La mégacaryopoïèse représente le processus de différenciation des cellules souches hématopoïétiques (CSH) en mégacaryocytes (MK). Ce processus précède la thrombopoïèse qui aboutira à la formation des plaquettes sanguines. Ces processus complexes ont lieu 1) au sein de la structure tridimensionnelle (3D) de la moelle osseuse, 2) dans les vaisseaux sinusoïdes de la moelle et 3) dans la circulation sanguine. Le but général de ce travail a été de comprendre le mécanisme de chaque étape. Le premier objectif a été d’étudier les effets d’une structure poreuse 3D mimant celle de la moelle osseuse, sur la différenciation mégacaryocytaire et la production plaquettaire in vitro. Cette étude a permis de démontrer que la synergie entre l’organisation spatiale et les signaux du microenvironnement améliore la production en MK et en plaquettes. Par la suite, nous avons souhaité caractériser in vitro et in vivo les plaquettes produites en conditions de flux. Nous avons notamment mis en évidence la capacité des plaquettes produites in vitro dans un système de microfluidique, à s’incorporer et à participer à la formation d’un thrombus in vitro et in vivo contrairement aux plaquettes obtenues en statique. Ces travaux prouvent donc l’intérêt d’une part, de mimer le microenvironnement de la moelle osseuse et d’autre part, de reproduire les forces de cisaillement du sang afin d’améliorer et d’augmenter la production de plaquettes in vitro pour de futures applications en thérapeutique. / Megakaryopoiesis is a process allowing hematopoietic stem cell (HSC) to proliferate and differentiate into megakaryocytes (MK). It is followed by thrombopoiesis allowing blood platelet production. These processes occur 1) in the bone marrow three-dimensional (3D) structure, 2) in the bone marrow sinusoid vessels and 3) in the blood flow. Our general aim was to decipher the mechanism associated to each process. The first objective was to study the effects of porous 3D structure on MK differentiation and platelet production. This study demonstrated that the synergy between spatial organization and biological cues improved MK and platelet production. We also characterized platelets produced from mature MK in flow conditions, with respect to their in vitro and in vivo properties. We highlighted the capacity of flow-derived platelets to incorporate in a thrombus in vitro and in vivo, compared to static-derived platelets. These works represent some new developments for mimicking the bone marrow structure and to reproduce blood shear forces in order to improve and increase in vitro platelet production for therapeutic use.

Interactions between proteins give rise to many functions in cells. In the lastdecade, highthroughput experiments have identified thousands of protein interactions, which are often represented together as large protein interaction networks. However, the classical way of representing interaction networks, as nodes and edges, is too limited to take dynamic properties such as compatible and mutually exclusive interactions into account. In this work, we study protein interaction networks using structural information. More specifically, the analysis of protein interfaces in threedimensional protein structures enables us to identify which interfaces are compatible and which are not. Based on this principle, we have implemented a method, which aims at the analysis of protein interaction networks from a structural point of view by (1) predicting possible binary interactions for proteins that have been found in complex experimentally and (2) identifying possible mutually exclusive and compatible complexes. We validated our method by using positive and negative reference sets from literature and set up an assay to benchmark the identification of compatible and mutually exclusive structural interactions. In addition, we reconstructed the protein interaction network associated with the G proteincoupled receptor Rhodopsin and defined related functional submodules by combining interaction data with structural analysis of the network. Besides its established role in vision, our results suggest that Rhodopsin triggers two additional signaling pathways towards (1) cytoskeleton dynamics and (2) vesicular trafficking. / Las funciones de las proteínas resultan de la manera con la que interaccionan entre ellas. Los experimentos de alto rendimiento han permitido identificar miles de interacciones de proteínas que forman parte de redes grandes y complejas. En esta tesis, utilizamos la información de estructuras de proteínas para estudiar las redes de interacciones de proteínas. Con esta información, se puede entender como las proteínas interaccionan al nivel molecular y con este conocimiento se puede identificar las interacciones que pueden ocurrir al mismo tiempo de las que están incompatibles. En base a este principio, hemos desarrollado un método que permite estudiar las redes de interacciones de proteínas con un punto de vista mas dinámico de lo que ofrecen clásicamente. Además, al combinar este método con minería de la literatura y Los datos de la proteomica hemos construido la red de interacciones de proteínas asociada con la Rodopsina, un receptor acoplado a proteínas G y hemos identificado sus sub--‐módulos funcionales. Estos análisis surgieron una novel vıa de señalización hacia la regulación del citoesqueleto y el trafico vesicular por Rodopsina, además de su papel establecido en la visión.

