Neste trabalho avaliamos a formação de oxigênio singlete in vitro em fagócitos, (células mononucleares e neutrófilos) isolados de sangue periférico humano, e eosinófilos, de lavado bronco alveolar de camundongos balb/c, ativados por estímulo partículado: zimosan opsonizado contendo o 9,10difenilantraceno (DPA) adsorvido como sonda captadora de 1O2. Por este método, a formação do 1O2 pode ser verificada pela formação do 9,10-difenilantraceno endoperóxido (DPAO2), que é detectado por HPLC. Observamos, que os fagócitos formam 1O2 e que esta formação parece ocorrer de forma diferenciada para os dois tipos celulares (neutrófilos e células mononucleares). Visando ampliar os estudos anteriores sobre o papel da melatonina (MLT) no processo inflamatório, foi testado seu efeito em fagócitos e a relação na produção de 1O2 destas células. Observamos que MLT inibe a formação de 1O2 totalmente no caso de neutrófilos e parcialmente no caso de células mononucleares e eosinófilos. Paralelamente, foi desenvolvida a síntese de um novo captador químico de 1O2, o éster 9,10-antracenil-3-bispropionato de etila (ABPE), cuja finalidade principal é o acúmulo no interior da célula, depois de sofrer hidrólise enzimática. Esta sonda, terá facil acesso ao interior das células em sua forma ester. Este novo captador de 1O2 foi testado em células mononucleares e neutrófilos estimulados de formas diferentes: via receptor independente e dependente. Os resultados demonstraram produção equivalente de 1O2 nestes fagócitos. / In this study, we evaluated the singlet oxygen (1O2) formation in vitro from phagocytes (neutrophils and mononuclear cells) isolated from human blood cells and eosinophils isolated from bronchoalveolar lavage fluid of mice balb/c activated, by opsonized zymosan. To determine whether singlet oxygen is produced by phagocytes, zymosan particles were coated with a specific chemical trap for 1O2, 9,10-diphenylanthracene (DPA). The production of 1O2 was followed using HPLC, to measure its product, 9,10-diphenylanthracene endoperoxide (DPAO2). We also noticed that the 1O2 production occurs at different levels of for two cell types, neutrophils and mononuclear cells. In order to broaden previous studies on the role of melatonin (MLT) in inflammatory processes, its effect was tested in phagocytes was tested in relation to 1O2 formation by these cells. We observed that MLT inhibits the 1O2 formation totallymt neutrophils and partiallym mononuclear cells and eosinophils. At the some time, it was also developed the synthesis of a new probe for 1O2, the 9,10-anthracene-bis-3-ethyl-propionate (ABEP), with the purpose to accumulate inside the cells, after its enzymatic hydrolysis. This probe presents easy acess to the inferior of the cells in its ester form. This new probe for trapping 1O2 was tested in mononuclear cells and neutrophils stimulated in two ways: via independent and dependent receptor. The results showed equivalent production of 1O2 for both cell types.

