Dissertations / Theses on the topic 'Biological modelling'
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Lemon, A. P. "Modelling the biological membrane." Thesis, University of Bath, 1995. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.760688.
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Lumley, James Andrew. "Molecular modelling of biological activity." Thesis, University of Reading, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.393752.
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Luo, Yang. "Stochastic modelling in biological systems." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610145.
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Billing, Alison Emslie. "Modelling techniques for biological systems." Master's thesis, University of Cape Town, 1987. http://hdl.handle.net/11427/21917.
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The objective of this investigation has been to develop and evaluate techniques which are appropriate to the modelling and simulation of biological reaction system behaviour. The model used as the basis for analysis of modelling and simulation techniques is a reduced version of the biological model proposed by the IAWPRC Task Group for mathematical modell ing in wastewater treatment design. This limited model has the advantage of being easily manageable in terms of analysis and presentation of the simulation techniQues whilst at the same time incorporating a range of features encountered with biological growth applications in general. Because a model may incorporate a number of different components and large number of biological conversion processes, a convenient method of presentation was found to be a matrix format. The matrix representation ensures clarity as to what compounds, processes and react ion terms are to be incorporated and allows easy comparison of different models. In addition, it facilitates transforming the model into a computer program. Simulation of the system response first involves specifying the reactor configuration and flow patterns. With this information fixed, mass balances for each compound in each reactor can be completed. These mass balances constitute a set of simultaneous non-linear differential and algebraic eQuations which, when solved, characterise the system behaviour.
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Cotton-Barratt, Rebecca. "Modelling biological form in evolution." Thesis, University of Warwick, 2013. http://wrap.warwick.ac.uk/70973/.
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How are processes working at the individual level, the species level and the macro-ecological level connected? This thesis explores the theoretical and structural constraints on biological evolution. It does this by developing an evolutionary program to model biological form. This development was necessary as the existing models of evolution are poorly suited to modelling morphological constraint. The model of biological form developed in this thesis uses graphs to abstractly represent organisms and the relationships of their internal structure. We show that by increasing the number of degrees of freedom, or by increasing the ruggedness of the fitness landscape, higher levels of diversity are supported - particularly when there is strong directional selection. We explore whether meta-regulation is bounded in the model by using an analytical framework. We show that there is no analytical steady state, but that one can be induced in the model by selection effects. We find that a mixed strategy between increasing object complexity and increasing hierarchical complexity maximises the average degree of a vertex. This agrees with the evolutionary history of meta-regulation. We claim that the macro-ecological response to environmental perturbation is determined by both the characteristic time scale of mutation and the time scale of the environmental change. We show that for high amplitude changes the system can adapt provide the mutation time scale is smaller than the environmental change. We also show that low amplitude environmental changes cause rapid turnovers in species' diversity. Finally, we show that mass extinctions can be the result of species' interactions and background rates of extinction, and do not need large external perturbations to occur. This, combined with the results above, suggests that many of the trends seen over geologically long time periods can be explained as a result of the interacting processes at the individual and species level.
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Djordjilovic, Vera. "Graphical modelling of biological pathways." Doctoral thesis, Università degli studi di Padova, 2015. http://hdl.handle.net/11577/3424702.
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Biological pathways underlie the basic functions of a living cell. They are complex diagrams featuring genes, proteins and other small molecules, showing how they work together to achieve a particular biological effect. From a technical point of view, they are networks represented through a graph where genes and their connections are, respectively, nodes and edges of a graph.
The main research objective of this thesis is to develop a framework for simulating effects of gene silencing.
