communities of co-expressed genes, and then to select the most relevant disease-related communities as well as the "key-genes" of these communities. It leads to a variety of biological interpretations, because a community of co-expressed genes is often associated with a specific biological function. We propose an original and efficient approach that permits to treat simultaneously the problem of modeling the gene co-expression network and the problem of detecting the communities in network. We put forward the performances of our approach by comparing it to the existing methods that are popular for analysing gene co-expression networks (WGCNA and spectral approaches). The last part presents the results produced by applying our proposed approach on a real-world data set. We obtain convincing and robust results that help us make more diverse biological interpretations than with results produced by common supervised learning methods.

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Soybean is a dominant oilseed cultivated worldwide for its use in multiple sectors such as food and feed industries, animal husbandry, cosmetics and pharmaceutical sectors, and more recently, in production of biodiesel. Increasing demand of soybean, changing environmental conditions, and evolution of pathogens pose challenges to soybean production in limited acreage. Genetic research is the key to ensure the continued growth in soybean production, with enhanced yield and quality, while reducing the losses due to diseases and pests. This research is focused on the understanding of transcriptional regulation of two economically important agronomic traits of soybean: low seed phytic acid and resistance to Soybean mosaic virus (SMV), using the 'transcriptomics' and 'genomics' approaches. The low phytic acid (lpa) soybean is more desirable than conventional soybean, as phytic acid is an anti-nutritional component of seed and is associated with phosphorus pollution. Despite the eco-friendly nature of the lpa soybean, it shows poor emergence, which reduces soybean yield. This research is mainly focused on addressing the impact of lpa-causing mutations on seed development, which is suspected to cause low emergence in lpa soybeans. The differences in transcriptome profiles of developing seeds in lpa and normal phytic acid soybean are revealed and the biological pathways that may potentially be involved in regulation of seed development are suggested. The second research project is focused on Rsv3-type resistance, which is effective against most virulent strains of Soybean mosaic virus. The Rsv3 locus, which maps on to soybean chromosome 14, contains 10 genes including a cluster of coiled coil-nucleotide binding-leucine rich repeat (CC-NB-LRR) protein-encoding genes. This dissertation employed a comparative sequencing approach to narrow down the list of Rsv3 gene candidates to the most promising CC-NB-LRR gene. The evidence provided in this study clearly indicates a single CC-NB-LRR gene as the most promising candidate to deliver Rsv3-type resistance. / Ph. D.

Uma das possíveis razões do sucesso da área de Redes Complexas decorre da flexibilidade destas estruturas para representação e modelagem de inúmeros sistemas complexos, incluindo em biologia. Entretanto, existem alguns aspectos do uso destes conceitos ainda pouco detalhados, como a questão da limiarização de relacionamentos graduados de forma a se obter uma rede binária de conexões. Uma outra questão interessante, ainda em aberto, refere-se a como redes complexas derivadas de sistemas diversos assemelham-se ou não umas às outras. Em biologia, esta questão aparece com particular interesse no que se refere às escalas das estruturas e sistemas biológicos, motivando a busca de analogias estruturais e funcionais. O presente trabalho de doutorado situa-se na interseção destes dois problemas. Em primeiro lugar, utilizamos a importante questão da limiarização de redes de co-expressão gênica como laboratório para desenvolver e comparar cinco métodos deste tipo, com fundamentações diferentes. Verificamos que dependendo da natureza do banco de dados, o impacto da limiarização nas propriedades topológicas pode ser grande, e sugerimos diretrizes de como utilizar os métodos diante do comportamento dos dados. Em seguida, abordamos a representação dos canais do sistema Haversiano dos ossos, com o objetivo de estudar este problema em particular e compará-lo com as redes de co-expressão na busca de analogias topológicas. As análises mostraram que os ossos são indistinguíveis em relação às propriedades topológicas das redes, mas nota-se uma variação mais pronunciada em relação às propriedades geométricas. Isso sugere que a arquitetura topológica do sistema vascular pode ser independente do tipo ósseo, mas que a demanda biológica de transporte pode variar em relação à posição no mesmo osso, e entre ossos diferentes. Como as redes do sistema Haversiano possuem pesos relacionados à espessura dos canais, utilizamos e comparamos os métodos de limiarização aqui propostos como forma de validação dos