Malfunction of the system which regulates the bloodflow in the brain is a major cause of stroke and dementia, costing many lives and many billions of pounds each year in the UK alone. This regulatory system, known as cerebral autoregulation, has been the subject of much experimental and mathematical investigation yet our understanding of it is still quite limited. One area in which our understanding is particularly lacking is that of the role of nitric oxide, understood to be a potent vasodilator. The interactions of nitric oxide with the better understood myogenic response remain un-modelled and poorly understood. In this thesis we present a novel model of the arteriolar control mechanism, comprising a mixture of well-established and new models of individual processes, brought together for the first time. We show that this model is capable of reproducing experimentally observed behaviour very closely and go on to investigate its stability in the context of the vasculature of the whole brain. In conclusion we find that nitric oxide, although it plays a central role in determining equilibrium vessel radius, is unimportant to the dynamics of the system and its responses to variation in arterial blood pressure. We also find that the stability of the system is very sensitive to the dynamics of Ca²⁺ within the muscle cell, and that self-sustaining Ca2+ waves are not necessary to cause whole-vessel radius oscillations consistent with vasomotion.

From the developing embryo to the evacuation of football stadiums, the migration and movement of populations of individuals is a vital part of human life. Such movement often occurs in crowded conditions, where the space occupied by each individual impacts on the freedom of others. This thesis aims to analyse and understand the effects of occupied volume (volume exclusion) on the movement of the individual and the population. We consider, as a motivating system, the rearrangement of individuals required to turn a clump of cells into a functioning embryo. Specifically, we consider the migration of cranial neural crest cells in the developing chick embryo. Working closely with experimental collaborators we construct a hybrid model of the system, consisting of a continuum chemoattractant and individual-based cell description and find that multiple cell phenotypes are required for successful migration. In the crowded environment of the migratory system, volume exclusion is highly important and significantly enhances the speed of cell migration in our model, whilst reducing the numbers of individuals that can enter the domain. The developed model is used to make experimental predictions, that are tested in vivo, using cycles of modelling and experimental work to give greater insight into the biological system. Our formulated model is computational, and is thus difficult to analyse whilst considering different parameter regimes. The second part of the thesis is driven by the wish to systematically analyse our model. As such, it concentrates on developing new techniques to derive continuum equations from diffusive and chemotactic individual-based and hybrid models in one and two spatial dimensions with the incorporation of volume exclusion. We demonstrate the accuracy of our techniques under different parameter regimes and using different mechanisms of movement. In particular, we show that our derived continuum equations almost always compare better to data averaged over multiple simulations than the equivalent equations without volume exclusion. Thus we establish that volume exclusion has a substantial effect on the evolution of a migrating population.