Les promesses de la médecine de précision dépendent de nouvelles solutions technologiques pour le diagnostic. Dans l’aire post-génomique, les approches de biologie synthétique pour la médecine apportent de nouvelles façon de sonder, monitorer et interfacer la physiopathologie humaine. Émergeant en tant que champ scientifique mature dont la transition clinique s’accélère, la biologie synthétique peut être utilisée pour appliquer des principes d’ingénierie afin de concevoir et construire des systèmes biologiques comprenant des spécifications cliniques. Une application particulièrement intéressante est de développer des outils diagnostiques polyvalents, programmables et intelligents étroitement interconnectés avec la thérapie. Cette thèse présente de nouveaux concepts et approches d’ingénierie pour concevoir des dispositifs biosynthétiques capable d’interfacer les maladies humaines dans des échantillons cliniques en exploitant du traitement de signal au niveau biomoléculaire, à la lumière d’un besoin croissant en termes de capacités et de robustesse. Cette thèse s’intéresse en premier lieu à l’ingénierie de circuits synthétiques de gènes, reposant sur les portes logiques à integrases, pour intégrer des opérations modulaires et programmables de biodétéction de biomarqueur associées à des algorithmes de décisions au sein de population de bactéries. Elle s’intéresse ensuite à des méthodologies systématiques dites bottom-up, pour programmer des protocellules synthétiques microscopiques, capables d’exécuter des opérations de biodétéction médicale et de biocomputation. Nous décrivons le développement de méthodes simples de fabrications microfluidique associées à des solutions pour implémenter des opérations Booléenne complexes en utilisant de circuits biochimiques synthétiques. Cette contribution s’élargit aussi à la caractérisation de l’espace de conception de protocellules à l’aide d’approches de design assisté par ordinateur, ainsi que à l’analyse de preuves mathématiques et biologiques pour l’utilisation de protocellules comme des dispositifs universels de calcul. L’articulation des principes biologiques fondamentaux avec les implications médicales concernant les dispositifs biosynthétiques développés dans ce travail, a été jusqu’à la validation clinique, et initie de nouveaux modèles pour le développement de diagnostics de nouvelle génération. Ce travail prévoit que la biologie synthétique est en train de préparer le future de la médecine, en supportant et accélérant le développement de diagnostics avec de nouvelles capacités, apportant un progrès biotechnologique direct depuis le laboratoire de biologie clinique jusqu’au patient. / The promise for real precision medicine is contingent on novel technological solutions to diagnosis. In the post-genomic era, synthetic biology approaches to medicine provide new ways to probe, monitor and interface human pathophysiology. Emerging as a mature field increasingly transitioning to the clinics, synthetic biology can be used to apply engineering principles to design and build biological systems with clinical specifications. A particularly tantalizing application is to develop versatile, programmable and intelligent diagnostic devices closely interconnected with therapy. This thesis presents novel engineering concepts and approaches to design synthetic biological devices interfacing human diseases in clinical samples through biomolecular digital signal processing, in light of a need for dramatic improvements in capabilities and robustness. It addresses primarily the engineering of synthetic gene circuits through integrase based digital genetic amplifiers and logic gates, to integrate modular and programmable biosensing of biomarkers and diagnostic decision algorithms into bacteria. It then investigates systematic bottom-up methodologies to program microscale synthetic protocells performing medical biosensing and biocomputing operations. We demonstrate streamlined microfluidic fabrication methods and solutions to implement complex Boolean operation using integrated synthetic biochemical circuits. This contribution also extends to the characterization of protocell design space through novel computer assisted design frameworks, as well as the analysis of mathematical and biological evidence for universal protocellular biocomputing devices. The articulation of biological governing principles and medical implications for the synthetic devices developed in this work was further validated in the clinic, and initiates new models towards next-generation diagnostics. This work envisions that synthetic biology is preparing the future of medicine, supporting and speeding up the development of diagnostics with novel capabilities to bring direct improvement in biotechnologies from the clinical lab to the patient.

Advances in computing capabilities have facilitated the application of quantum mechanical methods to increasingly larger and more complex chemical systems, including weakly interacting and biologically relevant species. One such ab initio-based composite methodology, the correlation consistent composite approach (ccCA), has been shown to be reliable for the prediction of enthalpies of formation and reaction energies of main group species in the gas phase to within 1 kcal mol-1, on average, of well-established experiment, without dependence on experimental parameterization or empirical corrections. In this collection of work, ccCA has been utilized to determine the proton affinities of deoxyribonucleosides within an ONIOM framework (ONIOM-ccCA) and to predict accurate enthalpies of formation for organophosphorus compounds. Despite the complexity of these systems, ccCA is shown to result in enthalpies of formation to within ~2 kcal mol-1 of experiment and predict reliable reaction energies for systems with little to no experimental data. New applications for the ccCA method have also been introduced, expanding the utility of ccCA to solvated systems and complexes with significant noncovalent interactions. By incorporating the SMD solvation model into the ccCA formulation, the Solv-ccCA method is able to predict the pKa values of nitrogen systems to within 0.7 pKa unit (less than 1.0 kcal mol-1), overall. A hydrogen bonding constant has also been developed for use with weakly interacting dimers and small cluster compounds, resulting in ccCA interaction energies for water clusters and dimers of the S66 set to within 1.0 kcal mol-1 of well-established theoretical values.