resultados. Concluindo estes desenvolvimentos, realizamos uma comparação estrutural dos dois tipos de redes obtidas, ou seja, de co-expressão gênica e de canais Haversianos. / One of the possible reasons for the success of Complex Networks arises from the flexibility of these structures for representation and modeling of numerous complex systems, including in biology. However, there are still some aspects of the use of these concepts, such as the question of the thresholding of graduated relationships in order to obtain a binary network of connections. Another interesting question, still open, concerns how complex networks derived from different systems are similar to another or are not. In biology, this question appears with particular interest in the scales of biological structures and systems, motivating the search for structural and functional analogies. The present PhD work lies at the intersection of these two problems. First, we used the important question of the thresholding of gene co-expression networks as a laboratory for development and to compare five methods of this type, with different foundations. We have found that depending on the nature of the database, the impact of thresholding on topological properties may be large, and we suggest guidelines on how to use the methods in face of the data`s behavior. Then, we discuss the characterization of the channels of the Haversian system of bones, with the aim of studying this particular problem and comparing it with the networks of co-expression in the search for topological analogies. The analyzes showed that the bones are indistinguishable in relation to the topological properties of the networks, but a more pronounced variation in relation to the geometric properties is noticed. This suggests that the topological architecture of the vascular system may be independent of the bone type but that the biological demand for transport may be varying relatively to the position in the same bone and between different bones. As the networks of the Haversian system have weights related to the thickness of the channels, we used and compared the thresholding methods proposed here for the validation of the results. Concluding these developments, we performed a structural comparison of the two types of networks obtained, the gene co-expression network and the Haversian channels network.

La verticilliose de la luzerne cultivée (Medicago sativa L.) est une maladie de flétrissement vasculaire causée par le champignon du sol Verticillium alfalfae. C’est une des maladies les plus dévastatrices et les plus difficiles à contrôler. Les symptômes sont un jaunissement des feuilles suivi de flétrissement et défoliation. Les structures de dormance produites en fin de cycle de maladie constituent une source de contamination pour plusieurs années. Aucun traitement fongicide n’est efficace, la seule méthode de contrôle reste la production de variétés résistantes. En raison de sa nature tétraploïde et de son allogamie, il est difficile de réaliser des études génétiques sur M. sativa. Un pathosystème entre la légumineuse modèle Medicago truncatula et V. alfalfae a été mis au point pour étudier les mécanismes mis en place au cours de l’interaction entre V. alfalfae et son hôte. Les lignées A17 et F83005.5 ont été identifiées comme étant respectivement résistante et sensible à la souche V31-2 de V. alfalfae. La première partie de ce travail de thèse est une étude comparative du processus d’infection de V. alfalfae V31-2 au cours d’une interaction compatible et incompatible. Nous avons étudié la cinétique de colonisation des racines d’A17 et F83005.5 avec la souche V31-2 exprimant le gène marqueur GFP ce qui confère une fluorescence verte au champignon. Les observations en microscopie confocale ont montré que le champignon se développait dans les racines des deux lignées contrastées de façon similaire pendant les premières étapes d’infection. Quelques jours plus tard, il n’était plus détectable dans la lignée résistante, tandis qu’il colonisait les vaisseaux du xylème dans la lignée sensible et avançait vers les parties aériennes. La lignée résistante A17 était donc capable d’inhiber totalement le développement du pathogène dans la partie racinaire. Ce résultat a été confirmé par la quantification de l’ADN du pathogène dans la racine et dans les parties aériennes. Nous avons conclu que la lignée A17 exprime une résistance totale à V. alfalfae. Dans la deuxième partie de cette thèse, nous avons cherché à identifier le rôle des hormones dans les mécanismes de défense de M. truncatula en réalisant des traitements exogènes avec l’acide salicylique (SA), le méthyl jasmonate (MeJA), l’éthylène (ET), l’auxine et l’acide abscissique (ABA). Ces traitements n’avaient aucun effet sur la résistance d’A17, mais toutes les hormones, à l’exception du MeJA, protégeaient la lignée sensible contre les symptômes de la maladie. La quantification de l’ADN du champignon in planta a montré que seule l’ABA inhibait significativement le développement du pathogène. Dans la troisième partie, nous avons cherché à