Made available in DSpace on 2015-03-04T18:51:19Z (GMT). No. of bitstreams: 1 TeseEsbel.pdf: 1480858 bytes, checksum: c16438606b97781ccf3a3353c4d9f319 (MD5) Previous issue date: 2010-02-28 / Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior / The study of the mechanisms that set off allergic reactions is being a subject of great scientific interest. Anaphylaxis, severe systemic allergic reaction, occupies a prominence place in researches. Different laboratory experiments, in vivo as well as in vitro, and also different mathematical models based on experimental results, tries to investigate if mast cells takes part in those mechanisms or not. However, the obtained results are inconclusive, dividing the scientific community in two groups: one considering that mast cells have a prime role in releasing histamine, and another one which considers that histamine is not the determinative neurotransmitter in the anaphylactic reaction. Previous works proposed differential models to simulate processes related to anaphylactic reactions in the cellular scale for the cell membrane potential generation mechanism. More recently, it has been proposed a probabilistic model, in the tissue scale, to simulate an in vitro antigen response. In the organism level scale, multi-compartimental models have been proposed for the kinetics of histamine in the blood. Nevertheless, no work, until now, has proposed the construction of a model that is able to describe the processes that participate in the mechanism of anaphylactic reaction in different scales. In this work, a model is proposed that integrates the cellular and the tissue scales, allowing to model in vitro experiments, being capable to be extended to the organism scale by the inclusion of the blood flow to model in vivo experiments. The proposed model couples the electric response in the cellular level with the reaction-diffusion of histamine and antigens in the tissue, considering the reaction mechanism mediated by the mast cells. To integrate these two scales, it is proposed here a constitutive relation based on experimental results for the mechanical response (tissue contraction) to electric stimulus. This model allows to design experiments specifically related to the anaphylaxis reaction, indicating the parameters that should be estimated. With this model, numerical simulations have been performed for a wide variation range of the parameters to identify the different domains of the model. A dimensionless parameter based analysis is presented for the obtained results. / O estudo dos mecanismos que desencadeiam as reações alérgicas é um tema de grande interesse científico na atualidade. A anafilaxia, reação alérgica sistêmica severa, tem ocupado um lugar de destaque nas pesquisas. Diferentes experimentos em laboratório, tanto in vivo quanto in vitro, assim como diferentes modelos matemáticos baseados nos resultados experimentais, têm procurado investigar a participação ou não dos mastócitos nesse mecanismo. No entanto, os resultados obtidos não são conclusivos, dividindo a comunidade científica em dois grupos: os que consideram determinante o papel dos mastócitos responsáveis pela liberação de histamina e os que consideram que a histamina não é o neurotransmissor determinante na reação anafilática. Trabalhos anteriores propuseram modelos diferenciais para simular processos relacionados com a reação anafilática na escala celular para o mecanismo de geração de potencial na membrana das células. Mais recentemente foi proposto, a nível de tecido, um modelo probabilístico para simular a resposta in vitro a antígenos. A nível de organismo têm sido propostos modelos de multi compartimentos para a cinética da histamina no fluido sanguíneo. Contudo, nenhum trabalho até o momento abordou a construção de um modelo capaz de descrever os processos que participam no mecanismo de reação anafilática nas diversas escalas. Neste trabalho propomos um modelo que integra as escalas celular e do tecido, que permite modelar experimentos in vitro, e que pode ser estendido para escala do organismo incluindo o fluxo sanguíneo para modelar experimentos in vivo. O modelo proposto integra o mecanismo de resposta elétrica a nível celular com o processo de reação-difusão da histamina e dos antígenos no tecido, considerando o mecanismo de reação mediado por mastócitos. Para integrar as duas escalas propomos uma relação constitutiva baseada em resultados experimentais da resposta mecânica (contração do tecido) a estímulos elétricos. Este modelo permite o desenho de novos experimentos especificamente direcionados ao estudo da reação anafilática, indicando os parâmetros a serem estimados. Utilizando-se o modelo proposto, foram realizadas simulações numéricas para uma ampla faixa de variação dos parâmetros visando identificar domínios com diferentes comportamentos do modelo. Uma análise dos resultados obtidos baseada em parâmetros adimensionais é apresentada.