Dans cette thèse, nous présentons un cadre théorique pour étudier les systèmes de particules actives fonctionnant avec une quantité discrète d'états internes qui contrôlent le comportement externe de ces objets. Les concepts théoriques développés dans cette thèse sont introduits afin de comprendre un grand nombre de systèmes biologiques multi-agents dont les individus présentent différents types de comportements se succédant au cours du temps. Par construction, le modèle théorique suppose que l'observateur extérieur a accès uniquement au comportement visible des individus, et non pas à leurs états internes. C'est seulement après une étude détaillée de la dynamique comportementale que l'existence de ces états internes devient évidente. Cette analyse est cruciale pour pouvoir associer les comportements observés expérimentalement avec un ou plusieurs états internes du modèle. Cette association entre les états et les comportements doit être faite selon les observations et la phénoménologie du système biologique faisant l'objet de l'étude. Les scénarios qui peuvent être observés en utilisant notre modèle théorique sont déterminés par la conception du mécanisme interne des individus (nombre d'états internes, taux de transition, etc…) et seront de nature markovienne par construction. Tous les travaux expérimentaux et théoriques contenus dans cette thèse démontrent que notre modèle est approprié pour décrire des systèmes réels montrant des comportements intermittents individuels ou collectifs. Ce nouveau cadre théorique pour des particules actives avec états internes, introduit ici, est encore en développement et nous sommes convaincus qu'il peut potentiellement ouvrir de nouvelles branches de recherche à l'interface entre la physique, la biologie et les mathématiques. / In this thesis we introduce a theoretical framework to understand collections of active particles that operate with a finite number of discrete internal states that control the external behavior of these entities. The theoretical concepts developed in this thesis are conceived to understand the large number of existing multiagent biological systems where the individuals display distinct behavioral phases that alternate with each other. By construction, the premise of our theoretical model is that an external observer has access only to the external behavior of the individuals, but not to their internal state. It is only after careful examination of the behavioral dynamics that the existence of these internal states becomes evident. This analysis is key to be able to associate the experimentally observed behaviors of individuals with one or many internal states of the model. This association between states and behaviors should be done accordingly to the observations and the phenomenology displayed by the biological system that is being the subject of study. The possible scenarios that can be observed using our theoretical model are determined by the design of the internal mechanism of the individuals (number of internal states, transition rates, etc...) and will be of markovian nature by construction. All the experimental and theoretical work contained in this thesis is evidence that our model is suitable to be used to describe real-life systems showing individual or collective intermittent behaviors. This here-introduced new framework of active particles with internal states is still in development and we are convinced that it can potentially open new branches of research at the interface between physics, biology and mathematics.

The aim of this thesis is to define and study stochastic models of repairable systems and the application of these models to biological systems, especially for cell survival after irradiation with ionizing radiation.

Multi-core, multi-processor, and Graphics Processing Unit (GPU) computer architectures pose significant challenges with respect to the efficient exploitation of parallelism for large-scale, scientific computing simulations. For example, a simulation of the human tonsil at the cellular level involves the computation of the motion and interaction of millions of cells over extended periods of time. Also, the simulation of Radiative Heat Transfer (RHT) effects by the Photon Monte Carlo (PMC) method is an extremely computationally demanding problem. The PMC method is example of the Monte Carlo simulation method—an approach extensively used in wide of application areas. Although the basic algorithmic framework of these Monte Carlo methods is simple, they can be extremely computationally intensive. Therefore, an efficient parallel realization of these simulations depends on a careful analysis of the nature these problems and the development of an appropriate software framework. The overarching goal of this dissertation is develop and understand what the appropriate parallel programming model should be to exploit these disparate architectures, both from the metric of efficiency, as well as from a software engineering perspective. In this dissertation we examine these issues through a performance study of PathSim2, a software framework for the simulation of large-scale biological systems, using two different parallel architectures’ distributed and shared memory. First, a message-passing implementation of a multiple germinal center simulation by PathSim2 is developed and analyzed for distributed memory architectures. Second, a germinal center simulation is implemented on shared memory architecture with two parallelization strategies based on Pthreads and OpenMP. Finally, we present work targeting a complete hybrid, parallel computing architecture. With this work we develop and analyze a software framework for generic Monte Carlo simulations implemented on multiple, distributed memory nodes consisting of a multi-core architecture with attached GPUs. This simulation framework is divided into two asynchronous parts: (a) a threaded, GPU-accelerated pseudo-random number generator (or producer), and (b) a multi-threaded Monte Carlo application (or consumer). The advantage of this approach is that this software framework can be directly used within any Monte Carlo application code, without requiring application-specific programming of the GPU. We examine this approach through a performance study of the simulation of RHT effects by the PMC method on a hybrid computing architecture. We present a theoretical analysis of our proposed approach, discuss methods to optimize performance based on this analysis, and compare this analysis to experimental results obtained from simulations run on two different hybrid, parallel computing architectures. / Ph. D.

