A predição da variância fenotípica é de grande importância para que os sistemas de produção de bovinos de corte consigam aumentar a rentabilidade otimizando o uso de recursos. Modelos mecanicistas dinâmicos do crescimento bovino vêm sendo utilizados como ferramentas de suporte à tomada de decisão em sistemas de manejo individual do gado. Entretanto, a aplicação desses modelos ainda fundamenta-se em parâmetros populacionais, sem qualquer abordagem para que se consiga capturar a variabilidade entre sujeitos nas simulações. Assumindo que modelos mecanicistas sejam capazes de simular o componente de desvio ambiental da variância fenotípica e considerando que marcadores SNPs possam predizer o componente genético dessa variância, esse projeto objetivou evoluir em direção a um modelo matemático que considere a variabilidade entre animais em seu nível genético. Seguindo conceitos de fisiologia genômica computacional, nós assumimos que a variância genética da característica complexa (i.e. produto do comportamento do modelo) surge de características componentes (i.e. parâmetros dos modelos) em níveis hierárquicos mais baixos do sistema biológico. Esse estudo considerou dois modelos mecanicistas do crescimento de bovinos - Cornell Cattle Value Discovery System (CVDS) e Davis Growth Model (DGM) - e ao questionar se os parâmetros de tais modelos mapeariam regiões genômicas que englobam QTLs já descritos para a característica complexa, verificou as suas interpretações biológicas esperadas. Tal constatação forneceu uma prova de conceito de que os parâmetros do CVDS e do DGM são de fato fenótipos cuja interpretação pode ser confirmada através das regiões genômicas mapeadas. Um método de predição genômica foi então utilizado para computar os parâmetros do CVDS e do DGM. Os efeitos dos marcadores SNPs foram estimados tanto para os parâmetros quanto para os fenótipos observados. Nós buscamos qual o melhor cenário de predição - simulações dos modelos com parâmetros computados a partir das informações genômicas ou predição genômica conduzida diretamente nos fenótipos complexos. Nós encontramos que enquanto a predição genômica dos fenótipos complexos pode ser uma melhor opção em relação aos modelos de crescimento, simulações conduzidas com parâmetros obtidos a partir de dados genômicos estão condizentes com simulações geradas com parâmetros obtidos a partir de métodos regulares. Esse é o principal argumento para chamar atenção da comunidade científica de que a abordagem apresentada nesse projeto representa um caminho para o desenvolvimento de uma nova geração de modelos nutricionais aplicados capazes de capturar a variabilidade genética entre bovinos de corte confinados e produzir simulações com variáveis de entrada específicas de cada genótipo. Esse projeto é a primeira abordagem no Brasil conhecida dos autores a usar genótipos Bos indicus para o estudo da aplicação de genômica integrada à modelos mecanicistas para o manejo e comercialização de animais na pecuária. / The prediction of phenotypic variance is important for beef cattle operations to increase profitability by optimizing resource use. Dynamic mechanistic models of cattle growth have been used as decision support tools for individual cattle management systems. However, the application of such models is still based on population parameters, with no further approach to capture between-subject variability. By assuming that mechanistic models are able to simulate environmental deviations components of phenotypic variance and considering that SNPs markers may predict the genetic component of this variance, this project aimed at evolving towards a mathematical model that takes between-animal variance to its genetic level. Following the concepts of computational physiological genomics, we assumed that genetic variance of the complex trait (i.e. outcome of model behavior) arises from component traits (i.e. model parameters) in lower hierarchical levels of biological systems. This study considered two mechanistic models of cattle growth - Cornell Cattle Value Discovery System (CVDS) and Davis Growth Model (DGM) - and verified their expected biological interpretation by asking whether model parameters would map genomic regions that harbors QTLs already described for the complex trait. This provided a proof of concept that CVDS and DGM parameters are indeed phenotypes whose expected interpretations may be stated by means of their mapped genomic regions. A method of genomic prediction to compute parameters for CVDS and DGM was then used. SNP marker effects were estimated both for their parameters and observed phenotypes. We looked for the best prediction scenario - model simulation with parameters computed from genomic data or genomic prediction on complex phenotypes directly. We found that while genomic prediction on complex phenotypes may still be a better option than predictions from growth models, simulations conducted with genomically computed parameters are in accordance with those performed with parameters obtained from regular methods. This is the main argument to call attention from the research community that this approach may pave the way for the development of a new generation of applied nutritional models capable of representing genetic variability among beef cattle under feedlot conditions and performing simulation with inputs from individual\'s genotypes. To our knowledge, this project is the first of this kind in Brazil and the first using Bos indicus genotypes to study the application of genomics integrated with mechanistic models for the management and marketing of commercial livestock.