Der erste Teil dieser Arbeit konzentriert sich auf die Mechanismen hormoninduzierter Ca2+-oszillationen, und wie diese von Konzentrationsschwankungen des Ca2+-freisetzenden Botenstoffes Inositol-1,4,5-trisphosphat (IP3) beinflusst werden. Wir konnten zeigen, dass IP3-Oszillationen die Frequenzkodierung des äußeren Stimulus durch Ca2+-Ozillationen deutlich verstärken. Zwei Mechanismen für das Entstehen der IP3-Oszillationen wurden untersucht: es zeigte sich, dass die Aktivierung der Phospholipase C durch Ca2+ der wahrscheinlichste Mechanismus ist. Um die Rolle der IP3-Oszillationen genauer zu verstehen, wurde ein Modell für den Stoffwechsel des IP3-Vorläufers Phosphatidylinositol-4,5-bisphosphat (PIP2) entwickelt. Es zeigt sich, dass die scheinbar nutzlosen Phosphorylierungs/Dephosphorylierungszyklen eine wichtige Rolle für den PIP2-Haushalt spielen. Durch Nachliefern von PIP2 während der Stimulierung ermöglichen sie anhaltende Ca2+-signale. Der zweite Teil der Arbeit beschäftigt sich mit einem DNS-Reparaturweg, der Reparatur mittels Entfernung von Nukleotiden (NER). Dieser Reparaturmechanismus ist äußerst vielseitig und entfernt Pyrimidinpaare, die durch UV-Strahlung erzeugt wurden, oder Schäden, die durch chemische Agentien erzeugt wurden. Es wurde ein mathematisches Model erarbeitet, das die Grundeigenschaften der NER beschreiben soll. Erstens wurde untersucht, wie die Bindungs- und Freisetzungskinetik der Reparaturfaktoren mit den strukturellen Eigenschaften des Systems, beispielsweise der Bindungsreihenfolge, zusammenhängt. Zweitens wurden anhand von in vivo gemessenen Rekrutierungskinetiken dreier Proteinfaktoren die Modellparameter bestimmt. Das so angepasste Modell sagt unter anderem eine Sättigung der NER durch den Verbrauch des Erkennungsfaktors vorher. Die theoretischen Untersuchungen deuten darauf hin, dass ein sequentieller Anlagerungsmechanismus im Hinblick auf Effizienz und auf Spezifität gegenüber den beschädigten Substrat große Vorteile bringen kann. / The first part of this thesis focuses on the mechanisms of hormone induced Ca2+ oscillations and how these depend on fluctuations in the concentration of the Ca2+-releasing messenger, inositol 1,4,5-trisphosphate (IP3). We were able to show that IP3 oscillations greatly enhances the ability to frequency encode the hormone stimulus by Ca2+ oscillations. Two mechanisms for the generation of IP3-oscillations have been investigated, we could show that Ca2+-activation of phospholipase C is the most probable mechanism. To better understand the role of IP3-oscillations a detailed model for the phosphoinositide pathway has been developed. The model illustrates the importance of futile (de)phosphorylation cycles for regenerating phosphatidylinositol-4,5-bisphophat during stimulation, an essential property to support long-lasting Ca2+ signals. The second part of the thesis is devoted to nucleotide excision repair (NER). It is a versatile DNA repair mechanism that can remove lesions such as UV light induced pyrimidine dimers and bulky adducts caused by chemical agents. To understand the mechanisms underlying the protein assembly during NER and the performance of repair, a mathematical model, delineating hallmarks and general characteristics of NER, has been developed. First, the binding and dissociation kinetics of repair factors are related to the structural properties of the system, such as the sequential order in which the factors enter repair. Second, using in vivo kinetic data for the recruitment of three different proteins at local damaged nuclei, the model parameters are determined and the dynamic behavior of the repair process is scrutinized in detail. The observed saturation of NER is predicted to rely on the high engagement of the recognition factor in repair. The theoretical analysis of repair performance indicates that a sequential assembly process is remarkably advantageous in terms of repair efficiency and can show a marked selectivity for the damaged substrate.

In this thesis, I discuss two main subjects coming from biology and I propose two models that mimic the behaviours of the biological networks studied. The first part of the thesis deals with intracellular transport of molecules. Proteins, RNA and, generally, any kind of cargo molecules move freely in the cytoplasm: intracellular transport as a consequence of Brownian motion is classically modelled as a diffusion process. Some specific proteins, like the tumour suppressor p53, use microtubules to facilitate their way towards the nucleus. Microtubules are a dense network of filaments that point towards the cell centre. Motor proteins bind to these filaments and move along, bearing a cargo bound to them. I propose a simplified bi-dimensional model of nucleocytoplasmic transport taking into account the kinetic processes linked to microtubule transport. Unlike in other models we know, I represented the position of a single MT filament. This model is given by a system of partial differential equations which are cast in different dimensions and connected by suitable exchange rules. A numerical scheme is introduced and several scenarios are presented and discussed to answer to the question of which proteins benefit from microtubule transport, depending on their diffusion coefficients. In the second part of the thesis, I design and analyse a physiologically based model representing the accumulation of protein p53 in the nucleus after triggering of the sentinel protein ATM by DNA damage. The p53 protein plays an essential role in the physiological maintenance of healthy tissue integrity in multicellular organisms (regulation of cell cycle arrest, repair pathways and apoptosis). Firstly, I developed a compartmental ODE model to represent the temporal dynamics of the protein. Since the p53 protein is known for its oscillatory behaviour, I performed a numerical bifurcation study to verify the existence, in the model, of stable periodic solutions. Next, I have expanded the model by the addition of a spatial variable and analysed the spatio-temporal dynamics of p53. After checking the existence of oscillations in the spatial setting, I have analysed the robustness of the system under spatial variations (diffusion and permeability coefficients, cell shape and size).