[pt] As estruturas civis durante toda a sua vida útil estão sujeitas a diversas ações de deterioração, desgastes ou corrosão de seus membros, que podem gerar variações em suas características físicas. Estas ações podem causar danos ao seu funcionamento, podendo chegar até ao colapso, em casos mais extremos. Além disso, o avanço tecnológico que permite a concepção de estruturas cada vez mais esbeltas, e que geram assim possíveis vibrações excessivas, elevam o monitoramento estrutural a um patamar de extrema importância e atenção na ótica dos gestores desses sistemas. Particularmente, no caso de obras de infraestrutura como pontes e passarelas, as grandes dimensões são características significativas que tornam as práticas de monitoramento e inspeção mais difíceis. Dessa forma, com o objetivo auxiliar no monitoramento estrutural e direcionar inspeções visuais, diversos métodos de identificação de danos são estudados com base nas características dinâmicas das estruturas, como as frequências naturais e os modos de vibração. A revisão de literatura, porém, demonstra que há uma dificuldade na aplicação desta identificação em estruturas mais complexas de grande porte. Assim, este trabalho visa estudar esta dificuldade e propor uma solução baseada na construção de um índice, composto pelos modos de vibração. Além disso, através da aplicação de algoritmos de aprendizado de máquina e de reconhecimento de padrões, como as Redes Neurais Artificiais (RNAs), propõese aumentar a eficiência do processo de localização espacial e quantificação dos danos. Em seguida, a metodologia proposta é, então, aplicada em um modelo de passarela metálica inspirado em uma estrutura real presente na região do Terminal Centro Olímpico da cidade do Rio de Janeiro – RJ. A identificação de danos é estudada através da aplicação do índice proposto, incorporando as redes neurais e avaliando do impacto da variação dos parâmetros da RNA na eficiência global da detecção. / [en] Civil structures are subjected to different deterioration and corrosion actions throughout their entire service life, which can generate variations in their physical characteristics. These actions can cause damage to its functioning, and possibly leading to collapse in more severe cases. In addition, technology development which allows the design of increasingly slender structures, can produce excessive vibrations, which elevates the importance ofstructural monitoring to a higher level from the perspective of infrastructure managers. Particularly, in the case of bridges and walkaways, due to their large dimensions make monitoring and inspection even more difficult. Thus, with the aim of providing methods to assist in structural monitoring and facilitate visual inspections, several damage identification methods are investigated, which are based on structures dynamic characteristics, such as natural frequencies and mode shapes. The conducted literature review revealed that there is a difficulty in applying these identification methods in large-scale and complex structures. Thus, this research aims to study these barriers and propose a solution based on the development of a new damage index based on the structure s mode shapes. Furthermore, through the application of machine learning algorithms and pattern recognition, such as Artificial Neural Networks (ANN), it is proposed to increase the efficiency of the damage identification and quantification process. Then, the proposed methodology is tested numerically on a steel footbridge model inspired by a real structure located in the region of the Olympic Center Terminal, in the city of Rio de Janeiro – RJ. The damage identification method is studied through the application of the proposed damage index, incorporating the neural network and assessing the impact of ANNs parameters variation in the global efficiency of the damage detection method.