A árvore de sufixos é uma estrutura dados, que representa em espaço linear todos os fatores de uma palavra, com diversos exemplos de aplicações práticas. Neste trabalho, definimos uma estrutura mais geral: a árvore de Ukkonen. Provamos para ela diversas propriedades combinatórias, dentre quais, a minimalidade em um sentido preciso. Acreditamos que a apresentação aqui oferecida, além de mais geral que as árvores de sufixo, tem a vantagem de oferecer uma descrição explícita da topologia da árvore, de seus vértices, arestas e rótulos, o que não vimos em nenhum outro trabalho. Como aplicações, apresentamos também a árvore esparsa de sufixos (que armazena apenas um subconjunto dos sufixos) e a árvore de k-fatores (que armazena apenas os segmentos de comprimento k, ao invés dos sufixos) definidas como casos particulares das árvores de Ukkonen. Propomos para as árvores esparsas um novo algoritmo de construção com tempo O(n) e espaço O(m), onde n é tamanho da palavra e m é número de sufixos. Para as árvores de k-fatores, propomos um novo algoritmo online com tempo e espaço O(n), onde n é o tamanho da palavra. / The suffix tree is a data structure that represents, in linear space, all factors of a given word, with several examples of practical applications. In this work, we define a more general structure: the Ukkonen\'s tree. We prove many properties for it, among them, its minimality in a precise sense. We believe that this presentation, besides being more general than the suffix trees, has the advantage of offering an explicit description of the tree topology, its vertices, edges and labels, which was not seen in any other work. As applications, we also presents the sparse suffix tree (which stores only a subset of the suffixes) and the k-factor tree (which stores only the substrings of length k, instead of the suffixes), both defined as Ukkonen\'s tree special cases. We propose a new construction algorithm for the sparse suffix trees with time O(n) and space O(m), where n is the size of the word and m is the number of suffixes. For the k-factor trees, we propose a new online algorithm with time and space O(n), where n is the size of the word.

Made available in DSpace on 2015-03-04T18:50:46Z (GMT). No. of bitstreams: 1 DissertacaoMestrado_FernandaNAlmeida.pdf: 2657877 bytes, checksum: df5f53867efd4a6e183687ebd25aa077 (MD5) Previous issue date: 2007-03-26 / Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior / This dissertation offers a computation approach to comparative analysis between cmpletely sequenced genomes of plant-associated bacteria. The created system was denominated ProBacter and it is composed of a relational database and computational tools for sequence analysis. The database was created from a diverse data source, including information from GenBank, TrEMBL, Interpro, COG and GO. The proteins were organized into clusters through the BBH (Bidirectional Best Hits) methodology and categorized according to the functional classification of the Xanthomonas Genome Project. Each entry displayed by the system in a friendly user interface corresponds to an information sheet with the gene and protein sequence, functional category, domain prediction, and related scientific publications, in addition to the group that it belongs, and external links. The system offers a search interface similar to other database systems with pre-formatted queries. For advanced queries, the user has access to an interface that can be used without previous knowledge of the SQL language or ProBacter s database arquiteture. The BLASTP program and two multiple sequence alignment tools, namely ClustalW and T-Coffee, were integrated into the system as well, allowing internal and external sequence comparison. In addition, the system makes available visualization tools capable of displaying the gene position inside a genome and BHH links of clusters. Also, the user is capable of adding new information for each gene in the system. ProBacter s goal is to collect information available from a large source of databases into one computational environment, organize this information and offer comparative tools for sequence analysis. / Esta dissertação resultou na implementação de uma abordagem computacional para a análise comparativa entre informações de genomas completamente seqüenciados de bactérias associadas à planta. O sistema desenvolvido foi denominado de Probacter e é composto de um banco de dados relacional e de ferramentas computacionais para a análise de seqüências, teve por finalidade agrupar as informações disponíveis em vários bancos de dados em um único ambiente, oferecer uma padronização às informações disponibilizadas e fornecer ferramentas para análises comparativas e de