Stochastic effects in cellular systems are usually modeled and simulated with Gillespie's stochastic simulation algorithm (SSA), which follows the same theoretical derivation as the chemical master equation (CME), but the low efficiency of SSA limits its application to large chemical networks. To improve efficiency of stochastic simulations, Haseltine and Rawlings proposed a hybrid of ODE and SSA algorithm, which combines ordinary differential equations (ODEs) for traditional deterministic models and SSA for stochastic models. In this dissertation, accuracy analysis, efficient implementation strategies, and application of of Haseltine and Rawlings's hybrid method (HR) to a budding yeast cell cycle model are discussed. Accuracy of the hybrid method HR is studied based on a linear chain reaction system, motivated from the modeling practice used for the budding yeast cell cycle control mechanism. Mathematical analysis and numerical results both show that the hybrid method HR is accurate if either numbers of molecules of reactants in fast reactions are above certain thresholds, or rate constants of fast reactions are much larger than rate constants of slow reactions. Our analysis also shows that the hybrid method HR allows for a much greater region in system parameter space than those for the slow scale SSA (ssSSA) and the stochastic quasi steady state assumption (SQSSA) method. Implementation of the hybrid method HR requires a stiff ODE solver for numerical integration and an efficient event-handling strategy for slow reaction firings. In this dissertation, an event-handling strategy is developed based on inverse interpolation. Performances of five wildly used stiff ODE solvers are measured in three numerical experiments. Furthermore, inspired by the strategy of the hybrid method HR, a hybrid of ODE and SSA stochastic models for the budding yeast cell cycle is developed, based on a deterministic model in the literature. Simulation results of this hybrid model match very well with biological experimental data, and this model is the first to do so with these recently available experimental data. This study demonstrates that the hybrid method HR has great potential for stochastic modeling and simulation of large biochemical networks. / Ph. D.

Magnetic resonance imaging (MRI) of the liver is an important non-invasive tool for diagnosing liver disease. A key application is dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). With the use of the hepatocyte specific contrast agent (CA) Gd-EOB-DTPA it is now possible to evaluate the liver function. Beyond traditional qualitative evaluation of the DCE-MRI images, parametric quantitative techniques are on the rise which yields more objective evaluations. Systems biology is a gradually expanding field using mathematical modeling to gain deeper mechanistic understanding in complex biological systems. The aim of this thesis to combine these two fields in order to derive a physiologically accurate minimal whole body model that can be used to quantitatively evaluate liver function using clinical DCE-MRI examinations.  The work is based on two previously published sources of data using Gd-EOB-DTPA in healthy humans; i) a region of interest analysis of the liver using DCE-MRI ii) a pre-clinical evaluation of the contrast agent using blood sampling.  The modeling framework consists of a system of ordinary differential equations for the contrast agent dynamics and non-linear models for conversion of contrast agent concentrations to relaxivity values in the DCE-MRI image volumes. Using a χ2-test I have shown that the model, with high probability, can fit the experimental data for doses up to twenty times the clinically used one, using the same parameters for all doses. The results also show that some of the parameters governing the hepatocyte flux of CA can be numerically identifiable. Future applications with the model might be as a basis for regional liver function assessment. This can lead to disease diagnosis and progression evaluation for physicians as well as support for surgeons planning liver resection.

Human induced pluripotent stem cells (hiPSCs) have two main properties: pluripotency and self-renewal. Physical cues presented by biomaterial scaffolds can stimulate differentiation of hiPSCs to neurons. In this work, we develop and analyze a mathematical model of aggregate growth and neural differentiation on melt electrospun biomaterial scaffolds. An ordinary differential equation model of population size of each cell state (stem, progenitor, differentiated) was developed based on experimental results and previous literature. Analysis and numerical simulations of the model successfully capture many of the dynamics observed experimentally. Analysis of the model gives optimal parameter sets, that correspond to experimental procedures, to maximize particular populations. The model indicates that a physiologic oxygen level (~5%) increases population sizes compared to atmospheric oxygen levels (~21%). Model analysis also indicates that the optimal scaffold porosity for maximizing aggregate size is approximately 63%. This model allows for the use of mathematical analysis and numerical simulations to determine the key factors controlling cell behavior when seeded on melt electrospun scaffolds. / Graduate

