Stationary, spatially inhomogenous solutions of reaction-diffusion systems are studied in this thesis. These systems appears in biological models based on a Tu- ring's idea of a diffusion driven instability. In the connection, a global behaviour of bifurcation branches of these stationary solutions is analyzed. The thesis in- sists on theory of differential equations and on (particularly topological) methods of nonlinear analysis. The existence, as well as non-compatness in one-dimensional space, of a bifurcation branch of general reaction-diffusion system leading to Tu- ring's efekt is proved. Further, a priori estimates of Thomas model are derived. The results tend to theorem, that forall diffusion coefficient from the preestab- lished set there exists at least one stacionary, spacially nontrivial solution of Tho- mas model.

<p>There are many types of bioreactors used for producing bacteria populations in commercial, medical and research applications. </p><p>This report presents a systematic discussion of some of the most important models corresponding to the well known reproduction kinetics such as the Michaelis-Menten kinetics, competitive substrate inhibition and competitive product inhibition. We propose a modification of a known model, analyze it in the same manner as known models and discuss the most popular types of bioreactors and ways of controlling them. </p><p>This work summarises much of the known results and may serve as an aid in attempts to design new models.</p>

There are many types of bioreactors used for producing bacteria populations in commercial, medical and research applications. This report presents a systematic discussion of some of the most important models corresponding to the well known reproduction kinetics such as the Michaelis-Menten kinetics, competitive substrate inhibition and competitive product inhibition. We propose a modification of a known model, analyze it in the same manner as known models and discuss the most popular types of bioreactors and ways of controlling them. This work summarises much of the known results and may serve as an aid in attempts to design new models.

Severe declines in the number of some songbirds over the last 40 years have caused heated debate amongst interested parties. Many factors have been suggested as possible causes for these declines, including an increase in the abundance and distribution of an avian predator, the Eurasian sparrowhawk Accipiter nisus. To test for evidence for a predator effect on the abundance of its prey, we analyse data on 10 species visiting garden bird feeding stations monitored by the British Trust for Ornithology in relation to the abundance of sparrowhawks. We apply Bayesian hierarchical models to data relating to averaged maximum weekly counts from a garden bird monitoring survey. These data are essentially continuous, bounded below by zero, but for many species show a marked spike at zero that many standard distributions would not be able to account for. We use the Tweedie distributions, which for certain areas of parameter space relate to continuous nonnegative distributions with a discrete probability mass at zero, and are hence able to deal with the shape of the empirical distributions of the data. The methods developed in this thesis begin by modelling single prey species independently with an avian predator as a covariate, using MCMC methods to explore parameter and model spaces. This model is then extended to a multiple-prey species model, testing for interactions between species as well as synchrony in their response to environmental factors and unobserved variation. Finally we use a relatively new methodological framework, namely the SPDE approach in the INLA framework, to fit a multi-species spatio-temporal model to the ecological data. The results from the analyses are consistent with the hypothesis that sparrowhawks are suppressing the numbers of some species of birds visiting garden feeding stations. Only the species most susceptible to sparrowhawk predation seem to be affected.

En raison des relations complexes entre les variables des systèmes biologiques, l’hétérogénéité des données biologiques pose un défi pour leur modélisation par des modèles mathématiques et statistiques. En réponse, étant conçus pour traiter des données multiniveaux et bruitées, les modèles à effets mixtes deviennent de plus en plus populaires en modélisation quantitative de systèmes biologiques. L'objectif de cette thèse est de présenter l’application de modèles à effets mixtes à différents systèmes biologiques. Le deuxième chapitre de ce mémoire vise à déterminer la relation entre la cote de qualité du sirop d'érable, divers indicateurs de qualité couramment obtenus par les producteurs ainsi qu'un nouvel indicateur, le COLORI, et la concentration en acides aminés (AA). Pour cela, nous avons créé deux modèles à effets mixtes : le premier est un modèle ordinal qui prédit directement la cote de qualité du sirop d'érable en utilisant la transmittance, COLORI et AA ; le deuxième modèle est un modèle non linéaire qui prédit la concentration en AA en utilisant COLORI avec le pH comme approximation temporelle. Nos résultats montrent que la concentration en AA est un bon prédicteur de la qualité du sirop d'érable et que COLORI est un bon prédicteur de la concentration en AA. Le troisième chapitre traite de l’utilisation d’un modèle de la pharmacocinétique de population (PopPK) pour décrire la dynamique de l'estradiol dans un modèle de pharmacologie quantitative des systèmes (QSP) de la différenciation des cellules mammaires en cellules myoépithéliales afin de capturer l'hétérogénéité de la population de patients. Nous avons trouvé que la composante PopPK du modèle QSP n’a pas ajoutée de grande variation dans la dynamique de patients virtuels, ce qui suggère que le modèle QSP inclut intrinsèquement l'hétérogénéité. Dans l'ensemble, ce mémoire démontre l'application de modèles à effets mixtes au systèmes biologiques pour comprendre l'hétérogénéité des données biologiques. / Modelling biological systems with mathematical models has been a challenge due to the tendency for biological data to be heavily heterogeneous with complex relationships between the variables. Mixed effects models are an increasingly popular choice as a statistical model for biological systems since it is designed for multilevel data and noisy data. The aim of this thesis is to showcase the range of usage of mixed effects modelling for different biological systems. The second chapter aims to determine the relationship between maple syrup quality rating and various quality indicator commonly obtained by producers as well as a new indicator, COLORI, and amino acid (AA) concentration. For this, we created two mixed effects models: the first is an ordinal model that directly predicts maple syrup quality rating using transmittance, COLORI and AA; the second model is a nonlinear model that predicts AA concentration using COLORI with pH as a time proxy. Our models show that AA concentration is a good predictor for maple syrup quality, and COLORI is a good predictor for AA concentration. The third chapter involves using a population pharmacokinetics (PopPK) model to estimate estradiol dynamics in a quantitative systems pharmacokinetics (QSP) model for mammary cell differentiation into myoepithelial cells in order to capture population heterogeneity among patients. Our results show that the QSP model inherently includes heterogeneity in its structure since the added PopPK estradiol portion of the model does not add large variation in the estimated virtual patients. Overall, this thesis demonstrates the application of mixed effects models in biology as a way to understand heterogeneity in biological data.