Linear digital signal processing based around linear, time-invariant systems theory finds substantial application in speech processing. The linear acoustic source-filter theory of speech production provides ready biomechanical justification for using linear techniques. Nonetheless, biomechanical studies surveyed in this thesis display significant nonlinearity and non-Gaussinity, casting doubt on the linear model of speech production. In order therefore to test the appropriateness of linear systems assumptions for speech production, surrogate data techniques can be used. This study uncovers systematic flaws in the design and use of exiting surrogate data techniques, and, by making novel improvements, develops a more reliable technique. Collating the largest set of speech signals to-date compatible with this new technique, this study next demonstrates that the linear assumptions are not appropriate for all speech signals. Detailed analysis shows that while vowel production from healthy subjects cannot be explained within the linear assumptions, consonants can. Linear assumptions also fail for most vowel production by pathological subjects with voice disorders. Combining this new empirical evidence with information from biomechanical studies concludes that the most parsimonious model for speech production, explaining all these findings in one unified set of mathematical assumptions, is a stochastic nonlinear, non-Gaussian model, which subsumes both Gaussian linear and deterministic nonlinear models. As a case study, to demonstrate the engineering value of nonlinear signal processing techniques based upon the proposed biomechanically-informed, unified model, the study investigates the biomedical engineering application of disordered voice measurement. A new state space recurrence measure is devised and combined with an existing measure of the fractal scaling properties of stochastic signals. Using a simple pattern classifier these two measures outperform all combinations of linear methods for the detection of voice disorders on a large database of pathological and healthy vowels, making explicit the effectiveness of such biomechanically-informed, nonlinear signal processing techniques.

We review fold usage on completed genomes in order to explore protein structure evolution and assess the evolutionary relevance of current structural classification systems (SCOP and CATH). We assign folds on a set of 150 completed genomes using fold recognition methods (PSI-BLAST, SUPERFAMILY and Gene3D). The patterns of presence or absence of folds on genomes gives us insights into the relationships between folds and how we have arrived at the set of folds we see today. In particular, we develop a technique to estimate the relative ages of a protein fold based on genomic occurrence patterns in a phylogeny. We find that SCOP's `alpha/beta' class has relatively fewer distinct folds on large genomes, and that folds of this class tend to be older; folds of SCOP's `small protein' class follow opposite trends. Usage patterns show that folds with many copies on a genome are generally old, but that old folds do not necessarily have many copies. In addition, longer domains tend to be older and hydrophobic amino acids have high propensities for older folds whereas, polar - but non-charged - amino acids are associated with younger folds. Generally domains with stabilising features tend to be older. We also show that the reliability of fold recognition methods may be assessed using occurrence patterns. We develop a method, that detects false positives by identifying isolated occurrences in a phylogeny of species, and is able to improve genome wide fold recognition assignment sets. We use a structural fragment library to investigate evolutionary links between protein folds. We show that 'older' folds have relatively more such links than 'younger' folds. This correlation becomes stronger for longer fragment lengths suggesting that such links may reflect evolutionary relatedness.