The low salinity water injection technique (LSWI) has become one of the important research topics in the oil industry because of its possible advantages for improving oil recovery. Several mechanisms describing the LSWI process have been suggested in the literature; however, there is no consensus on a single main mechanism for the low salinity effect on oil recovery. As a result of the latter, there are few models for LSWI and especially for carbonates due to their heterogeneity and complexity. In this research, we proposed a systematic approach for modeling the LSWI effect on oil recovery from carbonates by proposing six different methods for history matching and three different LSWI models for the UTCHEM simulator, empirical, fundamental, and mechanistic LSWI models. The empirical LSWI model uses contact angle measurements and injected water salinity. The fundamental LSWI model captures the effect of LSWI through the trapping number. In the mechanistic LSWI model, we include the effect of different geochemical reactions through Gibbs free energy. Moreover, field-scale predictions of LSWI were performed and followed by a sensitivity analysis for the most influential design parameters using design of experiment (DoE). The LSWI technique was also optimized using the response surface methodology (RSM) where a response surface was built. Also, we moved a step further by investigating the combined effect of injecting low salinity water and carbon dioxide on oil recovery from carbonates through modeling of the process and numerical simulations using the UTCOMP simulator. The analysis showed that CO₂ is the main controller of the residual oil saturation whereas the low salinity water boosts the oil production rate by increasing the oil relative permeability through wettability alteration towards a more water-wet state. In addition, geochemical modeling of LSWI only and the combined effect of LSWI and CO₂ were performed using both UTCHEM and PHREEQC upon which the geochemical model in UTCHEM was modified and validated against PHREEQC. Based on the geochemical interpretation of the LSWI technique, we believe that wettability alteration is the main contributor to the LSWI effect on oil recovery from carbonates by anhydrite dissolution and surface charge change through pH exceeding the point of zero charge. / text

A predição da variância fenotípica é de grande importância para que os sistemas de produção de bovinos de corte consigam aumentar a rentabilidade otimizando o uso de recursos. Modelos mecanicistas dinâmicos do crescimento bovino vêm sendo utilizados como ferramentas de suporte à tomada de decisão em sistemas de manejo individual do gado. Entretanto, a aplicação desses modelos ainda fundamenta-se em parâmetros populacionais, sem qualquer abordagem para que se consiga capturar a variabilidade entre sujeitos nas simulações. Assumindo que modelos mecanicistas sejam capazes de simular o componente de desvio ambiental da variância fenotípica e considerando que marcadores SNPs possam predizer o componente genético dessa variância, esse projeto objetivou evoluir em direção a um modelo matemático que considere a variabilidade entre animais em seu nível genético. Seguindo conceitos de fisiologia genômica computacional, nós assumimos que a variância genética da característica complexa (i.e. produto do comportamento do modelo) surge de características componentes (i.e. parâmetros dos modelos) em níveis hierárquicos mais baixos do sistema biológico. Esse estudo considerou dois modelos mecanicistas do crescimento de bovinos - Cornell Cattle Value Discovery System (CVDS) e Davis Growth Model (DGM) - e ao questionar se os parâmetros de tais modelos mapeariam regiões genômicas que englobam QTLs já descritos para a característica complexa, verificou as suas interpretações biológicas esperadas. Tal constatação forneceu uma prova de conceito de que os parâmetros do CVDS e do DGM são de fato fenótipos cuja interpretação pode ser confirmada através das regiões genômicas mapeadas. Um método de predição genômica foi então utilizado para computar os parâmetros do CVDS e do DGM. Os efeitos dos marcadores SNPs foram estimados tanto para os parâmetros quanto para os fenótipos observados. Nós buscamos qual o melhor cenário de predição - simulações dos modelos com parâmetros computados a partir das informações genômicas ou predição genômica conduzida diretamente nos fenótipos complexos. Nós encontramos que enquanto a predição genômica dos fenótipos complexos pode ser uma melhor opção em relação aos modelos de crescimento, simulações conduzidas com parâmetros obtidos a partir de dados genômicos estão condizentes com simulações geradas com parâmetros obtidos a partir de métodos regulares. Esse é o principal argumento para chamar atenção da comunidade científica de que a abordagem apresentada nesse projeto representa um caminho para o desenvolvimento de uma nova geração de modelos nutricionais aplicados capazes de capturar a variabilidade genética entre bovinos de corte confinados e produzir simulações com variáveis de entrada específicas de cada genótipo. Esse projeto é a primeira abordagem no Brasil conhecida dos autores a usar genótipos Bos indicus para o estudo da aplicação de genômica integrada à modelos mecanicistas para o manejo e comercialização de animais na pecuária. / The prediction of