Les Fonctions biologiques sont le résultat de l'interaction de beaucoup de processus, avec differents objectives, complexités, niveaux d'hiérarchie, et changements de conditions que modi ent le comportement de systèmes. Nous utilisons des équations diferenciales ou dynamiques plus générales, et Stochastic Systèmes de Transition pour décrire la dynamique de changements des modèles. La composition, réconciliation et reutilisation des modèles nous permettent d'obtenir des descriptions de systèmes biologiques complètes et compatibles et leur combiner. Notre spéci cation de Systèmes Hybrides avec BioRica assures l'intégrité de modèles, et implement notre approche. Nous appliquons notre approche pour décrire in-silico deux systèmes: la dynamique de la fermentation du vin, et des décisions cellulaires associées à la formation de tissu d'os.

O estudo dos mecanismos que desencadeiam as reações alérgicas é um tema de grande interesse científico na atualidade. A anafilaxia, reação alérgica sistêmica severa, tem ocupado um lugar de destaque nas pesquisas. Diferentes experimentos em laboratório, tanto in vivo quanto in vitro, assim como diferentes modelos matemáticos baseados nos resultados experimentais, têm procurado investigar a participação ou não dos mastócitos nesse mecanismo. No entanto, os resultados obtidos não são conclusivos, dividindo a comunidade científica em dois grupos: os que consideram determinante o papel dos mastócitos responsáveis pela liberação de histamina e os que consideram que a histamina não é o neurotransmissor determinante na reação anafilática. Trabalhos anteriores propuseram modelos diferenciais para simular processos relacionados com a reação anafilática na escala celular para o mecanismo de geração de potencial na membrana das células. Mais recentemente foi proposto, a nível de tecido, um modelo probabilístico para simular a resposta in vitro a antígenos. A nível de organismo têm sido propostos modelos de multi compartimentos para a cinética da histamina no fluido sanguíneo. Contudo, nenhum trabalho até o momento abordou a construção de um modelo capaz de descrever os processos que participam no mecanismo de reação anafilática nas diversas escalas. Neste trabalho propomos um modelo que integra as escalas celular e do tecido, que permite modelar experimentos in vitro, e que pode ser estendido para escala do organismo incluindo o fluxo sanguíneo para modelar experimentos in vivo. O modelo proposto integra o mecanismo de resposta elétrica a nível celular com o processo de reação-difusão da histamina e dos antígenos no tecido, considerando o mecanismo de reação mediado por mastócitos. Para integrar as duas escalas propomos uma relação constitutiva baseada em resultados experimentais da resposta mecânica (contração do tecido) a estímulos elétricos. Este modelo permite o desenho de novos experimentos especificamente direcionados ao estudo da reação anafilática, indicando os parâmetros a serem estimados. Utilizando-se o modelo proposto, foram realizadas simulações numéricas para uma ampla faixa de variação dos parâmetros visando identificar domínios com diferentes comportamentos do modelo. Uma análise dos resultados obtidos baseada em parâmetros adimensionais é apresentada. / The study of the mechanisms that set off allergic reactions is being a subject of great scientific interest. Anaphylaxis, severe systemic allergic reaction, occupies a prominence place in researches. Different laboratory experiments, in vivo as well as in vitro, and also different mathematical models based on experimental results, tries to investigate if mast cells takes part in those mechanisms or not. However, the obtained results are inconclusive, dividing the scientific community in two groups: one considering that mast cells have a prime role in releasing histamine, and another one which considers that histamine is not the determinative neurotransmitter in the anaphylactic reaction. Previous works proposed differential models to simulate processes related to anaphylactic reactions in the cellular scale for the cell membrane potential generation mechanism. More recently, it has been proposed a probabilistic model, in the tissue scale, to simulate an in vitro antigen response. In the organism level scale, multi-compartimental models have been proposed for the kinetics of histamine in the blood. Nevertheless, no work, until now, has proposed the construction of a model that is able to describe the processes that participate in the mechanism of anaphylactic reaction in different scales. In this work, a model is proposed that integrates the cellular and the tissue scales, allowing to model in vitro experiments, being capable to be extended to the organism scale by the inclusion of the blood flow to model in vivo experiments. The proposed model couples the electric response in the cellular level with the reaction-diffusion of histamine and antigens in the tissue, considering the reaction mechanism mediated by the mast cells. To integrate these two scales, it is proposed here a constitutive relation based on experimental results for the mechanical response (tissue contraction) to electric stimulus. This model allows to design experiments specifically related to the anaphylaxis reaction, indicating the parameters that should be estimated. With this model, numerical simulations have been performed for a wide variation range of the parameters to identify the different domains of the model. A dimensionless parameter based analysis is presented for the obtained results.

