A 13C-Análise de Fluxos Metabólicos (13C-MFA) tornou-se uma técnica de alta precisão para estimar fluxos metabólicos e obter informações importantes sobre o metabolismo. Este método consiste em procedimentos experimentais, técnicas de medição e em cálculos para análise de dados. Neste contexto, os grupos de pesquisa de engenharia metabólica necessitam de ferramentas computacionais precisas e adequadas aos seus objetos de estudo. No presente trabalho, foi construída uma ferramenta computacional na plataforma MATLAB que executa cálculos de 13C-MFA, com balanços de metabólitos e cumômeros. Além disso, um módulo para estimar os fluxos metabólicos e um módulo para quantificar as incertezas das estimativas também foram implementados. O programa foi validado com dados presentes na literatura e aplicado a estudos de caso. Na estimação de fluxos de Pseudomonas sp. LFM046, identificou-se que esse micro-organismo possivelmente utiliza a Via das Pentoses em conjunto com a Via Entner-Doudoroff para a biossíntese de Polihidroxialcanoato (PHA). No design ótimo de experimentos para uma rede genérica de Pseudomonas, identificou-se a glicose marcada no átomo cinco como um substrato que permitirá determinar o fluxo na Via das Pentoses com menor incerteza. / 13C-Metabolic Flux Analysis (13C-MFA) has become a high-precision technique to estimate metabolic fluxes and get insights into metabolism. This method consists of experimental procedures, measurement techniques and data analysis calculations. In this context, metabolic engineering research groups demand accurate and suitable computational tools to perform the calculations. A computational tool was implemented in MATLAB platform that performs 13C-MFA calculation, using metabolite and cumomer balances, as well as a module to estimate the fluxes and a module to quantify their uncertainty. The program was validated with some classical cases from literature. From the flux estimates of Pseudomonas sp. LFM046, it was identified that the microorganism possibly uses the Pentose Phosphate Pathway along with the Entner-Doudoroff Pathway for Polyhydroxyalkanoate (PHA) biosynthesis. From the optimal experimental design for a generic Pseudomonas network, it was possible to conclude that glucose labeled at atom five is the best option to determine the flux in the Pentose Phosphate Pathway with smaller uncertainty.

Huang Changquan. / "April 2002." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (p. 62-71). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

Neste trabalho, investigamos um algoritmo para estimação dos parâmetros de geradores síncronos baseado em análise de sensibilidade de trajetórias. Os parâmetros são estimados através da resolução de um problema de otimização não-linear de mínimos quadrados. Medidas são comparadas com as soluções obtidas dos modelos dinâmicos do gerador e o algoritmo busca minimizar a diferença entre as medidas e a saída do modelo matemático. As medidas foram obtidas de forma artificial por intermédios de simulações computacionais, admitindo-se não somente as dinâmicas transitórias da máquina, mas também considerando as dinâmicas sub-transitórias. O algoritmo proposto é adequado para medidas acessíveis em campo e permite estimar os parâmetros a partir de medidas de perturbações do sistema sem a necessidade da desconexão da máquina do sistema. A principal contribuição deste trabalho é a proposição de uma nova modelagem empregada para estimar os parâmetros do gerador síncrono. Para isto, propõe-se um modelo simplificado, modificado do modelo de dois eixos do gerador, que utiliza a corrente de campo do gerador como uma das entradas. Este modelo é constituído por um conjunto de equações algébrico-diferenciais (EADs) que contém uma equação algébrica de balanço de corrente. Esta equação elimina a necessidade de medidas de variáveis de difícil acesso. O algoritmo proposto foi testado com dados obtidos de simulações dinâmicas realizadas a partir de um sistema teste com resultados satisfatórios. Os resultados obtidos são analisados frente a resultados obtidos também para o modelo de dois eixos utilizando a tensão de campo como uma entrada. Através destes resultados é possível observar a possibilidade de sua utilização em aplicações reais. / In this work, we investigate an algorithm for estimating parameters of synchronous generators based on trajectories sensitivity analysis. The parameters are estimated by solving a nonlinear optimization problem of least squares. Measurements are compared with the solutions obtained from the dynamic model of the generator and the algorithm seeks to minimize the difference between the measurements and the output of the mathematical model. Measurements were obtained artificially by means of simulations, assuming not only the transient dynamics of the machine, but also considering the subtransient dynamics. The proposed algorithm is suitable for accessible measurements in the field and allows the estimation of parameters from measurements of system disturbances, without the necessity of disconnecting the machine from the system. The main contribution of this work is to propose a new generator model to estimate the parameters of the synchronous generator. To this end, a simplified model is proposed. This model is a modification of the two-axis model of the generator, which uses the generator field current as an input of the model. This model consists of a set of differential-algebraic equations (DAEs) containing an algebraic equation of balance of current. This equation eliminates the need of measuring variables that are difficult to access. The proposed algorithm has been tested with data obtained from dynamic simulations conducted from a test system with satisfactory results. The results has been analysed against the results of the two-axis model using the generator field voltage as an input of the model. These results indicate the possibility of application in real machines.