To this end, we propose a three step approach. First, we refine the structure of a pathway via our CK2 algorithm. Next, we assess the uncertainty in the refined structure. Finally, we simulate gene silencing through intervention analysis in causal graphical models. The proposed approach showed promising results when applied to the problem of predicting the effect of the knockdown of the nkd gene in Drosophila Melanogaster.I pathway biologici sono alla base del funzionamento delle cellule viventi. Tali pathway sono diagrammi complessi che coinvolgono geni, proteine e altre piccole molecole, mostrando come essi svolgano un ruolo congiunto nel raggiungimento di uno specifico effetto biologico. Da un punto di vista tecnico, questi network sono rappresentati mediante diagrammi dove i geni e le loro connessioni sono, rispettivamente, nodi e archi. Il principale obiettivo di questa ricerca è sviluppare una tecnica per simulare gli effetti del silenziamento genico. A tal fine, proponiamo un approccio basato su tre passi. Nel primo passo, raffiniamo la struttura di un pathway attraverso il nostro algoritmo CK2. In seguito, nel secondo passo, valutiamo l'incertezza nella struttura raffinata. Infine, nel terzo passo, simuliamo il silenziamento genico tramite intervention analysis nei modelli grafici causali. L'approccio proposto mostra risultati promettenti se applicato al problema della previsione dell'effetto del silenziamento del gene nkd della Drosophila Melanogaster.
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Hodgkinson, Arran. "Mathematical Methods for Modelling Biological Heterogeneity." Thesis, Montpellier, 2019. http://www.theses.fr/2019MONTS119.
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Les processus biologiques sont des phénomènes complexes, multi-échelles, présentant une hétérogénéité importante à travers l’espace, la structure et la fonction. De plus, ils impliquent des événements fortement corrélés et présentent des boucles de rétroaction à travers les échelles. Dans cette thèse, nous utilisons des représentations spatio-structuro-temporelles en grande dimension pour étudier l'hétérogénéité biologique à travers l'espace, la fonction biologique et le temps, et appliquons cette méthode à divers problèmes importants en biologie et en clinique.Nous commençons par introduire un nouveau cadre spatio-structuro-temporel, basé sur équations aux dérivées partielles, pour le cas d’un système biologique dont la fonction dépend de la dynamique dans le temps et l’espace des récepteurs membranaires, des ligands et du métabolisme. Afin d’étudier les solutions de ces équations, nous utilisons un schéma numérique de différences finies ainsi que divers résultats analytiques. Pour tester la validité de nos approches numériques nous prouvons un théorème sur la stabilité de notre schéma.Le cancer est un problème croissant pour la population mondiale, car ses taux d'incidence et sa résistance aux médicaments augmentent. D’abord nous modélisons l’invasion du cancer du sein agressif via sa capacité à produire des enzymes dégradant la matrice extracellulaire, et nous montrons la génération de structures spatiales anatomo-pathologiques difficiles à enlever par la chirurgie. Ensuite, nous développons des modèles mathématiques de tumeurs résistantes au traitement et appliquons ces modèles à la résistance aux thérapies ciblées (inhibiteurs de BRAF et de MEK) du mélanome cutané. Nous constatons que les tumeurs développent une résistance à la fois à travers des processus d'adaptations génétiques ou par le remodelage de leur métabolisme, mais montrons que seules les tumeurs métaboliquement plastiques manifestent une re-sensibilisation à ces thérapies. Enfin, via une approche basée sur des données d’expression en cellule unique (RNA-seq), nous montrons que la dynamique spatiale contribue à l'hétérogénéité tumorale et à la résistante aux traitements de façon liée au statut prolifératif des cellules cancéreuses.Nous appliquons nos méthodes à deux autres systèmes. Dans le contexte de la réponse immunitaire à l’infection virale, nous étudions la production et la dynamique spatiale de l’interféron (IFN) et l’apparent paradoxe de la conservation de molécules d’IFN avec affinités faibles et fortes. Nous constatons que les molécules IFN de faible affinité sont plus capables de se propager dans l'espace, alors que les molécules de haute affinité sont capables de maintenir le signal localement. L’addition de ligands de faible affinité à un système ne comprenant que des ligands de moyenne ou grande affinité peut entraîner une diminution de la charge virale d’environ 23%. Ensuite, nous explorons le contexte de la sélection sexuelle de l'apparence masculine dans l'évolution darwinienne. Nous constatons que les systèmes biologiques conservent les traits sélectionnés sexuellement, même si cela entraîne une diminution générale de la population.Enfin, nous introduisons deux