Cette thèse s'inscrit dans le cadre de la modélisation de systèmes biologiques complexes. En premier lieu (travail en collaboration avec l'équipe de biologistes de Louis Casteilla), nous introduisons un modèle individus-centré pour étudier l'émergence de structures cellulaires de forme lobulaire dans un réseau organisé de fibres. Une étude paramétrique sur les résultats numériques ainsi que des méthodes de traitement d'images sur les données biologiques nous permettent de montrer que l'émergence de structures biologiquement cohérentes peut être reproduite par un modèle basé essentiellement sur des règles mécaniques entre les cellules et le réseau de fibres. L'originalité de ce modèle réside dans la modélisation de structures géométriques complexes (réseaux de fibres) par un ensemble d'unités élémentaires connectées interagissant à l'aide de fonctionnelles simples. Cette nouveauté a donné lieu à d'autres types de travaux en cours présentés comme perspectives directes de ce travail. Les deuxième et troisième parties portent sur la dérivation d'un modèle cinétique puis macroscopique pour étudier la matrice extra-cellulaire dans sa globalité. L'originalité de notre résultat réside dans l'obtention d'un système d'équations fermé décrivant l'évolution de la distribution des fibres individuelles et des liens de fibres. La limite hydrodynamique de l'équation cinétique est obtenue à l'aide de techniques non conventionnelles dû au un manque d'équation de conservation pour le système étudié. Dans le cas d'une densité homogène de fibres, nous prouvons l'existence de solutions au modèle macroscopique, et les simulations numériques montrent une bonne correspondance entre le modèle macroscopique et son homologue microscopique. Finalement (travail en collaboration avec S. Motsch), nous nous intéressons à l'influence d’interactions de type répulsion cellules-cellules dans un modèle de croissance tumorale. Nous montrons que le modèle macroscopique dérivé des équations microscopiques fait apparaître une instabilité et proposons une version modifiée de l'équation macroscopique que nous sommes capables de relier à la dynamique cellulaire. Les simulations numériques montrent la bonne correspondance entre les deux modèles. / In a first part (work in collaboration with the team of biologists of L. Casteilla) we propose an Individual based model for studying the emergence of lobule-like structures of cells in an organized fiber network. A parametric analysis on the numerical results as well as image processing methods on the biological images enable us to show that biologically-relevant structures can be reproduced by a model mostly based on cell-fiber mechanical interactions. The originality of this model mainly relies in the modelling of complex geometrical structures such as fiber networks as sets of connected elementary units interacting through simple functionals. This novelty has been used to build other types of models presented as direct perspective of this work. The second and third parts lie in the derivation of kinetic and macroscopic models for an interconnected fiber network, closely linked to the microscopic one. The originality of this work lies in the obtained closed system of two evolution equations: one for the distribution of individual fibers and one for the fiber links. In the case of homogeneous fiber density, we show existence of stationary solutions to the macroscopic equation, and numerical simulations show the good correspondence between the microscopic and macroscopic models. Finally (work in collaboration with S. Motsch), we are interested in the role of cell-cell interactions in the invasion properties (speed, geometry...) of a growing mass of cells. We show that the macroscopic model derived from the microscopic one features instabilities, and we propose a modified macroscopic model that we are able to link to the particle dynamics. The numerical simulations show the relevance of the macroscopic model to describe the microscopic dynamics at large scale.

In mathematical population genetics a multiple merger <i>n</i>-coalescent process, or <i>Λ</i> <i>n</i>-coalescent process, {<i>Πⁿ(t) t</i> ≥ 0} models the genealogical tree of a sample of size <i>n</i> (e.g. of DNA sequences) drawn from a large population of haploid individuals. We study various properties of <i>Λ</i> coalescents. Novel in our approach is that we introduce the partition lattice as well as cumulants into the study of functionals of coalescent processes. We illustrate the success of this approach on several examples. Cumulants allow us to reveal the relation between the tree height, <i>T_n</i>, respectively the total branch length, <i>L_n</i>, of the genealogical tree of Kingman’s <i>n</i>-coalescent, arguably the most celebrated coalescent process, and the Riemann zeta function. Drawing on results from lattice theory, we give a spectral decomposition for the generator of both the Kingman and the Bolthausen-Sznitman <i>n</i>-coalescent, the latter of which emerges as a genealogy in models of populations undergoing selection. Taking mutations into account, let <i>M_j</i> count the number of mutations that are shared by <i>j</i> individuals in the sample. The random vector (<i>M₁</i>,...,<i>M_n-1</i>), known as the site frequency spectrum, can be measured from genetical data and is therefore an important statistic from the point of view of applications. Fu worked out the expected value, the variance and the covariance of the marginals of the site frequency spectrum. Using the partition lattice we derive a formula for the cumulants of arbitrary order of the marginals of the site frequency spectrum. Following another line of research, we provide a law of large numbers for a family of <i>Λ</i> coalescents. To be more specific, we show that the process {<i>#Πⁿ(t), t</i> ≥ 0} recording the number <i>#Πⁿ(t)</i> of individuals in the coalescent at time <i>t</i>, coverges, after a suitable rescaling, towards a deterministic limit as the sample size <i>n</i> grows without bound. In the statistical physics literature this limit is known as a hydrodynamic limit. Up to date the hydrodynamic limit was known for Kingman’s coalescent, but not for other <i>Λ</i> coalescents. We work out the hydrodynamic limit for beta coalescents that come down from infinity, which is an important subclass of the <i>Λ</i> coalescents.