seqüências. O banco de dados contém informações provenientes de diversas fontes, incluindo as bases GenBank, Swiss-Prot, TrEMBL, Interpro, COG e GO. As proteínas foram organizadas dentro de grupos, utilizando a metodologia de BBH (Bidirectional Best Hit) e a anotação padronizada de acordo com a classificação funcional anteriormente descrita para o Projeto Genoma de bactérias do gênero Xanthomonas. Cada entrada disponibilizada pelo sistema numa interface amigável corresponde a uma ficha contendo informações sobre o gene e a proteína por ele codificada, incluindo a categorização funcional, a predição de domínios, a seqüência de aminoácidos da proteína, a ligação com os grupos gerados pelo BBH, referências direta a outros bancos de dados, e as publicações científicas. O sistema oferece uma interface de busca comum a bancos de dados, utilizando consultas pré-definidas. Para consultas mais elaboradas, foi desenvolvida uma interface para ser utilizada sem que o usuário tenha conhecimento prévio de linguagens como SQL e/ou da arquitetura desta base. Ferramentas de alinhamento múltiplo ClustalW e T-Coffee e o programa BLASTP também foram integradas a este sistema, permitindo que sejam feitas comparações entre seqüências internas e externas ao banco. O ProBacter integra ferramentas de visualização gráfica, que permite disponibilizar o posicionamento dos genes pertencentes a grupos no genoma de cada organismo e que permite visualizar as ligações durante a formação dos grupos formados pelo BBH. Por fim, um campo aberto é disponibilizado para que seja possível a intervenção de usuários na anotação de novas informações em determinada entrada, sendo as informações novas oferecidas gravadas diretamente no banco de dados.

Submitted by Maria Cristina (library@lncc.br) on 2015-10-13T18:53:31Z No. of bitstreams: 1 Tese_Gregorio_LNCC_Set_2015_FINAL.pdf: 24967973 bytes, checksum: 0efd2d2481063521b74d53264c4be5bb (MD5) / Approved for entry into archive by Maria Cristina (library@lncc.br) on 2015-10-13T18:53:44Z (GMT) No. of bitstreams: 1 Tese_Gregorio_LNCC_Set_2015_FINAL.pdf: 24967973 bytes, checksum: 0efd2d2481063521b74d53264c4be5bb (MD5) / Made available in DSpace on 2015-10-13T18:53:59Z (GMT). No. of bitstreams: 1 Tese_Gregorio_LNCC_Set_2015_FINAL.pdf: 24967973 bytes, checksum: 0efd2d2481063521b74d53264c4be5bb (MD5) Previous issue date: 2015-09-17 / Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro / The protein structure prediciton problem (PSP) consists of discovering the native three-dimensional arrangement of a protein molecule using the information stored in its amino acid sequence. Unveiling the 3D structure of a protein is a way to obtain crucial information about its functions, given that the function of a protein is intrinsically related to its native three-dimensional structure. The experimental determination of the protein structure presents some technical difficulties and is also costly in workload and time. Thus, the investment in computational methods for PSP becomes imminent. This thesis has as main objective to increase the predictive ability of the GAPF protein structure prediction program and contribute to the advancement of theories and methodologies in the free-modeling prediction area. Efforts are directed on two fronts: (i) Improve the modeling of the energy function by the development and Implementing new potential for modeling the problem. (ii) To Increase the conformational search through the development and implementation of a multi-objective genetic algorithm. For the modeling of the problem, they were inserted in the function cost new ad hoc potentials that deal with hydrophobic compactation and with hydrogen bonds, key components in protein folding. For conformational search, a multiobjective steady-state genetic algorithm with phenotypic crowding was proposed. The new methodology was evaluated in a test set of 46 proteins, of all classes, and compared to consolidated methods in the literature, such as quark. The contributions of this thesis provided a major advance in the GAPF's predictive power, increasing the quality of the models and allowing investments in longer sequences. Advances have been notable in beta-sheets predictions, mainly due to the inclusion of hydrogen bonding potentials. Were made available also interesting tools for the future development of the program and GAPF was put as a good candidate for free-modeling predictions against prominent methodologies in the area. / O problema da predição de estrutura de proteínas (PSP) consiste em desvendar o arranjo tridimensional da molécula a partir de sua sequência de aminoácidos. Conhecer a estrutura das proteínas constituintes de um sistema biológico é uma forma de se obter