autres techniques de modélisation: pour augmenter la dimensionnalité de notre approche, nous développons une approche pseudo-spectrale basée sur les polynômes de Chebyshev et l’appliquons au même scénario de résistance aux médicaments phénotypiques que ci-dessus. Ensuite, pour étudier un scénario coopératif dans lequel des cellules cancéreuses prolifératives et invasives sont co-injectées, induisant des comportements invasifs dans les cellules prolifératives, nous développons une nouvelle méthode de simulation combinant des automates cellulaires et systèmes d’agents. Nous trouvons que cette méthode est capable de reproduire les résultats de l'expérience de coinjection et d'autres expériences dans lesquelles des cellules ont été placées dans des micropistes de collagèneBiological processes are complex, multi-scale phenomena displaying extensive heterogeneity across space, structure, and function. Moreover, these events are highly correlated and involve feedback loops across scales, with nuclear transcription being effected by protein concentrations and vice versa, presenting a difficulty in representing these through existing mathematical approaches. In this thesis we use higher-dimensional spatio-structuro-temporal representations to study biological heterogeneity through space, biological function, and time and apply this method to various scenarios of significance to the biological and clinical communities.We begin by deriving a novel spatio-structuro-temporal, partial differential equation framework for the general case of a biological system whose function depends upon dynamics in time, space, surface receptors, binding ligands, and metabolism. In order to simulate solutions for this system, we present a numerical finite difference scheme capable of this and various analytic results connected with this system, in order to clarify the validity of our predictions. In addition to this, we introduce a new theorem establishing the stability of the central differences scheme.Despite major recent clinical advances, cancer incidence continues to rise and resistance to newly synthesised drugs represents a major health issue. To tackle this problem, we begin by investigating the invasion of aggressive breast cancer on the basis of its ability to produce extracellular matrix degrading enzymes, finding that the cancer produced a surgically challenging morphology. Next, we produce a novel structure in which models of cancer resistance can be established and apply this computational model to study genetic and phenotypic modes of resistance and re-sensitisation to targeted therapies (BRAF and MEK inhibitors). We find that both genetic and phenotypic heterogeneity drives resistance but that only the metabolically plastic, phenotypically resistant, tumour cells are capable of manifesting re-sensitisation to these therapies. We finally use a data-driven approach for single-cell RNA-seq analysis and show that spatial dynamics fuel tumour heterogeneity, contributing to resistance to treatment accordingly with the proliferative status of cancer cells.In order to expound this method, we look at two further systems: To investigate a case where cell-ligand interaction is particularly important, we take the scenario in which interferon (IFN) is produced upon infection of the cell by a virus and ask why biological systems evolve and retain multiple different affinities of IFN. We find that low affinity IFN molecules are more capable of propagating through space; high affinity molecules are capable of sustaining the signal locally; and that the addition of low affinity ligands to a system with only medium or high affinity ligands can lead to a ~23% decrease in viral load. Next, we explore the non-spatial, structuro-temporal context of male elaboration sexual and natural selection in Darwinian evolution. We find that biological systems will conserve sexually selected traits even in the event where this leads to an overall population decrease, contrary to natural selection.Finally, we introduce two further modelling techniques: To increase the dimensionality of our approach, we develop a pseudo-spectral Chebyshev polynomial-based approach and apply this to the same scenario of phenotypic drug resistance as above. Next, to deal with one scenario in which proliferative and invasive cancer cells are co-injected, inducing invasive behaviours in the proliferative cells, we develop a novel agent-based, cellular automaton method and associated analytic theorems for generating numerical solutions. We find that this method is capable of reproducing the results of the co-injection experiment and further experiments, wherein cells migrate through artificially produced collagen microtracks
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Fear, Elise Carolyn. "Modelling biological cells exposed to electric fields." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ32685.pdf.