Mathematical Biology has found recent success applying Computational Fluid Dynamics (CFD) to model airflow in the human lung. Detailed modeling of flow patterns in the alveoli, where the oxygen-carbon dioxide gas exchange occurs, has provided data that is useful in treating illnesses and designing drug-delivery systems. Unfortunately, many CFD software packages have high licensing fees that are out of reach for independent researchers. This thesis uses three open-source software packages, Gmsh, OpenFOAM, and ParaView, to design a mesh, create a simulation, and visualize the results of an idealized terminal alveolar sac model. This model successfully demonstrates that OpenFOAM can be used to model airflow in the acinar region of the lung under biologically relevant conditions.

<p>Earth’s ecosystems emerge from interconnected biosphere, geosphere, and atmosphere processes. Changes to any one process ripple through the Earth system, affecting other processes. As global climate change continues, nitrogen deposition is anticipated to increase and precipitation is expected to have varied changes across the globe. These changes to the atmosphere and geosphere will have implications for the biosphere. Namely, vegetation will be impacted by changes to nutrient and precipitation regimes. Vegetation comprises the aggregate strategies of individual plants, which are also influenced by changes in nutrient and water availability. The responses of individual plants to nitrogen, water, and uncertainty are the main focus of this dissertation, as understanding those will be critical to scaling up to the aggregate.</p> <p> First, I describe a mathematical model that predicts grassland root and shoot biomass across carbon, nitrogen, and water gradients. The model simulates competition among plants by dynamically allocating carbon to either root or shoot growth depending on the growth strategy employed by the other plant. I show that the model accurately predicts root net primary productivity (NPP), but performs poorly for shoot and total NPP. At the biome scale, modeled NPP does not vary with water alone but rather water and nitrogen interact to influence NPP. Second, I conduct a greenhouse experiment using <em>Eragrostis capillaris</em> (L.) Nees to examine the predictions of the model mentioned above to answer the question: how do water and nitrogen affect fitness and biomass allocation in a drought-tolerant C4 grass? And ask: what is the nature of the relationship between water and nitrogen as resources? I show that water was important for increasing shoot and total biomass, but that root biomass and root:shoot ratio was influenced interactively by water and nitrogen as predicted by the model. I conclude that the nature of the relationship between water and nitrogen was that of either interacting or hemi-essential resources. That is, additional water was able to partially substitute for limited nitrogen to maintain biomass. Third, I explore how information theory can apply to plants that face uncertainty in resource availability and briefly review the types and sources of information and the mechanisms that plants use to perceive and respond to their environment. Overall, my framework posits that plants interpret information from their surroundings as an emergent property of distributed information processed by a network of cells. I end with a prospectus of directions for future research, including decoding signal from noise, storage of information, strategies to cope with information entropy, additional means of information transmission, and two-way information signaling with biotic partners. Finally, I use the information theory framework discussed above to answer the questions: can plants sense and respond to information entropy? I explore this question using data from an experiment which altered the temporal supply of nutrients and found no support that <em>P. sativum</em> can sense and respond to entropy. Understanding the relationships of water, nitrogen, and uncertainty is critical to predicting plant growth, especially as climate change continues to influence the global system.</p>