informações cruciais sobre o seu funcionamento, haja vista que a função de uma proteína está intrinsecamente relacionada à sua estrutura nativa tridimensional. A determinação experimental da estrutura de uma proteína além de apresentar dificuldades técnicas, é também dispendiosa em volume de trabalho e de tempo. Sendo assim, o investimento em métodos computacionais para PSP torna-se eminente. Essa tese tem como objetivo geral aumentar a capacidade preditiva do programa de predição de estrutura de proteínas GAPF e contribuir para o avanço das teorias e metodologias na área da predição independente de moldes (free-modeling). Os esforços são direcionados em duas frentes: (i) Melhorar a modelagem da função de energia, através do desenvolvimento e implementação de novos potenciais para a modelagem do problema. (ii) Incrementar a busca conformacional, através do desenvolvimento e implementação de um algoritmo genético multiobjetivo. Para a modelagem do problema, foram inseridos na função custo novos potenciais ad hoc que tratam da compactação hidrofóbica e das ligações de hidrogênio, componentes fundamentais no enovelamento protéico. Para a busca na superfície de energia, um algoritmo genético não-geracional multiobjetivo com crowding fenotípico foi proposto. A nova metodologia foi avaliada em um conjunto teste com 46 proteínas, de todas as classes, e comparada com métodos consolidados na literatura como o QUARK. As contribuições desta tese proporcionaram um grande avanço no poder preditivo do programa GAPF, aumentando a qualidade dos modelos e permitindo investir em sequências maiores. Avanços foram notáveis na predição de folhas-beta, principalmente fruto dos potenciais de ligação de hidrogênio inseridos. Disponibilizou-se, ainda, ferramentas interessantes para o desenvolvimento futuro do programa e colocou o GAPF como um bom candidato para predições independentes de molde frente metodologias de destaque na área.

Convulsões febris complexas durante a infância representam um fator de risco importante para o desenvolvimento da epilepsia. Porém, pouco se sabe sobre as alterações moleculares induzidas por crises febris que tornam o cérebro susceptível à atividade epiléptica. Nesse contexto, modelos experimentais de convulsões induzidas por hipertermia (CH) permitem a análise temporal das alterações moleculares no cérebro após CH. Neste projeto, foram investigadas alterações temporais em redes de co-expressão gênica hipocampais durante o desenvolvimento de ratos Wistar submetidos a CH. Amostras de RNA foram obtidas da região CA3 ventral do hipocampo em quatro intervalos de tempo após as CH induzidas no décimo primeiro dia pós-natal (P11). Essas amostras foram utilizadas para a análise da expressão gênica global por meio de técnicas de microarranjos de DNA. Os pontos temporais foram selecionados para investigar as fases aguda (P12), latentes (P30 e P60) e crônica (P120) do modelo experimental. Os dados de expressão gênica foram analisados a partir da construção de redes de co-expressão gênica para investigar módulos de genes co-expressos, dado que esses módulos podem conter genes com funções semelhantes. A análise transcriptômica consistiu na construção de redes de co-expressão gênica, identificação de módulos, análises de correlação entre módulos e grupos experimentais, e avaliação de mudanças de conectividade entre módulos dos grupos experimentais e controles. Os módulos relevantes foram enriquecidos funcionalmente para identificar funções biológicas associadas às CH. Os resultados mostraram que as CH induzem alterações em vias de sinalização envolvidas em processos imunológicos e de desenvolvimento, tais como Wnt, Hippo, Notch, JAK-STAT e MAPK. Módulos associados à diferenciação neuronal e transmissão sináptica foram identificados em todos os intervalos temporais analisados. Estes resultados sugerem que alterações transcricionais desencadeadas por CH podem levar à neurogênese hipocampal, ao remodelamento tecidual e à inflamação crônica, tornando o cérebro susceptível à atividade epiléptica crônica / Complex febrile seizures during infancy constitute an important risk factor for epilepsy development. However, little is known about the alterations induced by febrile seizures that could turn the brain susceptible to epileptic activity. In this context, experimental models of hyperthermic seizures (HS) may allow the temporal analysis of brain molecular changes after HS. Here, we investigated temporal changes in hippocampal gene co-expression networks during the development of rats subjected to HS. Total RNA samples were obtained from the ventral hippocampal CA3 region at four time points after HS at postnatal day 11 (P11) and later used for gene expression profiling. The