In this thesis we modify an existing model for the spread of resistant bacteria in a hospital. The existing model does not account for some of the trends seen in the data found in literature. The new model takes some of these trends into account. For the new model, we examine issues relating to identifiability, sensitivity analysis, parameter estimation, uncertainty analysis, and equilibrium stability.

abstract: A critical problem for airborne, ship board, and land based radars operating in maritime or littoral environments is the detection, identification and tracking of targets against backscattering caused by the roughness of the sea surface. Statistical models, such as the compound K-distribution (CKD), were shown to accurately describe two separate structures of the sea clutter intensity fluctuations. The first structure is the texture that is associated with long sea waves and exhibits long temporal decorrelation period. The second structure is the speckle that accounts for reflections from multiple scatters and exhibits a short temporal decorrelation period from pulse to pulse. Existing methods for estimating the CKD model parameters do not include the thermal noise power, which is critical for real sea clutter processing. Estimation methods that include the noise power are either computationally intensive or require very large data records. This work proposes two new approaches for accurately estimating all three CKD model parameters, including noise power. The first method integrates, in an iterative fashion, the noise power estimation, using one-dimensional nonlinear curve fitting, with the estimation of the shape and scale parameters, using closed-form solutions in terms of the CKD intensity moments. The second method is similar to the first except it replaces integer-based intensity moments with fractional moments which have been shown to achieve more accurate estimates of the shape parameter. These new methods can be implemented in real time without requiring large data records. They can also achieve accurate estimation performance as demonstrated with simulated and real sea clutter observation datasets. The work also investigates the numerically computed Cram\'er-Rao lower bound (CRLB) of the variance of the shape parameter estimate using intensity observations in thermal noise with unknown power. Using the CRLB, the asymptotic estimation performance behavior of the new estimators is studied and compared to that of other estimators. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2019

In this thesis we develop new mathematical models, using dynamical systems, to represent localized gene delivery of bone morphogenetic protein 2 into bone marrow-derived mesenchymal stem cells and rat calvarial defects. We examine two approaches, using pDNA or cmRNA treatments, respectively, towards the production of calcium deposition and bone regeneration in in vitro and in vivo experiments. We first review the relevant scientific literature and survey existing mathematical representations for similar treatment approaches. We then motivate and develop our new models and determine model parameters from literature, heuristic approaches, and estimation using sparse data. We next conduct a qualitative analysis using dynamical systems theory. Due to the nature of the parameter estimation, it was important that we obtain local and global sensitivity analyses of model outputs to changes in model inputs. Finally we compared results from different treatment protocols. Our model suggests that cmRNA treatments may perform better than pDNA treatments towards bone fracture healing. This work is intended to be a foundation for predictive models of non-viral local gene delivery systems.

