Sudden cardiac death is one of the leading causes of mortality in the western world. One of the main factors is myocardial ischaemia, when there is a mismatch between blood demand and supply to the heart, which may lead to disturbed cardiac excitation patterns, known as arrhythmias. Ischaemia is a dynamic and complex process, which is characterised by many electrophysiological changes that vary through space and time. Ischaemia-induced arrhythmic mechanisms, and the safety and efficacy of certain therapies are still not fully understood. Most experimental studies are carried out in animal, due to the ethical and practical limitations of human experiments. Therefore, extrapolation of mechanisms from animal to human is challenging, but can be facilitated by in silico models. Since the first cardiac cell model was built over 50 years ago, computer simulations have provided a wealth of information and insight that is not possible to obtain through experiments alone. Therefore, mathematical models and computational simulations provide a powerful and complementary tool for the study of multi-scale problems. The aim of this thesis is to investigate pro-arrhythmic electrophysiological consequences of acute myocardial ischaemia, using a multi-scale computational modelling and simulation framework. Firstly, we present a novel method, combining computational simulations and optical mapping experiments, to characterise ischaemia-induced spatial differences modulating arrhythmic risk in rabbit hearts. Secondly, we use computer models to extend our investigation of acute ischaemia to human, by carrying out a thorough analysis of recent human action potential models under varied ischaemic conditions, to test their applicability to simulate ischaemia. Finally, we combine state-of-the-art knowledge and techniques to build a human whole ventricles model, in which we investigate how anti-arrhythmic drugs modulate arrhythmic mechanisms in the presence of ischaemia.

The motile behaviour of bacteria underlies many important aspects of their actions, including pathogenicity, foraging efficiency, and ability to form biofilms. In this thesis, we apply mathematical modelling and analysis to various aspects of the planktonic motility of flagellated bacteria, guided by experimental observations. We use data obtained by tracking free-swimming Rhodobacter sphaeroides under a microscope, taking advantage of the availability of a large dataset acquired using a recently developed, high-throughput protocol. A novel analysis method using a hidden Markov model for the identification of reorientation phases in the tracks is described. This is assessed and compared with an established method using a computational simulation study, which shows that the new method has a reduced error rate and less systematic bias. We proceed to apply the novel analysis method to experimental tracks, demonstrating that we are able to successfully identify reorientations and record the angle changes of each reorientation phase. The analysis pipeline developed here is an important proof of concept, demonstrating a rapid and cost-effective protocol for the investigation of myriad aspects of the motility of microorganisms. In addition, we use mathematical modelling and computational simulations to investigate the effect that the microscope sampling rate has on the observed tracking data. This is an important, but often overlooked aspect of experimental design, which affects the observed data in a complex manner. Finally, we examine the role of rotational diffusion in bacterial motility, testing various models against the analysed data. This provides strong evidence that R. sphaeroides undergoes some form of active reorientation, in contrast to the mainstream belief that the process is passive.

Current studies of the highly complex pathobiology and molecular signatures of human disease require the analysis of large sets of high-throughput data, from clinical to genetic expression experiments, containing a wide range of information types. A number of computational techniques are used to analyse such high-dimensional bioinformatics data. In this thesis we focus on the development of a novel soft clustering technique, DifFUZZY, a fuzzy clustering algorithm applicable to a larger class of problems than other soft clustering approaches. This method is better at handling datasets that contain clusters that are curved, elongated or are of different dispersion. We show how DifFUZZY outperforms a number of frequently used clustering algorithms using a number of examples of synthetic and real datasets. Furthermore, a quality measure based on the diffusion distance developed for DifFUZZY is presented, which is employed to automate the choice of its main parameter. We later apply DifFUZZY and other techniques to data from a clinical study of children from The Gambia with different types of severe malaria. The first step was to identify the most informative features in the dataset which allowed us to separate the different groups of patients. This led to us reproducing the World Health Organisation classification for severe malaria syndromes and obtaining a reduced dataset for further analysis. In order to validate these features as relevant for malaria across the continent and not only in The Gambia, we used a larger dataset for children from different sites in Sub-Saharan Africa. With the use of a novel network visualisation algorithm, we identified pathobiological clusters from which we made and subsequently verified clinical hypotheses. We finish by presenting conclusions and future directions, including image segmentation and clustering time-series data. We also suggest how we could bridge data modelling with bioinformatics by embedding microarray data into cell models. Towards this end we take as a case study a multiscale model of the intestinal crypt using a cell-vertex model.