Living cells and their components play a key role within biotechnology industry. Cell cultures and their products of interest are used for the design of vaccines as well as in the agro-alimentary field. In order to ensure optimal working of such bioprocesses, the understanding of the complex mechanisms which rule them is fundamental. Mathematical models may be helpful to grasp the biological phenomena which intervene in a bioprocess. Moreover, they allow prediction of system behaviour and are frequently used within engineering tools to ensure, for instance, product quality and reproducibility.<p> <p>Mathematical models of cell cultures may come in various shapes and be phrased with varying degrees of mathematical formalism. Typically, three main model classes are available to describe the nonlinear dynamic behaviour of such biological systems. They consist of macroscopic models which only describe the main phenomena appearing in a culture. Indeed, a high model complexity may lead to long numerical computation time incompatible with engineering tools like software sensors or controllers. The first model class is composed of the first principles or white box models. They consist of the system of mass balances for the main species (biomass, substrates, and products of interest) involved in a reaction scheme, i.e. a set of irreversible reactions which represent the main biological phenomena occurring in the considered culture. Whereas transport phenomena inside and outside the cell culture are often well known, the reaction scheme and associated kinetics are usually a priori unknown, and require special care for their modelling and identification. The second kind of commonly used models belongs to black box modelling. Black boxes consider the system to be modelled in terms of its input and output characteristics. They consist of mathematical function combinations which do not allow any physical interpretation. They are usually used when no a priori information about the system is available. Finally, hybrid or grey box modelling combines the principles of white and black box models. Typically, a hybrid model uses the available prior knowledge while the reaction scheme and/or the kinetics are replaced by a black box, an Artificial Neural Network for instance.<p><p>Among these numerous models, which one has to be used to obtain the best possible representation of a bioprocess? We attempt to answer this question in the first part of this work. On the basis of two simulated bioprocesses and a real experimental one, two model kinds are analysed. First principles models whose reaction scheme and kinetics can be determined thanks to systematic procedures are compared with hybrid model structures where neural networks are used to describe the kinetics or the whole reaction term (i.e. kinetics and reaction scheme). The most common artificial neural networks, the MultiLayer Perceptron and the Radial Basis Function network, are tested. In this work, pure black box modelling is however not considered. Indeed, numerous papers already compare different neural networks with hybrid models. The results of these previous studies converge to the same conclusion: hybrid models, which combine the available prior knowledge with the neural network nonlinear mapping capabilities, provide better results.<p><p>From this model comparison and the fact that a physical kinetic model structure may be viewed as a combination of basis functions such as a neural network, kinetic model structures allowing biological interpretation should be preferred. This is why the second part of this work is dedicated to the improvement of the general kinetic model structure used in the previous study. Indeed, in spite of its good performance (largely due to the associated systematic identification procedure), this kinetic model which represents activation and/or inhibition effects by every culture component suffers from some limitations: it does not explicitely address saturation by a culture component. The structure models this kind of behaviour by an inhibition which compensates a strong activation. Note that the generalization of this kinetic model is a challenging task as physical interpretation has to be improved while a systematic identification procedure has to be maintained.