System Lupus Erythematosus (SLE) is a chronic inflammatory autoimmune disorder that affects many parts of the body including skin, joints, kidneys, brains and other organs. Lupus Nephritis (LN) is a disease caused by SLE. Given the complexity of LN, we establish an optimal treatment strategy based on a previously developed mathematical model.For our thesis work, the model variables are: Immune Complexes (I), Pro-inflammatory mediators (P), Damaged tissue (D), and Anti-inflammatory mediators (A). The analysis in this research project focuses on analyzing therapeutic strategies to control damage using both parameter estimation techniques (integration of data to quantify any uncertainties associated with parameters) and optimal control with the goal of minimizing time spent on therapy for treating damaged tissue by LN.

In this work we have developed three ordinary differential equation models of biological systems: body mass change in response to exercise, immune system response to a general inflammatory stimulus, and the immune system response in atherosclerosis. The purpose of developing such computational tools is to test hypotheses about the underlying biological processes that drive system outcomes as well as possible real medical interventions. Therefore, we focus our analysis on understanding key interactions between model parameters and outcomes to deepen our understanding of these complex processes as a means to developing effective treatments in obesity, sarcopenia, and inflammatory diseases. We develop a model of the dynamics of muscle hypertrophy in response to resistance exercise and have shown that the parameters controlling response vary between male and female group means in an elderly population. We further explore this individual variability by fitting to data from a clinical obesity study. We then apply logistic regression and classification tree methods to the analysis of between- and within-group differences in underlying physiology that lead to different long-term body composition outcomes following a diet or exercise program. Finally, we explore dieting strategies using optimal control methods. Next, we extend an existing model of inflammation to include different macrophage phenotypes. Complications with this phenotype switch can result in the accumulation of too many of either type and lead to chronic wounds or disease. With this model we are able to reproduce the expected timing of sequential influx of immune cells and mediators in a general inflammatory setting. We then calibrate this base model for the sequential response of immune cells with peritoneal cavity data from mice. Next, we develop a model for plaque formation in atherosclerosis by adapting the current inflammation model to capture the progression of macrophages to inflammatory foam cells in response to cholesterol consumption. The purpose of this work is ultimately to explore points of intervention that can lead to homeostasis.

Les immunoessais en phase solide sont utilisés pour le diagnostic in vitro afin de détecter ou de quantifier une molécule dans un échantillon biologique. Ils s'appuient sur l'interaction spécifique entre un antigène et un anticorps. Habituellement, des anticorps spécifiques aux antigènes à détecter sont immobilisés sur une surface solide pour capturer les antigènes d'intérêt et les séparer du reste de l'échantillon.Lors du développement d'un immunoessai, la sensibilité, la spécificité et le temps d’analyse sont optimisés par le choix - classiquement empirique - de ligands, de supports solides, de débits,… Une meilleure compréhension et prédiction des interactions moléculaires complexes se produisant au cours d’un immunoessai seraient utiles pour : identifier les paramètres critiques des immunoessais, simplifier et accélérer le processus d’identification des meilleures conditions opératoires et améliorer les immunoessais existants.L'instrument VIDAS®, commercialisé par bioMérieux, est l'un des systèmes d’immunoessais les plus utilisés dans les laboratoires cliniques. Dans ce travail de thèse, deux outils expérimentaux basés sur la chromatographie inverse sont construits et testés. Un modèle prédictif de la cinétique d'interaction anticorps/antigène est développé. Les outils expérimentaux, fonctionnant dans des conditions très proches du VIDAS®, sont utilisés pour valider le modèle et estimer ses paramètres caractérisant les interactions anticorps/antigène à partir de courbes expérimentales. Dans l’avenir et à partir des résultats, un des outils expérimentaux associé au modèle pourra être utilisé par bioMérieux pour concevoir des systèmes d’immunoessais / Solid-phase immunoassays are used for in vitro diagnostic to detect the presence or measure the concentration of a molecule of interest in a biological sample. They rely on the specific interaction between an antigen and an antibody. Usually, antibodies specific to the antigens to be detected are immobilized on a solid surface to capture the antigens of interest and separate them from the rest of the sample components. During solid-phase immunoassay development, sensitivity, specificity and time-to-result need to be optimized through the choice of dedicated ligands, solid supports, flow rates,… Classically, these choices are made empirically. A better understanding and prediction of the complex molecular interactions that occur in the different steps of a diagnostic immunoassay is likely to be useful to: identify the critical parameters of immunoassays, simplify and speed-up the process of identification of the best immunoassay conditions and improve the immunoassays currently available. The VIDAS® instrument, commercialized by bioMérieux is one of the most widely used immunoassay system in clinical laboratories worldwide. In this PhD work, two experimental tools based on inverse chromatography are built and tested. A predictive model of antibody/antigen interaction kinetics in immunoassays is developed. The experimental tools which mimic VIDAS® process conditions are used to validate the predictive model and to estimate model parameters characterizing antibody/antigen interaction kinetics from experimental curves. In the future, based on the results, one of the experimental tools associated with the model could be used by bioMérieux to design immunoassay systems