temporal endpoints were selected to investigate the acute (P12), latent (P30 and P60) and chronic (P120) stages of the HS model. A weighted gene co-expression network analysis was employed to investigate modules of co-expressed genes, as these modules may contain genes with similar biological functions. The transcriptome analysis pipeline consisted in building gene co-expression networks, identifying network modules and hubs, performing gene-trait correlations and examining module connectivity changes. Modules were functionally enriched to identify functions associated to HS. Our data showed that HS induce alterations in developmental and immune pathways, like Wnt, Hippo, Notch, JAK-STAT and MAPK. Interestingly, modules involved in cell adhesion, neuronal differentiation, axonogenesis and synaptic transmission were activated as early as one day after HS. These results suggest that HS trigger transcriptional alterations that may lead to persistent neurogenesis, tissue remodeling and chronic inflammation in the CA3 hippocampus, turning the brain prone to epileptic activity

Convulsões febris complexas durante a infância representam um fator de risco importante para o desenvolvimento da epilepsia. Porém, pouco se sabe sobre as alterações moleculares induzidas por crises febris que tornam o cérebro susceptível à atividade epiléptica. Nesse contexto, modelos experimentais de convulsões induzidas por hipertermia (CH) permitem a análise temporal das alterações moleculares no cérebro após CH. Neste projeto, foram investigadas alterações temporais em redes de co-expressão gênica hipocampais durante o desenvolvimento de ratos Wistar submetidos a CH. Amostras de RNA foram obtidas da região CA3 ventral do hipocampo em quatro intervalos de tempo após as CH induzidas no décimo primeiro dia pós-natal (P11). Essas amostras foram utilizadas para a análise da expressão gênica global por meio de técnicas de microarranjos de DNA. Os pontos temporais foram selecionados para investigar as fases aguda (P12), latentes (P30 e P60) e crônica (P120) do modelo experimental. Os dados de expressão gênica foram analisados a partir da construção de redes de co-expressão gênica para investigar módulos de genes co-expressos, dado que esses módulos podem conter genes com funções semelhantes. A análise transcriptômica consistiu na construção de redes de co-expressão gênica, identificação de módulos, análises de correlação entre módulos e grupos experimentais, e avaliação de mudanças de conectividade entre módulos dos grupos experimentais e controles. Os módulos relevantes foram enriquecidos funcionalmente para identificar funções biológicas associadas às CH. Os resultados mostraram que as CH induzem alterações em vias de sinalização envolvidas em processos imunológicos e de desenvolvimento, tais como Wnt, Hippo, Notch, JAK-STAT e MAPK. Módulos associados à diferenciação neuronal e transmissão sináptica foram identificados em todos os intervalos temporais analisados. Estes resultados sugerem que alterações transcricionais desencadeadas por CH podem levar à neurogênese hipocampal, ao remodelamento tecidual e à inflamação crônica, tornando o cérebro susceptível à atividade epiléptica crônica / Complex febrile seizures during infancy constitute an important risk factor for epilepsy development. However, little is known about the alterations induced by febrile seizures that could turn the brain susceptible to epileptic activity. In this context, experimental models of hyperthermic seizures (HS) may allow the temporal analysis of brain molecular changes after HS. Here, we investigated temporal changes in hippocampal gene co-expression networks during the development of rats subjected to HS. Total RNA samples were obtained from the ventral hippocampal CA3 region at four time points after HS at postnatal day 11 (P11) and later used for gene expression profiling. The temporal endpoints were selected to investigate the acute (P12), latent (P30 and P60) and chronic (P120) stages of the HS model. A weighted gene co-expression network analysis was employed to investigate modules of co-expressed genes, as these modules may contain genes with similar biological functions. The transcriptome analysis pipeline consisted in building gene co-expression networks, identifying network modules and hubs, performing gene-trait correlations and examining module connectivity changes. Modules were functionally enriched to identify functions associated to HS. Our data showed that HS induce alterations in developmental and immune pathways, like Wnt, Hippo, Notch, JAK-STAT and MAPK. Interestingly, modules involved in cell adhesion, neuronal differentiation, axonogenesis and synaptic transmission were activated as early as one day after HS. These results suggest that HS trigger transcriptional alterations that may lead to persistent neurogenesis, tissue remodeling and chronic inflammation in the CA3 hippocampus, turning the brain prone to epileptic activity