phenotypic variance is important for beef cattle operations to increase profitability by optimizing resource use. Dynamic mechanistic models of cattle growth have been used as decision support tools for individual cattle management systems. However, the application of such models is still based on population parameters, with no further approach to capture between-subject variability. By assuming that mechanistic models are able to simulate environmental deviations components of phenotypic variance and considering that SNPs markers may predict the genetic component of this variance, this project aimed at evolving towards a mathematical model that takes between-animal variance to its genetic level. Following the concepts of computational physiological genomics, we assumed that genetic variance of the complex trait (i.e. outcome of model behavior) arises from component traits (i.e. model parameters) in lower hierarchical levels of biological systems. This study considered two mechanistic models of cattle growth - Cornell Cattle Value Discovery System (CVDS) and Davis Growth Model (DGM) - and verified their expected biological interpretation by asking whether model parameters would map genomic regions that harbors QTLs already described for the complex trait. This provided a proof of concept that CVDS and DGM parameters are indeed phenotypes whose expected interpretations may be stated by means of their mapped genomic regions. A method of genomic prediction to compute parameters for CVDS and DGM was then used. SNP marker effects were estimated both for their parameters and observed phenotypes. We looked for the best prediction scenario - model simulation with parameters computed from genomic data or genomic prediction on complex phenotypes directly. We found that while genomic prediction on complex phenotypes may still be a better option than predictions from growth models, simulations conducted with genomically computed parameters are in accordance with those performed with parameters obtained from regular methods. This is the main argument to call attention from the research community that this approach may pave the way for the development of a new generation of applied nutritional models capable of representing genetic variability among beef cattle under feedlot conditions and performing simulation with inputs from individual\'s genotypes. To our knowledge, this project is the first of this kind in Brazil and the first using Bos indicus genotypes to study the application of genomics integrated with mechanistic models for the management and marketing of commercial livestock.

<p dir="ltr">Fish population abundance and age structure is primarily determined through annual recruitment success (i.e., early life growth and survival through periods of heightened vulnerability). Many physical and biological processes contribute to fish early life growth and survival. Determining the relative contribution of these to annual recruitment variation is challenging owing to the multiple scales across which highly variable processes influence early life growth, mortality, and eventual survival. Elucidating these mechanisms are increasingly complicated by climate change and various anthropogenic stressors as conditions deviate from historic baselines. Thus, while traditional correlative approaches are often sufficient in explaining broad-scale patterns in historic recruitment variation, they are often unable to elicit processes at finer scales of potential importance and forecast future recruitment potential under climate change. Approaches that a) leverage mechanisms withstanding more than a century of research, and b) attempt to account for spatial and temporal scale-dependencies may promote a new understanding of conditions structuring annual patterns in recruitment and advance forecasting of future recruitment potential. Here, we used statistical and mechanistic modeling strategies to explore patterns in alewife and yellow perch recruitment in large lake ecosystems (i.e., the Laurentian Great Lakes). In chapter 2, we investigated nonstationary shifts in yellow perch stock-recruitment relationships in Saginaw Bay, Lake Huron. In chapter 3, we explore climate-induced physical transport phenologies of larval fish and consider the consequences of transport to experienced thermal conditions and encountered prey availability. Finally, in chapter 4, we investigated spatial scales of recruitment variation in a large lake to understand the relative influence of fine-scale asynchrony in structuring broader patterns in historic and potential future recruitment potential. </p>