High frequency time series data have become increasingly common. In many settings, such as the medical sciences or economics, these series may additionally display semi-reflective boundaries. These are boundaries, either physically existing, arbitrarily set, or determined based on inherent qualities of the series, which may be exceeded and yet based on probable consequences offer incentives to return to mid-range levels. In a lane control setting, Dawson, Cavanaugh, Zamba, and Rizzo (2010) have previously developed a weighted third-order autoregressive model utilizing flat, linear, and quadratic projections with a signed error term in order to depict key features of driving behavior, where the probability of a negative residual is predicted via logistic regression. In this driving application, the intercept (Λ0) of the logistic regression model describes the central tendency of a particular driver while the slope parameter (Λ1 ) can be intuitively defined as a representation of the propensity of the series to return to mid-range levels. We call this therefore the "re-centering" parameter, though this is a slight misnomer since the logistic model does not describe the position of the series, but rather the probability of a negative residual. In this framework a multi-step estimation algorithm, which we label as the Single-Pass method, was provided. In addition to investigating the statistical properties of the Single-Pass method, several other estimation techniques are investigated. These techniques include an Iterated Grid Search, which utilizes the underlying likelihood model, and four modified versions of the Single-Pass method. These Modified Single-Pass (MSP) techniques utilize respectively unconstrained least squares estimation for the vector of projection coefficients (Β), use unconstrained linear regression with a post-hoc application of the summation constraint, reduce the regression model to include only the flat and linear projections, or implement the Least Absolute Shrinkage and Selection Operator (LASSO). For each of these techniques, mean bias, confidence intervals, and coverage probabilities were calculated which indicated that of the modifications only the first two were promising alternatives. In a driving application, we therefore considered these two modified techniques along with the Single-Pass and Iterative Grid Search. It was found that though each of these methods remains biased with generally lower than ideal coverage probabilities, in a lane control setting they are each able to distinguish between two populations based on disease status. It has also been found that the re-centering parameter, estimated based on data collected in a driving simulator amongst a control population, is significantly correlated with neuropsychological outcomes as well as driving errors performed on-road. Several of these correlations were apparent regardless of the estimation technique, indicating real-world validity of the model across related assessments. Additionally, the Iterated Grid Search produces estimates that are most distinct with generally lower bias and improved coverage with the exception of the estimate of Λ1. However this method also requires potentially large time and memory commitments as compared to the other techniques considered. Thus the optimal estimation scheme is dependent upon the situation. When feasible the Iterated Grid Search appears to be the best overall method currently available. However if time or memory is a limiting factor, or if a reliable estimate of the re-centering parameter with reasonably accurate estimation of the Β vector is desired, the Modified Single-Pass technique utilizing unconstrained linear regression followed by implementation of the summation constraint is a sensible alternative.