abstract: The phycologist, M. R. Droop, studied vitamin B12 limitation in the flagellate Monochrysis lutheri and concluded that its specific growth rate depended on the concentration of the vitamin within the cell; i.e. the cell quota of the vitamin B12. The Droop model provides a mathematical expression to link growth rate to the intracellular concentration of a limiting nutrient. Although the Droop model has been an important modeling tool in ecology, it has only recently been applied to study cancer biology. Cancer cells live in an ecological setting, interacting and competing with normal and other cancerous cells for nutrients and space, and evolving and adapting to their environment. Here, the Droop equation is used to model three cancers. First, prostate cancer is modeled, where androgen is considered the limiting nutrient since most tumors depend on androgen for proliferation and survival. The model's accuracy for predicting the biomarker for patients on intermittent androgen deprivation therapy is tested by comparing the simulation results to clinical data as well as to an existing simpler model. The results suggest that a simpler model may be more beneficial for a predictive use, although further research is needed in this field prior to implementing mathematical models as a predictive method in a clinical setting. Next, two chronic myeloid leukemia models are compared that consider Imatinib treatment, a drug that inhibits the constitutively active tyrosine kinase BCR-ABL. Both models describe the competition of leukemic and normal cells, however the first model also describes intracellular dynamics by considering BCR-ABL as the limiting nutrient. Using clinical data, the differences in estimated parameters between the models and the capacity for each model to predict drug resistance are analyzed. Last, a simple model is presented that considers ovarian tumor growth and tumor induced angiogenesis, subject to on and off anti-angiogenesis treatment. In this environment, the cell quota represents the intracellular concentration of necessary nutrients provided through blood supply. Mathematical analysis of the model is presented and model simulation results are compared to pre-clinical data. This simple model is able to fit both on- and off-treatment data using the same biologically relevant parameters. / Dissertation/Thesis / Doctoral Dissertation Applied Mathematics 2015

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Les neurosciences computationnelles consistent en l’étude du système nerveux par la modélisation et la simulation. Plus le modèle sera proche de la réalité et plus les ressources calculatoires exigées seront importantes. La question de la complexité et de la précision est un problème bien connu dans la simulation. Les travaux de recherche menés dans le cadre de cette thèse visent à améliorer la simulation de modèles mathématiques représentant le comportement physique et chimique de récepteurs synaptiques. Les modèles sont décrits par des équations différentielles ordinaires (EDO), et leur résolution passe par des méthodes numériques. Dans le but d’optimiser la simulation, j’ai implémenté différentes méthodes de résolution numérique des EDO. Afin de faciliter la sélection du meilleur algorithme de résolution numérique, une méthode nécessitant un minimum d’information a été proposée. Cette méthode permet de choisir l’algorithme qui optimise la simulation. La méthode a permis de démontrer que la dynamique d’un modèle de récepteur synaptique influence plus les performances des algorithmes de résolution que la structure cinétique du modèle lui-même. De plus, afin de caractériser des comportements pathogènes, une phase d’optimisation est réalisée. Cependant, certaines valeurs de paramètres rendent le modèle instable. Une étude de stabilité a permis de déterminer la stabilité du modèle pour des paramètres fournis par la littérature, mais également de remonter à des contraintes de stabilité sur les paramètres. Le respect de ces contraintes permet de garantir la stabilité des modèles étudiés, et donc de garantir le succès de la procédure permettant de rendre un modèle pathogène. / Computational Neuroscience consists in studying the nervous system through modeling and simulation. It is to characterize the laws of biology by using mathematical models integrating all known experimental data. From a practical point of view, the more realistic the model, the largest the required computational resources. The issue of complexity and accuracy is a well known problem in the modeling and identification of models. The research conducted in this thesis aims at improving the simulation of mathematical models representing the physical and chemical behavior of synaptic receptors. Models of synaptic receptors are described by ordinary differential equations (ODE), and are resolved with numerical procedures. In order to optimize the performance of the simulations, I have implemented various ODE numerical resolution methods. To facilitate the selection of the best solver, a method, requiring a minimum amount of information, has been proposed. This method allows choosing the best solver in order to optimize the simulation. The method demonstrates that the dynamic of a model has greater influence on the solver performances than the kinetic scheme of the model. In addition, to characterize pathogenic behavior, a parameter optimization is performed. However, some parameter values lead to unstable models. A stability study allowed for determining the stability of the models with parameters provided by the literature, but also to trace the stability constraints depending to these parameters. Compliance with these constraints ensures the stability of the models studied during the optimization phase, and therefore the success of the procedure to study pathogen models.