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Liu, Dianbo. "Modelling biological networks : topology, dynamics and generation." Thesis, University of Dundee, 2017. https://discovery.dundee.ac.uk/en/studentTheses/8ab98533-d17f-4ea5-adb7-62b23d1e42bc.
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Lumbers, Jeremy. "Rotating biological contactors : mechanisms, modelling and design." Thesis, Imperial College London, 1988. http://hdl.handle.net/10044/1/47161.
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Romanel, Alessandro. "Dynamic Biological Modelling: a language-based approach." Doctoral thesis, Università degli studi di Trento, 2010. https://hdl.handle.net/11572/368272.
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Systems biology investigates the interactions and relationships among the components of biological systems to understand how they globally work. The metaphor “cells as computations†, introduced by Regev and Shapiro, opened the realm of biological modelling to concurrent languages. Their peculiar characteristics led to the development of many different bio-inspired languages that allow to abstract and study specific aspects of biological systems. In this thesis we present a language based on the process calculi paradigm and specifically designed to account for the complexity of signalling networks. We explore a new design space for bio-inspired languages, with the aim to capture in an intuitive and simple way the fundamental mechanisms governing protein-protein interactions. We develop a formal framework for modelling, simulating and analysing biological systems. An implementation of the framework is provided to enable in-silico experimentation.
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Palmisano, Alida. "Modelling and Inference Strategies for Biological Systems." Doctoral thesis, Università degli studi di Trento, 2010. https://hdl.handle.net/11572/368774.
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For many years, computers have played an important role in helping scientists to store, manipulate, and analyze data coming from many different disciplines. In recent years, however, new technological capabilities and new ways of thinking about the usefulness of computer science is extending the reach of computers from simple analysis of collected data to hypothesis generation.
The aim of this work is to provide a contribution in the Computational Systems Biology field. The main purpose of this recent discipline is to enhance the intertwined relationship connecting Biology and Computer Science, by developing tools and theoretical frameworks able to formally and quantitatively investigate the interactions among the components of biological systems. The final goal of these efforts is to assemble the different pieces into a working model of a living, responding, reproducing cell; a model that can be used for performing in-silico tests and simulations in order to understand and predict possible emergent properties.
In this thesis we present the application to real biological case studies of a specific concurrent modelling language (derived by the metaphors of molecules-as-object" -introduced by Fontana- and "cells-as-computations" -introduced by Regev and Shapiro- at the end of last century) and the development and implementation of a tool for inferring knowledge from experimental data in order to link the numerical aspects of a model to real wet-lab data."
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Yeste, Lozano Jose. "Microphysiological systems for modelling and monitoring biological barriers." Doctoral thesis, Universitat Autònoma de Barcelona, 2018. http://hdl.handle.net/10803/664204.
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Los sistemas microfisiológicos (MPS) son modelos in vitro microfabricados que
emulan las condiciones in vivo fisiológicamente relevantes, como la organización
celular y las señales microambientales. Las microtecnologías han permitido el
desarrollo de sofisticados MPS capaces de recapitular fielmente la fisiología a nivel de
tejido y órgano. Los MPS son particularmente útiles para modelar barreras biológicas,
es decir, epitelios y endotelios que separan la circulación sanguínea de los
compartimentos tisulares. Su función de barrera es crucial para mantener la homeostasis
en los órganos y su desregulación juega un papel importante en la fisiopatología de
muchas enfermedades humanas prevalentes. La función principal de un tejido barrera es
controlar el transporte transepitelial de solutos. Por lo tanto, la capacidad de cuantificar
el transporte en un modelo de barrera es crítico. La espectroscopía de impedancia
eléctrica (EIS) permite su cuantificación con las ventajas de ser no destructiva, sin
utilizar marcadores y de fácil aplicación en tiempo real. EIS puede determinar 1) la
resistencia eléctrica transepitelial (TEER), que evalúa la integridad de la barrera
(estrechamente relacionada con la rigidez del espacio intercelular); 2) la capacitancia de
la capa celular (Ccl), que puede proporcionar información sobre el área superficial de la
membrana; y 3) la contribución de la solución del medio a la impedancia.