<p><p>The last part of this work is devoted to another kind of biological systems: proteins. Such macromolecules, which are essential parts of all living organisms and consist of combinations of only 20 different basis molecules called amino acids, are currently used in the industrial world. In order to allow their functioning in non-physiological conditions, industrials are open to modify protein amino acid sequence. However, substitutions of an amino acid by another involve thermodynamic stability changes which may lead to the loss of the biological protein functionality. Among several theoretical methods predicting stability changes caused by mutations, the PoPMuSiC (Prediction Of Proteins Mutations Stability Changes) program has been developed within the Genomic and Structural Bioinformatics Group of the Université Libre de Bruxelles. This software allows to predict, in silico, changes in thermodynamic stability of a given protein under all possible single-site mutations, either in the whole sequence or in a region specified by the user. However, PoPMuSiC suffers from limitations and should be improved thanks to recently developed techniques of protein stability evaluation like the statistical mean force potentials of Dehouck et al. (2006). Our work proposes to enhance the performances of PoPMuSiC by the combination of the new energy functions of Dehouck et al. (2006) and the well known artificial neural networks, MultiLayer Perceptron or Radial Basis Function network. This time, we attempt to obtain models physically interpretable thanks to an appropriate use of the neural networks.<p> / Doctorat en sciences appliquées / info:eu-repo/semantics/nonPublished

Les fonctions biologiques sont le résultat de l'interaction de beaucoup de processus, avec différents objectifs, complexités, niveaux de hiérarchie, et changements de conditions que modifient le comportement de systèmes. Nous utilisons des équations différentielles ou dynamiques plus générales, et systèmes stochastiques de transition pour décrire la dynamique de changements des modèles. La composition, réconciliation et réutilisation des modèles nous permettent d'obtenir des descriptions de systèmes biologiques complètes et compatibles et leur combiner. Notre spécification de systèmes hybrides avec BioRica assure l'intégrité de modèles, et implémente notre approche. Nous appliquons notre approche pour décrire in-silico deux systèmes: la dynamique de la fermentation du vin, et des décisions cellulaires associées à la formation de tissu d'os. / The main aim of this thesis is to develop an approach that allows us to describe biological systems with theoretical sustenance and good results in practice. Biological functions are the result of the interaction of many processes, that connect different hierarchy levels going from macroscopic to microscopic level. Each process works in different way, with its own goal, complexity and hierarchy level. In addition, it is common to observe that changes in the conditions, such as nutrients or environment, modify the behavior of the systems. So, to describe the behavior of a biological system over time, it is convenient to combine different types of models: continuous models for gradual changes, discrete models for instantaneous changes, deterministic models for completely predictable behaviors, and stochastic or non- deterministic models to describe behaviors with imprecise or incomplete information. In this thesis we use the theory of Composition and Hybrid Systems as basis, and the BioRica framework as tool to model biological systems and analyze their emergent properties in silico.With respect to Hybrid Systems, we considered continuous models given by sets of differential equations or more general dynamics. We used Stochastic Transition Systems to describe the dynamics of model changes, allowing cofficient switches that control the parameters of the continuous model, and strong switches that choose different models. Composition, reconciliation and reusing of models allow us to build complete and consistent descriptions of complex biological systems by combining them. Compositions of hybrid systems are hybrid systems, and the refinement of a model forming part of a composed system results in a refinement of the composed system. To implement our approach ideas we complemented the theory of our approach with the improving of the BioRica framework. We contributed to do that giving a BioRica specification of Hybrid Systems that assures integrity of models, allowing composition, reconciliation, and reuse of models with SBML specification.We applied our approach to describe two systems: wine fermentation kinetics, and cell fate decisions leading to bone and fat formation. In the case of wine fermentation, we reused known models that describe the responses of yeasts cells to different temperatures, quantities of resources and toxins, and we reconciled these models choosing the model with best adjustment to experimental data depending on the initial conditions and fermentation variable. The resulting model can be applied to avoid process problems as stuck and sluggish fermentations. With respect to cell fate decisions the idea is very ambitious. By using accurate models to predict the bone and fat formation in response to activation of pathways such as the Wnt pathway, and changes of conditions affecting these functions such as increments in Homocysteine, one can analyze the responses to treatments for osteoporosis and other bone mass disorders. We think that here we are giving a first step to obtain in silico evaluations of medical treatments before testing them in vitro or in vivo.