The liver is the major detoxifying organ, clearing the blood from drugs and other xenobiotics. The extent of hepatic clearance (CL) determines drug exposure and hence, the efficacy and toxicity associated with exposure. Drug-drug interactions (DDIs) that alter the hepatic CL may cause more or less severe outcomes, such as adverse drug reactions. Accurate predictions of drug CL and DDI risk from in vitro data are therefore crucial in drug development. Liver CL depends on several factors including the activities of transporters involved in the hepatic uptake and efflux. The work in this thesis aimed at developing new in vitro and in silico methods to predict hepatic transporter-mediated CL and DDIs in vivo. Particular emphasis was placed on interactions involving the hepatic uptake transporters OATP1B1, OATP1B3, and OATP2B1. These transporters regulate the plasma concentration-time profiles of many drugs including statins. Inhibition of OATP-mediated transport by 225 structurally diverse drugs was investigated in vitro. Several novel inhibitors were identified. The data was used to develop in silico models that could predict OATP inhibitors from molecular structure. Models were developed for static and dynamic predictions of in vivo transporter-mediated drug CL and DDIs. These models rely on a combination of in vitro studies of transport function and mass spectrometry-based quantification of protein expression in the in vitro models and liver tissue. By providing estimations of transporter contributions to the overall hepatic uptake/efflux, the method is expected to improve predictions of transporter-mediated DDIs. Furthermore, proteins of importance for hepatic CL were quantified in liver tissue and isolated hepatocytes. The isolation of hepatocytes from liver tissue was found to be associated with oxidative stress and degradation of transporters and other proteins expressed in the plasma membrane. This has implications for the use of primary hepatocytes as an in vitro model of the liver. Nevertheless, by taking the altered transporter abundance into account using the method developed herein, transport function in hepatocyte experiments can be scaled to the in vivo situation. The concept of protein expression-dependent in vitro-in vivo extrapolations was illustrated using atorvastatin and pitavastatin as model drugs.

L’acquisition de données génomiques ne cesse de croître, ainsi que l’appétit pour les interpréter. Mais déterminer les processus qui ont façonné l’évolution des séquences codantes (et leur importance relative) est un défi scientifique passant par le développement de modèles statistiques de l’évolution prenant en compte de plus en plus d’hétérogénéités au niveau des processus mutationnels et de sélection. Identifier la sélection est une tâche qui nécessite typiquement de détecter un écart entre deux modèles : un modèle nulle ne permettant pas de régime évolutif adaptatif et un modèle alternatif qui lui en permet. Lorsqu’un test entre ces deux modèles rejette le modèle nulle, on considère avoir détecter la présence d’évolution adaptative. La tâche est d’autant plus difficile que le signal est faible et confondu avec diverses hétérogénéités négligées par les modèles. La détection de la sélection sur l’usage des codons spécifiquement est controversée, particulièrement chez les Vertébrés. Plusieurs raisons peuvent expliquer cette controverse : (1) il y a un biais sociologique à voir la sélection comme moteur principal de l’évolution, à un tel point que les hétérogénéités relatives aux processus de mutation sont historiquement négligées ; (2) selon les principes de la génétique des populations, la petite taille efficace des populations des Vertébrés limite le pouvoir de la sélection sur les mutations synonymes conférant elles-mêmes un avantage minime ; (3) par contre, la sélection sur l’usage des codons pourrait être très localisée le long des séquences codantes, à des sites précis, relevant de contraintes de sélection relatives à des motifs utilisés par la machinerie d’épissage, par exemple. Les modèles phylogénétiques de type mutation-sélection sont les outils de prédilection pour aborder ces questions, puisqu’ils modélisent explicitement les processus mutationnels ainsi que les contraintes de sélection. Toutes les hétérogénéités négligées par les modèles mutation-sélection de Yang and Nielsen [2008] peuvent engendrer de faux positifs allant de 20% (préférence site-spécifique en acides aminés) à 100% (hypermutabilité des transitions en contexte CpG) [Laurin-Lemay et al., 2018b]. En particulier, l’hypermutabilité des transitions du contexte CpG peut à elle seule expliquer la sélection détectée par Yang and Nielsen [2008] sur l’usage des codons. Mais, modéliser des phénomènes qui prennent en compte des interdépendances dans les données (par exemple l’hypermutabilité du contexte CpG) augmente de beaucoup la complexité des fonctions de vraisemblance. D’autre part, aujourd’hui le niveau de sophistication des modèles fait en sorte que des vecteurs de paramètres de haute dimensionnalité sont nécessaires pour modéliser l’hétérogénéité des processus étudiés, dans notre cas de contraintes de sélection sur la protéine. Le calcul bayésien approché (Approximate Bayesian Computation ou ABC) permet de contourner le calcul de la vraisemblance. Cette approche diffère de l’échantillonnage par Monte Carlo par chaîne de Markov (MCMC) communément utilisé pour faire l’approximation de la distribution a posteriori. Nous avons exploré l’idée de combiner ces approches pour une problématique spécifique impliquant des paramètres de haute dimensionnalité et de nouveaux paramètres prenant en compte des dépendances entre sites. Dans certaines conditions, lorsque les paramètres de haute dimensionnalité sont faiblement corrélés aux nouveaux paramètres d’intérêt, il est possible d’inférer ces mêmes paramètres de haute dimensionnalité avec la méthode MCMC, et puis les paramètres d’intérêt au moyen de l’ABC. Cette nouvelle approche se nomme CABC [Laurin-Lemay et al., 2018a], pour calcul bayésien approché conditionnel (Conditional Approximate Bayesian Computation : CABC). Nous avons pu vérifier l’efficacité de la méthode CABC en étudiant un cas d’école, soit celui de l’hypermutabilité des transitions en contexte CpG chez les Eutheria [Laurin-Lemay et al., 2018a]. Nous trouvons que 100% des 137 gènes testés possèdent une hypermutabilité des transitions significative. Nous avons aussi montré que les modèles incorporant l’hypermutabilité des transitions en contexte CpG prédisent un usage des codons plus proche de celui des gènes étudiés. Ceci suggère qu’une partie importante de l’usage des codons peut être expliquée à elle seule par les processus mutationnels et non pas par la sélection. Finalement nous explorons plusieurs pistes de recherche suivant nos développements méthodologiques : l’application de la détection de l’hypermutabilité des transitions en contexte CpG à l’échelle des Vertébrés ; l’expansion du modèle pour reconnaître des contextes autres que seul le CpG (e.g., hypermutabilité des transitions et transversions en contexte CpG et TpA) ; ainsi que des perspectives méthodologiques d’amélioration de la performance du CABC. / The acquisition of genomic data continues to grow, as does the appetite to interpret them. But determining the processes that shaped the evolution of coding sequences (and their relative importance) is a scientific challenge that requires the development of statistical models of evolution that increasingly take into account heterogeneities in mutation and selection processes. Identifying selection is a task that typically requires comparing two models: a null model that does not allow for an adaptive evolutionary regime and an alternative model that allows it. When a test between these two models rejects the null, we consider to have detected the presence of adaptive evolution. The task is all the more difficult as the signal is weak and confounded with various heterogeneities neglected by the models. The detection of selection on codon usage is controversial, particularly in Vertebrates. There are several reasons for this controversy: (1) there is a sociological bias in seeing selection as the main driver of evolution, to such an extent that heterogeneities relating to mutation processes are historically neglected; (2) according to the principles of population genetics, the small effective size of vertebrate populations limits the power of selection over synonymous mutations conferring a minimal advantage; (3) On the other hand, selection on the use of codons could be very localized along the coding sequences, at specific sites, subject to selective constraints related to DNA patterns used by the splicing machinery, for example. Phylogenetic mutation-selection models are the preferred tools to address these issues, as they explicitly model mutation processes and selective constraints. All the heterogeneities neglected by the mutation-selection models of Yang and Nielsen [2008] can generate false positives, ranging from 20% (site-specific amino acid preference) to 100% (hypermutability of transitions in CpG context)[Laurin-Lemay et al., 2018b]. In particular, the hypermutability of transitions in the CpG context alone can explain the selection on codon usage detected by Yang and Nielsen [2008]. However, modelling phenomena that take into account data interdependencies (e.g., hypermutability of the CpG context) greatly increases the complexity of the likelihood function. On the other hand, today’s sophisticated models require high-dimensional parameter vectors to model the heterogeneity of the processes studied, in our case selective constraints on the protein. Approximate Bayesian Computation (ABC) is used to bypass the calculation of the likelihood function. This approach differs from the Markov Chain Monte Carlo (MCMC) sampling commonly used to approximate the posterior distribution. We explored the idea of combining these approaches for a specific problem involving high-dimensional parameters and new parameters taking into account dependencies between sites. Under certain conditions, when the high dimensionality parameters are weakly correlated to the new parameters of interest, it is possible to infer the high dimensionality parameters with the MCMC method, and then the parameters of interest using the ABC. This new approach is called Conditional Approximate Bayesian Computation (CABC) [Laurin-Lemay et al., 2018a]. We were able to verify the effectiveness of the CABC method in a case study, namely the hypermutability of transitions in the CpG context within Eutheria [Laurin-Lemay et al.,2018a]. We find that 100% of the 137 genes tested have significant hypermutability of transitions. We have also shown that models incorporating hypermutability of transitions in CpG contexts predict a codon usage closer to that of the genes studied. This suggests that a significant part of codon usage can be explained by mutational processes alone. Finally, we explore several avenues of research emanating from our methodological developments: the application of hypermutability detection of transitions in CpG contexts to the Vertebrate scale; the expansion of the model to recognize contexts other than only CpG (e.g., hypermutability of transitions and transversions in CpG and TpA context); and methodological perspectives to improve the performance of the CABC approach.