identifier des acteurs moléculaires impliqués dans la résistance et la sensibilité en comparant le transcriptome de la lignée F83005.5 et A17 dans la phase précoce de l’infection. L’analyse des gènes différentiellement exprimés en réponse à l’inoculation montre que les deux lignées induisent des gènes impliqués dans la production de métabolites secondaires, et des gènes des voies de signalisation hormonale. Mais seule la lignée résistante montre une induction de l’expression de gènes de résistance et de gènes impliqués dans les voies de signalisation tels que des gènes de la synthèse de l’ABA et des facteurs de transcription. Ces résultats renforcent l’hypothèse que l’ABA serait un facteur important dans la résistance à V. alfalfae chez M. truncatula. L’analyse des réseaux de gènes coexprimés a montré une désorganisation de la réponse de la lignée F83005.5. En revanche, dans la lignée A17, on observe une réponse organisée et orientée vers la défense. Ce travail décrit pour la première fois les mécanismes de défense de M. truncatula contre V. alfalfae. L’ensemble des résultats montre que la résistance exprimée chez la lignée A17 est différente des mécanismes de résistance contre la verticilliose décrits chez la tomate et le coton. / Verticllium wilt of alfalfa (Medicago sativa L.) is a vascular disease caused by the soil fungus Verticillium alfalfae. It is one of the most devastating diseases and most difficult to control. Symptoms are leaf yellowing followed by wilting and defoliation. Survival structures which are produced at the end of the disease cycle are a source of inoculum for many years. Fungicide treatment is not efficient, and the only way to control this disease is to breed resistant cultivars. Genetic studies are difficult in M. sativa because it is tetraploid and outcrossing. A pathosystem has been set up in our laboratory in order to study the mechanisms involved in the interaction between V. alfalfae and its host. It involves the model legume plant M. truncatula and strain V31-2 of V. alfalfae. The lines A17 and F83005.5 were identified as respectively resistant and susceptible to V31-2. The first part of this thesis is a comparative study of the infection process of V. alfalfae V31-2 in a compatible and incompatible interaction. The time course of root colonization in lines A17 and F83005.5 was studied with a GFP-expressing strain which confers green fluorescence to the fungus. Observations by confocal microscopy showed that the fungus developed in a similar way in roots of both lines during the first stage of the interaction. Some days later the fungus was not detectable anymore in roots of the resistant line, but has colonized the xylem vessels and grew towards the aerial part of the plant in the susceptible line. Quantification of fungal DNA in roots and aerial parts confirmed these results. This showed that the resistant line A17 was able to suppress the pathogen’s development in the root. It can be concluded that line A17 presents total resistance towards V. alfalfae. The second part of the thesis concerns the role of phytohormones for defence mechanisms against V. alfalfae in M. truncatula. Susceptible and resistant plants were treated with salicylic acid (SA), methyl jasmonate (MeJA), ethylene (ET), auxine and abscissic acid (ABA). Resistance of line A17 was not affected by these treatments, but all hormones except MeJA protected the susceptible line against disease symptoms. However, when fungal DNA was quantified in planta in these assays, only ABA inhibited the pathogen’s development significantly. The third part of this thesis aims at identifying molecular factors involved in resistance and susceptibility. To address this topic, the transcriptome of lines A17 and F83005.5 was compared during the early stages of infection, in inoculated or mock-inoculated plants. A bioinformatics analysis of differentially expressed genes showed that both lines responded to inoculation by inducing genes involved in secondary metabolism and hormone signaling pathways. However, only resistant line A17 showed induction of the expression of putative resistance and signaling genes, genes involved in ABA synthesis and transcription factors. This result confirms our hypothesis that ABA might be an important factor in M. truncatula resistance against V. alfalfae. Gene network analysis of co-expressed genes showed a disorganised response in the susceptible line, whereas in the resistant line the response was highly organised and turned to defence. Taken together, this work describes for the first time defence mechanisms against V. alfalfae in M. truncatula. The results show that resistance of line A17 is different from resistance mechanisms Verticillium resistance described in tomato and cotton. Several approaches for future research are presented in order to test our hypotheses concerning genes and molecules putatively involved in this interaction. With regard to applied research, defence and signaling genes identified in this work may be useful for the improvement of alfalfa, after functional validation.