Ce mémoire présente une approche basée sur des méthodes garanties pour résoudre des problèmes d’optimisation de systèmes dynamiques multi-physiques. Ces systèmes trouvent des applications directes dans des domaines variés tels que la conception en ingéniérie, la modélisation de réactions chimiques, la simulation de systèmes biologiques ou la prédiction de la performance sportive.La résolution de ces problèmes d’optimisation s’effectue en deux phases. La première consiste à mettre le problème en équations sous forme d’un modèle mathématique constitué d’un ensemble de variables, d’un ensemble de contraintes algébriques et fonctionelles ainsi que de fonctions de coût. Celles-ci sont utilisées lors de la seconde phase qui consiste à d’extraire du modèle les solutions optimales selon plusieurs critères (volume, poids, etc).Les contraintes algébriques permettent de manipuler des grandeurs statiques (quantité, taille, densité, etc). Elles sont non linéaires, non convexes et parfois discontinues.Les contraintes fonctionnelles permettent de manipuler des grandeurs dynamiques. Ces contraintes peuvent être relativement simples comme la monotonie ou la périodicité, mais aussi bien plus complexe par la prise en compte de contraintes différentielles simples ou définies par morceaux. Les équations différentielles sont utilisées pour modéliser des comportements physico-chimiques (magnétiques, thermiques, etc) et d’autres caractéristiques qui varient lors de l’évolution du système.Il existe plusieurs niveaux d’approximation pour chacune de ces deux phases. Ces approximations donnent des résultats pertinents, mais elles ne permettent pas de garantir l’optimalité ni la réalisabilité des solutions.Après avoir présenté un ensemble de méthodes garanties permettant de résoudre de manière garantie des équations différentielles ordinaires, nous formalisons un modèle particulier de systèmes hybrides sous la forme d’équations différentielles ordinaires par morceaux. A l’aide de plusieurs preuves et théorèmes nous étendons la première méthode de résolution pour résoudre de manière garantie ces équations différentielles par morceaux. Dans un second temps, nous intégrons ces deux méthodes au sein d’un module de programmation par contracteurs, que nous avons implémenté. Ce module basé sur des méthodes garantie permet de résoudre des problèmes de satisfaction de contraintes algébriques et fonctionnelles. Ce module est finalement utilisé dans un algorithme d’optimisation globale déterministe modulaire permettant de résoudre les problèmes considérés. / In this thesis a set of tools based on guaranteed methods are presented in order to solve multi-physics dynamic problems. These systems can be applied in various domains such that engineering design process, model of chemical reactions, simulation of biological systems or even to predict athletic performances.The resolution of these optimization problems is made of two stages. The first one consists in defining a mathematical model by setting up the equations for the problem. The model is made of a set of variables, a set of algebraic and functional constraints and cost functions. The latter are used in the second stage in order to extract the optimal solutions from the model depending on several criteria (volume, weight, etc).Algebraic constraints are used to describe the static properties of the system (quantity, size, density, etc). They are non-linear, non-convex and sometimes discontinuous. Functional constraints are used to manipulate dynamic quantities. These constraints can be quite simple such as monotony or periodicity or they can be more complex such as simple or piecewise differential constraints. Differential equations are used to describe physico-chemical properties (magnetic, thermal, etc) and other features evolving with the component use. Several levels of approximation exist for each of these two stages. These approximations give some relevant results but they do not guarantee the feasibility nor the optimality of the solutions.After presenting a set of guaranteed methods in order to perform the guaranteed integration of ordinary differential equations, a peculiar type of hybrid system that can be modeled with piecewise ordinary differential equation is considered. A new method that computes guaranteed integration of these piecewise ordinary differential equations is developed through an extension of the initial algorithm based on several proofs and theorems. In a second step these algorithms are gathered within a contractor programming module that have been implemented. It is used to solve algebraic and functional constraint satisfaction problems with guaranteed methods. Finally, the considered optimization problems are solved with a modular deterministic global optimization algorithm that uses the previous modules.