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Les challenges posés par les missions d’exploration du système solaire sont très différents de ceux de la Station Spatiale Internationale, puisque les distances sont beaucoup plus importantes, limitant la possibilité de ravitaillements réguliers. Les systèmes support-vie basés sur des plantes supérieures et des micro-organismes, comme le projet de l’Agence Spatiale Européenne (ESA) MELiSSA (Micro Ecological Life Support System Alternative) permettront aux équipages d’être autonomes en termes de production de nourriture, revitalisation de l’air et de recyclage d’eau, tout en fermant les cycles de l’eau, de l’oxygène, de l’azote et du carbone, pendant les missions longue durée, et deviendront donc essentiels.La croissance et le développement des plantes et autres organismes biologiques sont fortement influencés par les conditions environnementales (par exemple la gravité, la pression, la température, l’humidité relative, les pressions partielles en O2 et CO2). Pour prédire la croissance des plantes dans ces conditions non-standard, il est crucial de développer des modèles de croissance mécanistiques, permettant une étude multi-échelle des différents phénomènes, ainsi que d’acquérir une compréhension approfondie de tous les processus impliqués dans le développement des plantes en environnement de gravité réduite et d’identifier les lacunes de connaissance.En particulier, les échanges gazeux à la surface de la feuille sont altérés en gravité réduite, ce qui pourrait diminuer la croissance des plantes dans l’espace. Ainsi, nous avons étudié les relations complexes entre convection forcée, niveau de gravité et production de biomasse et avons trouvé que l’inclusion de la gravité comme paramètre dans les modèles d’échanges gazeux des plantes nécessite une description précise des transferts de matière et d’énergie dans la couche limite. Nous avons ajouté un bilan d’énergie au bilan de masse du modèle de croissance de plante déjà existant et cela a ajouté des variations temporelles sur la température de surface des feuilles.Cette variable peut être mesurée à l’aide de caméras infra-rouges et nous avons réalisé une expérience en vol parabolique et cela nous a permis de valider des modèles de transferts gazeux locaux en 0g et 2g, sans ventilation.Enfin, le transport de sève, la croissance racinaire et la sénescence des feuilles doivent être étudiés en conditions de gravité réduite. Cela permettrait de lier notre modèle d’échanges gazeux à la morphologie des plantes et aux allocations de ressources dans une plante et ainsi arriver à un modèle mécanistique complet de la croissance des plantes en environnement de gravité réduite. / Challenges triggered by human space exploration of the solar system are different from those of the International Space Station because distances and time frames are of a different scale, preventing frequent resupplies. Bioregenerative life-support systems based on higher plants and microorganisms, such as the ESA Micro-Ecological Life Support System Alternative (MELiSSA) project will enable crews to be autonomous in food production, air revitalization, and water recycling, while closing cycles for water, oxygen, nitrogen, and carbon, during long-duration missions and will thus become necessary.The growth and development of higher plants and other biological organisms are strongly influenced by environmental conditions (e.g. gravity, pressure, temperature, relative humidity, partial pressure of O2 or CO2). To predict plant growth in these non-standard conditions, it is crucial to develop mechanistic models of plant growth, enabling multi-scale study of different phenomena, as well as gaining thorough understanding on all processes involved in plant development in low gravity environment and identifying knowledge gaps.Especially gas exchanges at the leaf surface are altered in reduced gravity, which could reduce plant growth in space. Thus, we studied the intricate relationships between forced convection, gravity levels and biomass production and found that the inclusion of gravity as a parameter in plant gas exchanges models requires accurate mass and heat transfer descriptions in the boundary layer. We introduced an energy coupling to the already existing mass balance model of plant growth and this introduced time-dependent variations of the leaf surface temperature.This variable can be measured using infra-red cameras and we implemented a parabolic flight experiment, which enabled us to validate local gas transfer models in 0g and 2g without ventilation.Finally, sap transport needs to be studied in reduced gravity environments, along with root absorption and leaf senescence. This would enable to link our gas exchanges model to plant morphology and resources allocations, and achieve a complete mechanistic model of plant growth in low gravity environments.