The tumour microenvironment comprises the non-cancerous cells present in the tumour mass (fibroblasts, endothelial, and immune cells), as well as signalling molecules and extracellular matrix. Tumour growth, invasion, metastasis, and response to therapy are influenced by the tumour microenvironment. Therefore, characterising the cellular and molecular components of the tumour microenvironment, and understanding how they influence tumour progression, represent a crucial aim for the success of cancer therapies. High-grade serous ovarian cancer provides an excellent opportunity to systematically study the tumour microenvironment due to its clinical presentation of advanced disseminated disease and debulking surgery being standard of care. This thesis first presents a case report of a long-term survivor (>10 years) of metastatic high-grade serous ovarian cancer who exhibited concomitant regression/progression of the metastatic lesions (5 samples). We found that progressing metastases were characterized by immune cell exclusion, whereas regressing metastases were infiltrated by CD8+ and CD4+ T cells. Through a T cell - neoepitope challenge assay we demonstrated that pre- dicted neoepitopes were recognised by the CD8+ T cells obtained from blood drawn from the patient, suggesting that regressing tumours were subjected to immune attack. Immune excluded tumours presented a higher expression of immunosuppressive Wnt signalling, while infiltrated tumours showed a higher expression of the T cell chemoattractant CXCL9 and evidence of immunoediting. These findings suggest that multiple distinct tumour immune microenvironments can co-exist within a single individual and may explain in part the hetero- geneous fates of metastatic lesions often observed in the clinic post-therapy. Second, this thesis explores the prevalence of intra-patient tumour microenvironment het- erogeneity in high-grade serous ovarian cancer at diagnosis (38 samples from 8 patients), as well as the effect of chemotherapy on the tumour microenvironment (80 paired samples from 40 patients). Whole transcriptome analysis and image-based quantification of T cells from treatment-naive tumours revealed highly prevalent variability in immune signalling and distinct immune microenvironments co-existing within the same individuals at diagnosis. ConsensusTME, a method that generates consensus immune and stromal cell gene signatures by intersecting state-of-the-art deconvolution methods that predict immune cell populations using bulk RNA data was developed. ConsensusTME improved accuracy and sensitivity of T cell and leukocyte deconvolutions in ovarian cancer samples. As previously observed in the case report, Wnt signalling expression positively correlated with immune cell exclusion. To evaluate the effect of chemotherapy on the tumour microenvironment, we compared site-matched and site-unmatched tumours before and after neoadjuvant chemotherapy. Site- matched samples showed increased cytotoxic immune activation and oligoclonal expansion of T cells after chemotherapy, unlike site-unmatched samples where heterogeneity could not be accounted for. In addition, low levels of immune activation pre-chemotherapy were found to be correlated with immune activation upon chemotherapy treatment. These results cor- roborate that the tumour-immune interface in advanced high-grade serous ovarian cancer is intrinsically heterogeneous, and that chemotherapy induces an immunogenic effect mediated by cytotoxic cells. Finally, the different deconvolution methods were benchmarked along with ConsensusTME in a pan-cancer setting by comparing deconvolution scores to DNA-based purity scores, leukocyte methylation data, and tumour infiltrating lymphocyte counts from image analysis. In so far as it has been benchmarked, unlike the other methods, ConsensusTME performs consistently among the top three methods across cancer-related benchmarks. Additionally, ConsensusTME provides a dynamic and evolvable framework that can integrate newer de- convolution tools and benchmark their performance against itself, thus generating an ever updated version. Overall, this thesis presents a systematic characterisation of the tumour microenvironment of high grade serous ovarian cancer in treatment-naive and chemotherapy treated samples, and puts forward the development of an integrative computational method for the systematic analysis of the tumour microenvironment of different tumour types using bulk RNA data.