Esta pesquisa de doutorado apresenta resultados em três áreas distintas: (i) Ciência da Computação e Estatística -- devido ao desenvolvimento de uma nova solução para o problema de seleção de características, um problema conhecido em Reconhecimento de Padrões; (ii) Bioinformática -- em razão da construção de um método baseado em um \\textit de algoritmos, incluindo o de seleção de características, visando abordar o problema de identificação de arquiteturas de redes de expressão gênica; e (iii) Biologia -- ao relacionar o estrógeno com uma nova função biológica, após analisar informações extraídas de séries temporais de \\textit pelas novas ferramentas computacionais-estatísticas desenvolvidas. O estrógeno possui um importante papel nos tecidos reprodutivos. O crescimento das gândulas mamárias e do endométrio durante a gravidez e o ciclo menstrual são estrógeno dependentes. O crescimento das células tumorais nesses órgãos podem ser estimuladas pela simples presença de estrógeno; mais de $300$ genes são conhecidos por terem regulação positiva ou negativa devido a sua presença. A motivação inicial desta pesquisa foi a construção de um método que possa servir de ferramenta para a identificação de genes que tenham seu nível de expressão alterado a partir de uma resposta induzida por estrógeno, mais precisamente, um método para modelar os inter-relacionamentos entre os diversos genes dependentes do estrógeno. Apresentamos um novo \\textit de algoritmos que, a partir de dados temporais de \\textit e um conjunto inicial de genes que compartilham algumas características comuns, denominados de \\textit{genes sementes}, devolve como saída a arquitetura de uma rede gênica representada por um grafo dirigido. Para cada nó da rede, uma tabela de predição do gene representado pelo nó em função dos seus genes preditores (genes que apontam para ele) pode ser obtida. O método foi aplicado em estudo de série-temporal de \\textit para uma cultura de células \\textit submetidas a tratamento com estrógeno, e uma possível rede de regulação foi obtida. Encontrar o melhor subconjunto preditor de genes para um dado gene pode ser estudado como um problema de seleção de características, no qual o espaço de busca pode ser representado por um reticulado Booleano e cada um de seus elementos representa um subconjunto candidato. Uma característica importante desse problema é o fato de que para cada elemento existe uma função custo associada, e esta possui forma de curva em U para qualquer cadeia maximal do reticulado. Para esse problema, apresentamos um nova solução, o algoritmo ewindex. Esse algoritmo é um método do tipo \\textit, o qual utiliza a estrutura do reticulado Booleano e a característica de curva em U da função custo para explorar um subconjunto do espaço de busca equivalente à busca completa. Nosso método obteve excelentes resultados em eficiência e valores quando comparado com as heurísticas mais utilizadas (SFFS e SFS). A partir de um método baseado no \\textit e de um conjunto inicial de genes regulados \\textit pelo estrógeno, identificamos uma evidência de envolvimento do estrógeno em um processo biológico ainda não relacionado: a adesão celular. Esse resultado pode direcionar os estudos sobre estrógeno e câncer à investigação de processo metastático, o qual é influenciado por genes relacionados à adesão celular. / This Phd. research presents in three distinct areas: (i) Computer Science and Statistics -- on the development of a new solution for the feature selection problem which is an important problem in Pattern Recognition; (ii) Bioinformatics -- for the construction of a pipeline of algorithms, including the feature selection solution, to address the problem of identification the architecture of a genetic expression network and; (iii) Biology -- relating estrogen to a new biological function, from the results obtained by the new computational-statistic tools developed and applied to a time-series microarray data. Estrogen has an important role in reproductive tissues. The growth mammary glands and endometrial growing during menstrual cycle and pregnancy are estrogen dependent. The growth of tumor cells in those organs can be stimulated by the simple presence of estrogen. Over $300$ genes are known by their positive or negative regulation by estrogen. The initial motivation of this research was the construction of a method that can serve as a tool for the identification of genes that have changed their level of expression changed by a response induced by estrogen, more specifically, a method to model the inter-relationships between the several genes dependent on estrogen. We present a new pipeline of algorithms that from the data of a time-series microarray experiment and from an initial set of genes that share some common characteristics, known as \\textit{seed genes}, gives as an output an architecture of the genetic expression network represented by a directed graph. For each node of the network, a prediction table of the gene, represented by the node, in function of its predictors genes (genes that link to it) can be obtained. The method was applied in a study of time-series microarray for a cell line \\textit submitted to a estrogen treatment and a possible regulation network was obtained. Finding the best predictor subset of genes for a given gene can be studied as a problem of feature selection where the search space can be represented by a Boolean lattice and each one of its elements represents a possible subset. An important characteristic of this problem is: for each element in the lattice there is a cost function associated to it and this function has a U-shape in any maximal chain of the search space. For this problem we present a new solution, the \\textit algorithm. This algorithm is a branch-and-bound solution which uses the structure of the Boolean lattice and U-shaped curves to explore a subset of the search space that is equivalent to the full search. Our method obtained excellent results in performance and values when compared with the most commonly used heuristics (SFFS and SFS). From a method based on the pipeline of algorithms and from an initial set of genes direct regulated by estrogen, we identified an evidence of involvement of estrogen in a biological process not yet related to estrogen: the cell adhesion. This result can guide studies on estrogen and cancer to research in metastatic process, which is affected by cell adhesion related genes.