This thesis is concerned with large-scale systems of Ordinary Differential Equations that model Biomolecular Reaction Networks (BRNs) in Systems and Synthetic Biology. It addresses the strategies of model reduction and decomposition used to overcome the challenges posed by the high dimension and stiffness typical of these models. A number of developments of these strategies are identified, and their implementation on various BRN models is demonstrated. The goal of model reduction is to construct a simplified ODE system to closely approximate a large-scale system. The error estimation problem seeks to quantify the approximation error; this is an example of the trajectory comparison problem. The first part of this thesis applies semi-definite programming (SDP) and dissipativity theory to this problem, producing a single a priori upper bound on the difference between two models in the presence of parameter uncertainty and for a range of initial conditions, for which exhaustive simulation is impractical. The second part of this thesis is concerned with the BRN decomposition problem of expressing a network as an interconnection of subnetworks. A novel framework, called layered decomposition, is introduced and compared with established modular techniques. Fundamental properties of layered decompositions are investigated, providing basic criteria for choosing an appropriate layered decomposition. Further aspects of the layering framework are considered: we illustrate the relationship between decomposition and scale separation by constructing singularly perturbed BRN models using layered decomposition; and we reveal the inter-layer signal propagation structure by decomposing the steady state response to parametric perturbations. Finally, we consider the large-scale SDP problem, where large scale SDP techniques fail to certify a system’s dissipativity. We describe the framework of Structured Storage Functions (SSF), defined where systems admit a cascaded decomposition, and demonstrate a significant resulting speed-up of large-scale dissipativity problems, with applications to the trajectory comparison technique discussed above.

Colorectal cancer (CRC) arises in part from the dysregulation of cellular proliferation, associated with the canonical Wnt pathway, and differentiation, effected by the Notch signalling network. In this thesis, we develop a mathematical model of ordinary differential equations (ODEs) for the coupled interaction of the Notch and Wnt pathways in cells of the human intestinal epithelium. Our central aim is to understand the role of such crosstalk in the genesis and treatment of CRC. An embedding of this model in cells of a simulated colonic tissue enables computational exploration of the cell fate response to spatially inhomogeneous growth cues in the healthy intestinal epithelium. We also examine an alternative, rule-based model from the literature, which employs a simple binary approach to pathway activity, in which the Notch and Wnt pathways are constitutively on or off. Comparison of the two models demonstrates the substantial advantages of the equation-based paradigm, through its delivery of stable and robust cell fate patterning, and its versatility for exploring the multiscale consequences of a variety of subcellular phenomena. Extension of the ODE-based model to include mutant cells facilitates the study of Notch-mediated therapeutic approaches to CRC. We find a marked synergy between the application of γ-secretase inhibitors and Hath1 stabilisers in the treatment of early-stage intestinal polyps. This combined treatment is an efficient means of inducing mitotic arrest in the cell population of the intestinal epithelium through enforced conversion to a secretory phenotype and is highlighted as a viable route for further theoretical, experimental and clinical study.

Cette thèse porte sur l'analyse mathématique de modèles de réaction-dispersion de la forme [delta]tu=D(u) +f(x,u). L'objectif est de comprendre l'influence du terme de réaction f, de l'opérateur de dispersion D, et de la donnée initiale u0 sur la propagation des solutions de ces équations. Nous nous sommes intéressés principalement à deux types d'équations de réaction-dispersion : les équations de réaction-diffusion où l'opérateur de dispersion différentielle est D=[delta]2z et les équations intégro-différentielles pour lesquelles D est un opérateur de convolution, D(u)=J* u-u. Dans le cadre des équations de réaction-diffusion en milieu homogène, nous proposons une nouvelle approche plus intuitive concernant les notions de fronts progressifs tirés et poussés. Cette nouvelle caractérisation nous a permis de mieux comprendre d'une part les mécanismes de propagation des fronts et d'autre part l'influence de l'effet Allee, correspondant à une diminution de la fertilité à faible densité, lors d'une colonisation. Ces résultats ont des conséquences importantes en génétique des populations. Dans le cadre des équations de réaction-diffusion en milieu hétérogène, nous avons montré sur un exemple précis comment la fragmentation du milieu modifie la vitesse de propagation des solutions. Enfin, dans le cadre des équations intégro-différentielles, nous avons montré que la nature sur- ou sous-exponentielle du noyau de dispersion J modifie totalement la vitesse de propagation. / This thesis deals with the mathematical analysis of reaction-dispersion models of the form [delta]tu=D(u) +f(x,u). We investigate the influence of the reaction term f, the dispersal operator D and the initial datum u0 on the propagation of the solutions of these reaction-dispersion equations. We mainly focus on two types of equations: reaction-diffusion equations (D=[delta]2z and integro-differential equations (D is a convolution operator, D(u)=J* u-u). We first investigate the homogeneous reaction-diffusion equations. We provide a new and intuitive explanation of the notions of pushed and pulled traveling waves. This approach allows us to understand the inside dynamics the traveling fronts and the impact of the Allee effect, that is a low fertility at low density, during a colonisation. Our results also have important consequences in population genetics. In the more general and realistic framework of heterogeneous reaction-diffusion equations, we exhibit examples where the fragmentation of the media modifies the spreading speed of the solution. Finally, we investigate integro-differential equations and prove that emph{fat-tailed} dispersal kernels J, that is kernels which decay slower than any exponentially decaying function at infinity, lead to acceleration of the level sets of the solution u.