Recurrent neural networks (RNNs) are state of the art sequential machine learning tools, but have difficulty learning sequences with long-range dependencies due to the exponential growth or decay of gradients backpropagated through the RNN. Some methods overcome this problem by modifying the standard RNN architecure to force the recurrent weight matrix W to remain orthogonal throughout training. The first half of this thesis presents a novel orthogonal RNN architecture that enforces orthogonality of W by parametrizing with a skew-symmetric matrix via the Cayley transform. We present rules for backpropagation through the Cayley transform, show how to deal with the Cayley transform's singularity, and compare its performance on benchmark tasks to other orthogonal RNN architectures. The second half explores two deep learning approaches to problems in RNA secondary structure inference and compares them to a standard structure inference tool, the nearest neighbor thermodynamic model (NNTM). The first uses RNNs to detect paired or unpaired nucleotides in the RNA structure, which are then converted into synthetic auxiliary data that direct NNTM structure predictions. The second method uses recurrent and convolutional networks to directly infer RNA base pairs. In many cases, these approaches improve over NNTM structure predictions by 20-30 percentage points.

Plants are threatened by global change, increasing variability in weather patterns, and associated abiotic stress. Consequently, there is an urgent need to enhance our ability to predict plant community dynamics, shifts in species distributions, and physiological responses to environmental challenges. By building a fundamental understanding of plant stress tolerance, it may be possibly to protect the ecological services, economic industries, and communities that depend on plants. Dehydration tolerance (DhT) is an important mechanism of water stress tolerance with promising translational applications. Here, I take advantage natural variation in DhT to gain a deeper insight into this complex trait. In addition, I address questions related to the causes and consequences of sexual dimorphisms in DhT. Understanding sexual dimorphisms in stress tolerance is critical because these dimorphisms can drive spatial segregation of the sexes, biased sex ratios, and may ultimately reduce sexual reproduction and population persistence. This work takes an integrated approach, addressing DhT on multiple scales from ecology, to physiology, to genomics in the tropical liverwort Marchantia inflexa. Initially, I tested for correlations between DhT and environmental dryness, sex differences in DhT, and genetic vs. plastic contributions to DhT variability. I found that patterns of variation in DhT are associated with environmental variability, including complex sexual dimorphisms, and derive from a combination of plasticity and genetic differences in DhT. Subsequently, I leveraged the variability in DhT to identify candidate DhT enhancing genes. In M. inflexa intraspecific differences in DhT are impacted by baseline variability among plants, as well as unique gene expression responses initiated during drying. In parallel, I assembled a draft genome assembly for M. inflexa, which was employed to investigate questions of sex chromosome evolution and sexual dimorphism in DhT. Finally, the bacteriome of M. inflexa was characterized and found to be extremely diverse and variable. Collectively, this work adds to a growing understanding of DhT and highlights the importance of sampling approaches that seek to comprehensively describe variability in DhT. I detected complex patterns of variability in DhT among populations and the sexes of M. inflexa, which were used to gain insight into the genetic intricacies of DhT.