Indiana University-Purdue University Indianapolis (IUPUI) / In systems biology study, biological networks were used to gain insights into biological systems. While the traditional approach to studying biological networks is based on the identification of interactions among genes or the identification of a gene set ranking according to differentially expressed gene lists, little is known about interactions between higher order biological systems, a network of gene sets. Several types of gene set network have been proposed including co-membership, linkage, and co-enrichment human gene set networks. However, to our knowledge, none of them contains directionality information. Therefore, in this study we proposed a method to construct a regulatory gene set network, a directed network, which reveals novel relationships among gene sets. A regulatory gene set network was constructed by using publicly available gene regulation data. A directed edge in regulatory gene set networks represents a regulatory relationship from one gene set to the other gene set. A regulatory gene set network was compared with another type of gene set network to show that the regulatory network provides additional information. In order to show that a regulatory gene set network is useful for understand the underlying mechanism of a disease, an Alzheimer's disease (AD) regulatory gene set network was constructed. In addition, we developed Pathway and Annotated Gene-set Electronic Repository (PAGER), an online systems biology tool for constructing and visualizing gene and gene set networks from multiple gene set collections. PAGER is available at http://discern.uits.iu.edu:8340/PAGER/. Global regulatory and global co-membership gene set networks were pre-computed. PAGER contains 166,489 gene sets, 92,108,741 co-membership edges, 697,221,810 regulatory edges, 44,188 genes, 651,586 unique gene regulations, and 650,160 unique gene interactions. PAGER provided several unique features including constructing regulatory gene set networks, generating expanded gene set networks, and constructing gene networks within a gene set. However, tissue specific or disease specific information was not considered in the disease specific network constructing process, so it might not have high accuracy of presenting the high level relationship among gene sets in the disease context. Therefore, our framework can be improved by collecting higher resolution data, such as tissue specific and disease specific gene regulations and gene sets. In addition, experimental gene expression data can be applied to add more information to the gene set network. For the current version of PAGER, the size of gene and gene set networks are limited to 100 nodes due to browser memory constraint. Our future plans is integrating internal gene or proteins interactions inside pathways in order to support future systems biology study.

In recent biomedical research, graph representation learning and cell profiling techniques have emerged as transformative tools for analyzing high-dimensional biological data. The integration of these methods, as investigated in this study, has facilitated an enhanced understanding of complex biological systems, consequently improving drug discovery. The research aimed to decipher connections between chemical structures and cellular phenotypes while incorporating other biological information like proteins and pathways into the workflow. To achieve this, machine learning models' efficacy was examined for classification and regression tasks. The newly proposed graph-level and bio-graph integrative predictors were compared with traditional models. Results demonstrated their potential, particularly in classification tasks. Moreover, the topology of the COVID-19 BioGraph was analyzed, revealing the complex interconnections between chemicals, proteins, and biological pathways. By combining network analysis, graph representation learning, and statistical methods, the study was able to predict active chemical combinations within inactive compounds, thereby exhibiting significant potential for further investigations. Graph-based generative models were also used for molecule generation opening up further research avenues in finding lead compounds. In conclusion, this study underlines the potential of combining graph representation learning and cell profiling techniques in advancing biomedical research in drug repurposing and drug combination. This integration provides a better understanding of complex biological systems, assists in identifying therapeutic targets, and contributes to optimizing molecule generation for drug discovery. Future investigations should optimize these models and validate the drug combination discovery approach. As these techniques continue to evolve, they hold the potential to significantly impact the future of drug screening, drug repurposing, and drug combinations.