L’objectif de ce travail est de mettre au point une nouvelle approche automatique pour identifier les réseaux de gènes concourant à une même fonction biologique. Ceci permet une meilleure compréhension des phénomènes biologiques et notamment des processus impliqués dans les maladies telles que les cancers. Différentes stratégies ont été développées pour essayer de regrouper les gènes d’un organisme selon leurs relations fonctionnelles : génétique classique et génétique moléculaire. Ici, nous utilisons une propriété connue des réseaux de gènes fonctionnellement liés à savoir que ces gènes sont généralement co-régulés et donc co-exprimés. Cette co-régulation peut être mise en évidence par des méta-analyses de données de puces à ADN (micro-arrays) telles que Gemma ou COXPRESdb. Dans un travail précédent [Al Adhami et al., 2015], la topologie d’un réseau de co-expression de gènes a été caractérisé en utilisant deux paramètres de description des réseaux qui discriminent des groupes de gènes sélectionnés aléatoirement (modules aléatoires, RM) de groupes de gènes avec des liens fonctionnels connus (modules fonctionnels, FM), c’est-à-dire des gènes appartenant au même processus biologique GO. Dans le présent travail, nous avons cherché à généraliser cette approche et à proposer une méthode, appelée TopoFunc, pour améliorer l’annotation existante de la fonction génique. Nous avons d’abord testé différents descripteurs topologiques du réseau de co-expression pour sélectionner ceux qui identifient le mieux des modules fonctionnels. Puis, nous avons constitué une base de données rassemblant des modules fonctionnels et aléatoires, pour lesquels, sur la base des descripteurs sélectionnés, nous avons construit un modèle de discrimination LDA [Friedman et al., 2001] permettant, pour un sous-ensemble de gènes donné, de prédire son type (fonctionnel ou non). Basée sur la méthode de similarité de gènes travaillée par Wang et ses collègues [Wang et al., 2007], nous avons calculé un score de similarité fonctionnelle entre les gènes d’un module. Nous avons combiné ce score avec celui du modèle LDA dans une fonction de fitness implémenté dans un algorithme génétique (GA). À partir du processus biologique d’ontologie de gènes donné (GO-BP), AG visait à éliminer les gènes faiblement co-exprimés avec la plus grande clique de GO-BP et à ajouter des gènes «améliorant» la topologie et la fonctionnalité du module. Nous avons testé TopoFunc sur 193 GO-BP murins comprenant 50-100 gènes et avons montré que TopoFunc avait agrégé un certain nombre de nouveaux gènes avec le GO-BP initial tout en améliorant la topologie des modules et la similarité fonctionnelle. Ces études peuvent être menées sur plusieurs espèces (homme, souris, rat, et possiblement poulet et poisson zèbre) afin d’identifier des modules fonctionnels conservés au cours de l’évolution. / The aim of this work is to develop a new automatic approach to identify networks of genes involved in the same biological function. This allows a better understanding of the biological phenomena and in particular of the processes involved in diseases such as cancers. Various strategies have been developed to try to cluster genes of an organism according to their functional relationships : classical genetics and molecular genetics. Here we use a well-known property of functionally related genes mainly that these genes are generally co-regulated and therefore co-expressed. This co-regulation can be detected by microarray meta-analyzes databases such as Gemma or COXPRESdb. In a previous work [Al Adhami et al., 2015], the topology of a gene coexpression network was characterized using two description parameters of networks that discriminate randomly selected groups of genes (random modules, RM) from groups of genes with known functional relationship (functional modules, FM), e.g. genes that belong to the same GO Biological Process. We first tested different topological descriptors of the co-expression network to select those that best identify functional modules. Then, we built a database of functional and random modules for which, based on the selected descriptors, we constructed a discrimination model (LDA)[Friedman et al., 2001] allowing, for a given subset of genes, predict its type (functional or not). Based on the similarity method of genes worked by Wang and co-workers [Wang et al., 2007], we calculated a functional similarity score between the genes of a module. We combined this score with that of the LDA model in a fitness function implemented in a genetic algorithm (GA). Starting from a given Gene Ontology Biological Process (GO-BP), AG aimed to eliminate genes that were weakly coexpressed with the largest clique of the GO-BP and to add genes that "improved" the topology and functionality of the module. We tested TopoFunc on the 193 murine GO-BPs comprising 50-100 genes and showed that TopoFunc aggregated a number of novel genes to the initial GO-BP while improving module topology and functional similarity. These studies can be conducted on several species (humans, mice, rats, and possibly chicken and zebrafish) to identify functional modules preserved during evolution.