Le nombre considérable d'interactions protéine-protéine (PPIs) existant au sein d'un organisme, ainsi que leur implication cruciale dans la vie cellulaire et dans de nombreuses pathologies, font des PPIs un immense réservoir de cibles potentielles pour la recherche de médicaments. Les PPIs sont aujourd'hui sur le devant de la scène grâce au développement de méthodologies innovantes et la validation récente de molécules chimiques modulant ces interactions dans des essais précliniques.L'étude des modulateurs d'interactions protéine-protéine (PPIM), a des implications tant dans la recherche fondamentale que thérapeutique. Les PPIMs peuvent aider à la compréhension des réseaux d'interactions. Elles permettront également de faire émerger de nouvelles familles d'agents thérapeutiques actifs dans diverses pathologies.Mon travail de thèse a principalement porté sur deux aspects de l'étude de l'inhibition des PPIs. D'une part, l'étude de l'implication des divers paramètres physicochimiques gouvernant une PPI dans sa capacité à être modulée (étude dite de la « druggabilité »), m'a amené à participer à la création d'une base de données structurale des interactions protéine-protéine : 2P2IDB (http://2p2idb.cnrs-mrs.fr/). D'autre part, j'ai contribué à l analyse de l'espace chimique des molécules présentes dans la base de données 2P2IDB. Nous avons défini la « Rule Of 4 » comme ligne de conduite pour caractériser ces molécules. Nous avons de plus utilisé le SVM afin de créer un protocole innovant (2P2IHUNTER) qui nous a permis de filtrer de grandes collections de composés afin de créer des chimiothèques dédiées aux PPIs. / The number of protein-protein interactions (PPIs) existing in an organism, and their crucial implication in cellular life and in many pathologies, demonstrates the importance of PPIs as a large reservoir of potential targets for medicinal research. Neglected for a long time by both pharmaceutical companies and academic laboratories because they were historically classified as difficult targets, PPIs are now getting into the groove due to the development of innovative methodologies and the growing number of small molecule compounds modulating these interactions.The study of PPI modulators has implications in both fundamental and therapeutics research. On the one hand, PPI modulators can be used in basic research to decipher the role of PPIs in biological networks. On the other hand, they represent a valuable source of new families of therapeutic agents in pathologic processes.In the first part of my PhD, I contributed to the development of a structural database dedicated to protein-protein interactions: 2P2IDB (http://2p2idb.cnrs-mrs.fr/). The interface descriptors of protein-protein interfaces which are typical of complexes present in 2P2IDB have been used to develop a qualitative scoring function to assess the ‘druggability' of PPI targets.In the second part of my PhD, I contributed to the analysis of the chemical space of PPI inhibitors present in the 2P2I database using chemoinformatics tools. We defined the ‘Rule-of-4' as a guideline to characterize these compounds. We have used support vector machine approaches to elaborate a protocol: 2P2IHUNTER, which allows filtering large collection of compounds to design chemical libraries dedicated to PPI targets.

<p>In every living cell, millions of different types of molecules constantly interact and react chemically in a complex system that can adapt to fluctuating environments and extreme conditions, living to survive and reproduce itself. The information required to produce these components is stored in the genome, which is copied in each cell division and transferred and mixed with another genome from parent to child. The regulatory mechanisms that control biological systems, for instance the regulation of expression levels for each gene, has evolved so that global robustness and ability to survive under harsh conditions is a strength, at the same time as biological tasks on a detailed molecular level must be carried out with good precision and without failures. This has resulted in systems that can be described as a hierarchy of levels of complexity: from the lowest level, where molecular mechanisms control other components at the same level, to pathways of coordinated interactions between components, formed to carry out particular biological tasks, and up to large-scale systems consisting of all components, connected in a network with a topology that makes the system robust and flexible. This thesis reports on work that model and analyze complex biological systems, and the signals and noise that regulate them, at all different levels of complexity. Also, it shows how signals are transduced vertically from one level to another, as when a single mutation can cause errors in low level mechanisms, disrupting pathways and create systemwide imbalances, such as in type 2 diabetes. The advancement of our knowledge of biological systems requires both that we go deeper and towards more detail, of single molecules in single cells, as well as taking a step back to understand the organisation and dynamics in the large networks of all components, and unite the different levels of complexity.</p>