The evolutionary options of a population are strongly influenced by the avail- ability of adaptive mutants. In this thesis, I use the concept of neutral networks to show that neutral drift can actually increase the accessibility of adaptive mu- tants, and therefore facilitate adaptive evolutionary change. Neutral networks are groups of unique genotypes which all code for the same phenotype, and are connected by simple point mutations. I calculate the size and shape of the networks in a small but exhaustively enumerated space of RNA genotypes by mapping the sequences to RNA secondary structure phenotypes. The qual- itative results are similar to those seen in many other genotype–phenotype map models, despite some significant methodological differences. I show that the boundary of each network has single point–mutation connections to many more phenotypes than the average individual genotype within that network. This means that paths involving a series of neutral point–mutation steps across a network can allow evolution to adaptive phenotypes which would otherwise be extremely unlikely to arise spontaneously. This can be likened to walking along a flat ridge in an adaptive landscape, rather than traversing or jumping across a lower fitness valley. Within this model, when a genotype is made up of just 10 bases, the mean neutral path length is 1.88 point mutations. Furthermore, the map includes some networks that are so convoluted that the path through the network is longer than the direct route between two sequences. A minimum length adaptive walk across the genotype space usually takes as many neutral steps as adaptive ones on its way to the optimum phenotype. Finally I show that the shape of a network can have a very important affect on the number of generations it takes a population to drift across it, and that the more routes between two sequences, the fewer generations required for a population to find an advantageous sequence. My conclusion is that, within the RNA map at least, the size, shape and connectivity of neutral networks all have a profound effect on the way that sequences change and populations evolve, and by not considering them, we risk missing an important evolutionary mechanism.

No description available.

Ticks are of global interest as the pathogens they spread can cause diseases that are of importance to both human health and economies. In Scotland, the most populous tick species is the sheep tick Ixodes ricinus, which is the vector of pathogens causing diseases such as Lyme borreliosis and Louping-ill. Recently, both the density and spread of I. ricinus ticks have grown across much of Europe, including Scotland, increasing disease risk. Due to the nature of the tick lifecycle they are particularly dependent on environmental factors, including temperature and habitat type. Because of this, the recent increase in tick-borne disease risk is believed to be linked to climate change. Many mathematical models have been used to explore the interactions between ticks and factors within their environments; this thesis begins by presenting a thorough review of previous modelling of tick and tick-borne pathogen dynamics, identifying current knowledge gaps. The main body of this thesis introduces an original mathematical modelling framework with the aim to further our understanding of the impact of climate change on tick-borne disease risk. This modelling framework takes into account how key environmental factors influence the I. ricinus lifecycle, and is used to create predictions of how I. ricinus density and disease risk will change across Scotland under future climate warming scenarios. These predictions are mapped using Geographical Information System software to give a clear spatial representation of the model predictions. It was found that as temperatures increase, so to do I. ricinus densities, as well as Louping-ill and Lyme borreliosis risk. These results give a strong indication of the disease risk implications of any changes to the Scottish environment, and so have the potential to inform policy-making. Additionally, the models identify areas of possible future research.

L'objectif de cette thèse est l'étude qualitative de certains modèles de la dynamique et la distribution des bactéries dans une rivière. Il s'agit de la stabilité des états stationnaires et l'existence des solutions périodiques. Nous considérons, dans la première partie de la thèse, un système d'équations différentielles ordinaires qui modélise les interactions et la dynamique de quatre espèces de bactéries dans une rivière. Nous avons étudié le comportement asymptotique des états stationnaires. L'étude de la stabilité des états stationnaires est essentiellement faite par la construction d'une fonction de Lyapunov combinée avec le principe d'invariance de LaSalle. D'autre part, l'existence des solutions périodiques est démontrée en utilisant le théorème de continuation de Mawhin. La deuxième partie de la thèse est consacrée à l'étude d'un système de convection-diffusion non-autonome. Ce modèle tient compte du transport des bactéries. Nous étudions l'analyse qualitative des solutions, nous déterminons l'ensemble limite du système et nous démontrons l'existence des états stationnaires positifs. L'étude de l'existence des états stationnaires (les seuls qu'il soit possible d'obtenir) est basée sur le théorème de Leray-Schauder. / The objective of this thesis is the qualitative study of some models of the dynamic and the distribution of bacteria in a river. We are interested in the stability of equilibria and the existence of periodic solutions. The thesis can be divided into two parts; the first part is concerned with a mathematical analysis of a system of differential equations modelling the dynamics and the interactions of four species of bacteria in a river. The asymptotic behavior of equilibria is established. The stability study of equilibrium states is mainly done by construction of Lyapunov functions combined with LaSalle's invariance principle. On the other hand, the existence of periodic solutions is proved under certain conditions using the continuation theorem of Mawhin. In the second part of this thesis, we propose a non-autonomous convection-reaction diffusion system with nonlinear reaction source functions. This model refers to the quantification and the distribution of antibiotic resistant bacteria (ARB) in a river. Our main contributions are : (i) the determination of the limit set of the system; it is shown that it is reduced to the solutions of the associated elliptic system; (ii) sufficient conditions for the existence of a positive solution of the associated elliptic system based on the Leray Schauder's degree theory.