Les vaccins ont été une grande réussite en matière de santé publique au cours des dernières années. Cependant, le développement de vaccins efficaces contre les maladies infectieuses telles que le VIH ou le virus Ebola reste un défi majeur. Cela peut être attribué à notre manque de connaissances approfondies en immunologie et sur le mode d'action de la mémoire immunitaire. Les modèles mathématiques peuvent aider à comprendre les mécanismes de la réponse immunitaire, à quantifier les processus biologiques sous-jacents et à développer des vaccins fondés sur un rationnel scientifique. Nous présentons un modèle mécaniste de la dynamique de la réponse immunitaire humorale après injection d'un vaccin Ebola basé sur des équations différentielles ordinaires. Les paramètres du modèle sont estimés par maximum de vraisemblance dans une approche populationnelle qui permet de quantifier le processus de la réponse immunitaire et ses facteurs de variabilité. En particulier, le schéma vaccinal n'a d'impact que sur la réponse à court terme, alors que des différences significatives entre des sujets de différentes régions géographiques sont observées à plus long terme. Cela pourrait avoir des implications dans la conception des futurs essais cliniques. Ensuite, nous développons un outil numérique basé sur la programmation dynamique pour optimiser des schémas d'injections répétées. En particulier, nous nous intéressons à des patients infectés par le VIH sous traitement mais incapables de reconstruire leur système immunitaire. Des injections répétées d'un produit immunothérapeutique (IL-7) sont envisagées pour améliorer la santé de ces patients. Le processus est modélisé par un modèle de Markov déterministe par morceaux et des résultats récents de la théorie du contrôle impulsionnel permettent de résoudre le problème numériquement à l'aide d'une suite itérative. Nous montrons dans une preuve de concept que cette méthode peut être appliquée à un certain nombre de pseudo-patients. Dans l'ensemble, ces résultats s'intègrent dans un effort de développer des méthodes sophistiquées pour analyser les données d'essais cliniques afin de répondre à des questions cliniques concrètes. / Vaccines have been one of the most successful developments in public health in the last years. However, a major challenge still resides in developing effective vaccines against infectious diseases such as HIV or Ebola virus. This can be attributed to our lack of deep knowledge in immunology and the mode of action of immune memory. Mathematical models can help understanding the mechanisms of the immune response, quantifying the underlying biological processes and eventually developing vaccines based on a solid rationale. First, we present a mechanistic model for the dynamics of the humoral immune response following Ebola vaccine immunizations based on ordinary differential equations. The parameters of the model are estimated by likelihood maximization in a population approach, which allows to quantify the process of the immune response and its factors of variability. In particular, the vaccine regimen is found to impact only the response on a short term, while significant differences between subjects of different geographic locations are found at a longer term. This could have implications in the design of future clinical trials. Then, we develop a numerical tool based on dynamic programming for optimizing schedule of repeated injections. In particular, we focus on HIV-infected patients under treatment but unable to recover their immune system. Repeated injections of an immunotherapeutic product (IL-7) are considered for improving the health of these patients. The process is first by a piecewise deterministic Markov model and recent results of the impulse control theory allow to solve the problem numerically with an iterative sequence. We show in a proof-of-concept that this method can be applied to a number of pseudo-patients. All together, these results are part of an effort to develop sophisticated methods for analyzing data from clinical trials to answer concrete clinical questions.