La RMN du solide a récemment émergé en tant que technique très puissante en biologie structurale, permettant de caractériser au niveau atomique des systèmes qui ne peuvent être étudiés par d’autres méthodes. Des protocoles spécifiques à la RMN du solide sont à présent bien établis pour la préparation des échantillons, l’attribution des spectres et l’acquisition de contraintes structurales. Ensemble, ces protocoles ont ouvert la voie aux premières déterminations de structures tridimensionnelles de molécules biologiques à l’état solide avec une résolution atomique, et ce non seulement pour des échantillons protéiques microcristallins, mais également pour des systèmes plus complexes tels que des fibrilles ou des protéines membranaires.La détermination structurale de tels systèmes est cependant encore loin d’être une routine, et des avancées de plus large ampleur sont attendues grâce à des développements aux niveaux méthodologique et matériel. Pour cette raison, une majeure partie du travail présenté dans cette thèse a été consacrée au développement d’expériences à la fois nouvelles et sophistiquées pour améliorer la sensibilité et la résolution des méthodes déjà existantes pour attribuer les spectres et élargir les possibilités offertes par la RMN du solide en vue d’étudier la structure de systèmes protéiques plus larges. Ces développements reposent notamment sur l’utilisation de champs magnétiques très intenses et sur la rotation des échantillons à l’angle magique dans la gamme des très hautes vitesses angulaires. Nous montrons que dans ce cadre, il est possible de concevoir des expériences utilisant uniquement des champs radiofréquences à faible puissance ainsi que d’utiliser des transferts sélectifs, l’acquisition de corrélations à travers les liaisons chimiques et la détection proton.En particulier, nous montrons que des expériences de corrélation homonucléaire reposant sur des transferts scalaires deviennent une alternative compétitive aux expériences basées sur des transferts dipolaires. Deux nouvelles séquences d’impulsion permettant de détecter des corrélations 13C-13C à travers les liaisons chimiques avec une excellente résolution sont présentées; couplées à des transferts 15N-13C, elles permettent l’attribution des résonances de la chaîne principale des protéines avec une grande sensibilité.De plus, nous démontrons qu’il est possible d’obtenir des raies très fines pour les résonances de protons dans des protéines complètement protonées à l’état solide grâce à la rotation à l’angle magique à ultra-haute vitesse, sans avoir recours à la deutération. Dans ce contexte, nous avons développé de nouvelles stratégies permettant d’attribuer rapidement et de façon fiable les résonances des spins 1H, 15N, 13CO, 13CA et 13CB dans différentes classes de protéines, ainsi que pour mesurer des contraintes structurales à partir des distances entre protons. L’approche proposée repose sur la haute sensibilité des protons et accélère donc considérablement les processus d’attribution et de détermination structurale des protéines à l’état solide, comme illustré sur la protéine beta-2-microglobuline.Enfin, nous avons appliqué cette nouvelle approche pour réaliser l’attribution et l’étude structurale et fonctionnelle de trois catégories de complexes protéiques: les fibrilles amyloidogènes formées par beta-2-microglobuline, les nucléocapsides du virus de la rougeole, et les nucléocapsides d’Acinetobacter phage205. Nous avons également utilisé la technique de Polarisation Nucléaire Dynamique pour obtenir des informations sur l’ARN des nucléocapsides du virus de la rougeole.Nous considérons que les résultats présentés dans cette thèse représentent une avancée substantielle dans le domaine de la RMN du solide appliquée à la biologie structurale. Grâce aux progrès actuels dans ce domaine, l’impact de la RMN biomoléculaire à l’état solide promet d’augmenter dans les prochaines années. / Solid-state NMR has recently emerged as a key technique in modern structural biology, by providing information at atomic level for the characterization of a wide range of systems that cannot be investigated by other atomic-scale methods. There are now well established protocols for sample preparation, resonance assignment and collection of structural restraints, that have paved the way to the first three-dimensional structure determinations at atomic resolution of biomolecules in the solid state, from microcrystalline samples to fibrils and membrane-associated systems. These determinations are however still far from being routine, and larger breakthroughs are expected with further methodological and hardware developments. Accordingly, most of the work presented in this thesis consists of the development of new, sophisticated NMR experiments to improve the sensitivity and resolution of the