Made available in DSpace on 2016-03-15T19:38:51Z (GMT). No. of bitstreams: 1 Pericles do Prado Turnes Junior.pdf: 1375255 bytes, checksum: 24dc630ef135368b840995d533e161e8 (MD5) Previous issue date: 2014-07-29 / Instituto Presbiteriano Mackenzie / There are many epidemiological models written in terms of ordinary differential equations (ODE). This approach is based on the homogeneous mixing assumption; that is, the topological structure of the network of social contacts, established by the individuals in the population, is not relevant to forecast the propagation of the studied pathogen. In this work, an epidemiological model formulated in terms of ODE and probabilistic cellular automata (PCA) is proposed to study the spread of contagious diseases that do not conferimmunity. The state variables of this model are the percentages of susceptible individuals, infected individuals and empty space. It is shown that this dynamical system can experience Hopf and transcritical bifurcations. Then, this model is used to evaluate the validity of the homogeneous mixing assumption, by using real data related to the transmission of gonorrhea, hepatitis C virus, human immunodeficiency virus and obesity. / Muitos modelos epidemiológicos são escritos em termos de equações diferenciais ordinárias (EDO). Essa abordagem baseia-se no pressuposto de mistura homogênea; ou seja, a estrutura topológica da rede de contatos sociais, estabelecida pelos indivíduos da população, não é relevante para prever o avanço do patógeno em estudo. Neste trabalho, é proposto um modelo epidemiológico formulado em termos de EDO e de autômato celular probabilista (ACP) para estudar a propagação de doenças contagiosas que não conferem imunidade. As variáveis de estado desse modelo são as porcentagens de indivíduos suscetíveis, de indivíduos infectados e de espaço vazio. Mostra-se que esse sistema dinâmico pode apresentar bifurcações de Hopf e transcrítica. O modelo é , então, usado para avaliar a validade da hipótese de mistura homogênea, usando dados relacionados à transmissão de gonorreia, vírus da hepatite C, vírus da imunodeficiência humana e obesidade.

Ce travail porte sur l'étude de structures multicouches à gradient de propriétés (FGM : Functionnally Graded Materials). Ces matériaux sont apparus afin d'obtenir des dépôts aux caractéristiques nouvelles et innovantes. Les FGM sont désormais présents dans divers applications de haute technologie.Un système multicouche à gradient de composition entre le cuivre et le nickel, a fait l'objet d'une étude expérimentale par l'application de la technique des ultrasons laser (LU) couplée à une étude numérique basée sur le formalisme de Stroh et la méthode de la matrice de raideur. Le travail de thèse est organisé autour de quatre chapitres. Le premier chapitre est dédié à l'aspect théorique de la propagation des ondes de surface dans une structure multicouche et à gradient de propriétés. Ainsi, un développement des méthodes numériques pour les matériaux dotés de la piézoélectricité est fourni. Le second chapitre se consacre à l'élaboration des échantillons utilisés dans notre étude et obtenus par pulvérisation cathodique. Le troisième chapitre présente la méthode opto-acoustique utilisée pour caractériser les échantillons réalisés. le dernier chapitre présente les résultats expérimentaux, confrontés aux résultats théoriques, décrivant le comportement dispersif des multicouches submicrométriques. / This thesis focuses on the study of multilayered and FGM systems (FGM : Functionnally Graded Materials). The main purpose of this type of materials is to obtain deposits with new and innovative features and to increase the fracture toughness. From now on, FGM have been used in various high technology applications.A multilayer system with a composition gradient of copper and nickel was studied experimentally by the application of the laser ultrasonics (LU) technique which was coupled to a theoretical study based on the ordinary differential equations (ODE) and the Stiffness Matrix Method (SMM). This PhD thesis is organized around four chapters. The first chapter is dedicated to a theoretical study of the propagation behavior of surface acoustic wave (SAW) in a multilayer system with à gradient of properties. Thus, the numerical methods developped for the piezoelectric materials (FGPM) are presented. The second chapter is devoted to describe the setup for making the samples used in this study which were obtained by sputtering technique. The third chapter presents the experimental study dedicated to the measurement of surface wave velocities in many crystal orientations. The last chapter of the manuscript presents experimental results, compared to the theoretical results, describing the dispersive behavior of submicrometer multilayers.