This thesis is devoted to the study and application of constraint-based metabolic models. The objective was to find simple ways to handle the difficulties that arise in practice due to uncertainty (knowledge is incomplete, there is a lack of measurable variables, and those available are imprecise). With this purpose, tools have been developed to model, analyse, estimate and predict the metabolic behaviour of cells. The document is structured in three parts. First, related literature is revised and summarised. This results in a unified perspective of several methodologies that use constraint-based representations of the cell metabolism. Three outstanding methods are discussed in detail, network-based pathways analysis (NPA), metabolic flux analysis (MFA), and flux balance analysis (FBA). Four types of metabolic pathways are also compared to clarify the subtle differences among them. The second part is devoted to interval methods for constraint-based models. The first contribution is an interval approach to traditional MFA, particularly useful to estimate the metabolic fluxes under data scarcity (FS-MFA). These estimates provide insight on the internal state of cells, which determines the behaviour they exhibit at given conditions. The second contribution is a procedure for monitoring the metabolic fluxes during a cultivation process that uses FS-MFA to handle uncertainty. The third part of the document addresses the use of possibility theory. The main contribution is a possibilistic framework to (a) evaluate model and measurements consistency, and (b) perform flux estimations (Poss-MFA). It combines flexibility on the assumptions and computational efficiency. Poss-MFA is also applied to monitoring fluxes and metabolite concentrations during a cultivation, information of great use for fault-detection and control of industrial processes. Afterwards, the FBA problem is addressed. / Llaneras Estrada, F. (2011). Interval and Possibilistic Methods for Constraint-Based Metabolic Models [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10528

L'auto-organisation des organismes vivants est d'une complexité et d'une efficacité étonnantes. Plus précisément, les systèmes biologiques abritent un nombre gigantesque de réactions très spécifiques qui nécessitent que la bonne biomolécule se retrouve à la bonne place, dans le bon ordre et en un temps suffisamment court pour permettre le fonctionnement cellulaire, et au-delà la vie cellulaire. D'un point de vue dynamique, cela pose la question fondamentale de savoir comment les biomolécules trouvent efficacement leur(s) cible(s) spécifique(s), ou encore, quels types de forces rassemblent tous ces partenaires de réaction spécifiques dans un environnement aussi dense et ionisé que les micro-environnements cellulaires. Dans cette thèse, nous explorons la possibilité que des biomolécules puissent interagir à travers des interactions électromagnétiques de longue-portée telles que ces dernières sont prédites à partir des premiers principes de la physique; ''longue-portée'' signifiant que les interactionsen question sont actives sur des distances bien plus larges que les dimensions typiques des molécules mises en jeu (i.e., plus grandes qu'environ 50 angströms dans les systèmes biologiques). Après avoir posé les fondements théoriques concernant les interactionsde longue-portée potentiellement actives sur de longue distances dans un contexte biologique, nous étudions la posssibilité de détecter leur éventuelle contribution à partir de dispositifs expérimentaux qui sont accessibles de nos jours. Sur ce dernier point, des résultats préliminaires encourageants tant sur le plan théorique qu'expérimental sont présentés. / Self-organization of living organisms is of an astonishing complexity and efficiency. More specifically, biological systems are the site of a huge number of very specific reactions thatrequire the right biomolecule to be at the right place, in the right order and in a reasonably short time to sustain cellular function and ultimately cellular life. From the dynamic point of view, this raises the fundamental question of how biomolecules effectively find their target(s); in other words, what kinds of forces bring all these specific cognate partners together in an environment as dense and ionized as cellular micro-environments. In the present thesis, we explore the possibility that biomolecules interact through long-range electromagnetic interactions as they are predicted from the first principles of physics; "long-range" meaning that the mentioned interactions are effective over distances much larger than the typical dimensions of the molecules involved (i.e., larger than about 50 angströms in biological systems).After laying the theoretical foundations about interactions that are potentially active over long distances in a biological context, we investigate the possibility of detecting their contribution from experimental devices which are nowadays available. On the latter point, encouraging preliminary results both at the theoretical and experimental levels are exposed.

Le thème des travaux qui fait l'objet de ce mémoire de thèse s'inscrit dans le cadre de la caractérisation de systèmes biologiques par modèles non entiers. Cette thèse comporte deux parties qui reposent sur deux collaborations distinctes. La première s'appuie sur une collaboration avec le laboratoire Mouvement Adaptation Cognition de l'Université Bordeaux 2 et l'institut Magendie de l'Inserm. L'objectif de ce travail consiste à étudier l'influence la longueur du muscle sur sa dynamique dans les cas de variations statiques et dynamiques de cette grandeur. La deuxième collaboration est un projet original, en partenariat avec l'équipe Anesthésiologie-Réanimation II du CHU Haut-Lévêque ayant pour but l'identification de transfert thermique dans le poumon au cours d'opération à cœur ouvert, grâce à des mesures obtenues sur des poumons de mouton. / This PhD thesis deals with biological system characterization using fractional models. This study is divided in two parts stemming from two different cooperations. The first one involves the laboratoire Mouvement Adaption Cognition of Université Bordeaux 2 and the Institut Magendie of Inserm. The aim of this teamwork is to study the muscle length effect on its dynamic, considering static and dynamical length variations. The second collaboration involves the Anesthésiologie-Réanimation team of CHU Haut-Lévêque from Bordeaux. This research work aims at identifying models of thermal transfer inside the lungs during open-heart surgery.

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.