currently existing schemes for resonance assignment and to extend the capabilities of solid-state NMR in terms of structural investigation of proteins for the analysis of large substrates. These developments notably rely on the use of very high magnetic fields and ultra-fast magic-angle spinning (MAS). We show the great potential of this particular regime, which enables the use of low-power experiments and the acquisition of selective cross-polarization transfers, through-bond correlations and 1H-detected correlations.In particular, we show that homonuclear correlation experiments based on through-bond transfers become competitive alternatives to dipolar transfer schemes. Two new pulse sequences that detect sensitive and resolved 13C-13C through-bond correlations are introduced, which coupled to 15N-13C dipolar transfer steps provide sensitive routes for protein backbone resonance assignment.Furthermore, we demonstrate that narrow 1H NMR line widths can be obtained for fully protonated proteins in the solid state under ultra-fast MAS, even without perdeuteration. In this context, we have developed new strategies for extensive, robust and expeditious assignments of the 1H, 15N, 13CO, 13CA and 13CB resonances of proteins in different aggregation states, and new schemes for the measurements of site-specific 1H-1H distance restraints. This approach relying on the very high sensitivity of 1H spins remarkably accelerates the processes of assignment and structure determination of proteins in the solid state, as shown by the assignment and de novo structure determination of native beta-2-microglobulin. Finally, we apply this new approach to perform resonance assignment and to study structural and dynamic features of three complex protein aggregates: amyloid fibrils formed by native and D76N beta-2-microglobulin, Acinetobacter phage 205 nucleocapsids and measles virus (MeV) nucleocapsids. We also used Dynamic Nuclear Polarization to obtain the first information about RNA in MeV nucleocapsids.We believe that the results presented in this thesis represent a substantial step forward for solid-state NMR in structural biology. With all the current advances in the field, the impact of biomolecular solid-state NMR is likely to increase in the next years.

Lors de l’infection par un pathogène intracellulaire, l’organisme déclenche une réponse immunitaire spécifique dont les acteurs principaux sont les lymphocytes T-CD8. Ces cellules sont responsables de l’éradication de ce type d’infections et de la constitution du répertoire immunitaire de l’individu. Les processus qui composent la réponse immunitaire se répartissent sur plusieurs échelles physiques inter-connectées (échelle intracellulaire, échelle d’une cellule, échelle de la population de cellules). La réponse immunitaire est donc un processus complexe, pour lequel il est difficile d’observer ou de mesurer les liens entre les différents phénomènes mis en jeu. Nous proposons trois modèles mathématiques multi-échelles de la réponse immunitaire, construits avec des formalismes différents mais liés par une même idée : faire dépendre le comportement des cellules TCD8 de leur contenu intracellulaire. Pour chaque modèle, nous présentons, si possible, sa construction à partir des hypothèses biologiques sélectionnées, son étude mathématique et la capacité du modèle à reproduire la réponse immunitaire au travers de simulations numériques. Les modèles que nous proposons reproduisent qualitativement et quantitativement la réponse immunitaire T-CD8 et constituent ainsi de bons outils préliminaires pour la compréhension de ce phénomène biologique. / Upon infection by an intracellular pathogen, the organism triggers a specific immune response,mainly driven by the CD8 T cells. These cells are responsible for the eradication of this type of infections and the constitution of the immune repertoire of the individual. The immune response is constituted by many processes which act over several interconnected physical scales (intracellular scale, single cell scale, cell population scale). This biological phenomenon is therefore a complex process, for which it is difficult to observe or measure the links between the different processes involved. We propose three multiscale mathematical models of the CD8 immune response, built with different formalisms but related by the same idea : to make the behavior of the CD8 T cells depend on their intracellular content. For each model, we present, if possible, its construction process based on selected biological hypothesis, its mathematical study and its ability to reproduce the immune response using numerical simulations. The models we propose succesfully reproduce qualitatively and quantitatively the CD8 immune response and thus constitute useful tools to further investigate this biological phenomenon.