Mientras que el EIS es fácil de realizar mediante electrodos extracelulares, es difícil
lograr la distribución de corriente uniforme requerida para mediciones precisas dentro
de los canales de cultivo celular miniaturizados. Entonces, se puede suponer
erróneamente que todo el área de cultivo de células contribuye igual a la medición, lo
que puede conducir a errores de cálculo del TEER. Esto puede explicar parcialmente la
gran disparidad de los valores de TEER reportados para tipos de células idénticas. Aquí,
se presenta un estudio numérico para dilucidar este problema en algunos cultivos
celulares previamente reportados y para proponer un factor de corrección geométrica
(GCF) que corrige este error y que permite aplicarse retrospectivamente. Este estudio
también se usó para optimizar una configuración tetrapolar especialmente adecuada para
realizar mediciones EIS precisas en canales microfluídicos, y lo que es más importante,
los electrodos cubren mínimamente la superficie lo que permite que las células se
puedan visualizar junto con el análisis de TEER. Posteriormente, se desarrolló una
cámara de perfusión modular con electrodos integrados en base a esta configuración
óptima. El dispositivo comprende una membrana porosa desechable en la que se forma
el tejido barrera y dos placas reutilizables donde se encuentran los electrodos. Por lo
tanto, el tejido en la membrana se puede ensamblar en el sistema para medirlo y
exponerlo al flujo, no solo para aplicar un estímulo mecánico fluidico sino también para
suministrar continuamente nutrientes y eliminar los desechos. Además, la concentración
de NaCl en ambos lados del tejido se puede estimar a partir de la conductancia eléctrica
medida con los mismos electrodos integrados en una configuración bipolar. Un modelo
in vitro del túbulo renal se utilizó para validar el sistema de medición. Como resultado,
la concentración de NaCl se estimó a partir de la conductancia que permite la medición
en línea del gradiente químico transepitelial de NaCl, que es una función primaria del
túbulo renal.
El desarrollo de MPS con múltiples barreras biológicas interconectadas expandirá
la tecnología para recapitular funciones más complejas a nivel de órgano. Sin embargo,
existen múltiples desafíos técnicos para reproducir varias barreras biológicas en un solo
dispositivo mientras se mantiene un microambiente controlado particular para cada tipo
de célula. Aquí se presenta un novedoso dispositivo microfluídico donde 1) múltiples
tipos de células que están dispuestas en compartimentos uno al lado del otro están
interconectadas con microsurcos y donde 2) múltiples tejidos barrera se miden a través
de electrodos metálicos que están enterrados debajo de los microsurcos. Como prueba
de concepto, el dispositivo se usó para imitar la estructura de la barrera hematorretiniana
(BRB), incluidas las barreras interna y externa. Ambas barreras se formaron con éxito
en el dispositivo y se monitorearon en tiempo real, lo que demuestra su gran potencial
para su aplicación a la tecnología de órgano en un chip.Microphysiological systems (MPS) are biologically inspired microengineered in vitro models that emulate physiologically relevant in vivo conditions, such as cell organization and microenvironmental cues. Microtechnologies have enabled the development of significant MPS that are able to faithfully recapitulate tissue- and organ-level physiology. MPS are particularly useful for modelling biological barriers, that is, epithelia and endothelia that separate the blood circulation from tissue compartments. Their barrier function is crucial to maintain organ homeostasis and their deregulation play an important role in the pathophysiology of many prevalent human diseases. The primary function of a barrier tissue is to control the transepithelial transport of solutes. Therefore, the ability to quantify transport in a barrier model is critical. Electrical impedance spectroscopy (EIS) permits its quantification with the advantages of being non-destructive, label-free, and easily applicable in real time. EIS can determine 1) the transepithelial electrical resistance (TEER), which evaluates the barrier integrity (closely related with the tightness of the intercellular space); 2) the cell layer capacitance (Ccl), which can yield information about the membrane surface area; and 3) the contribution of the medium solution to the impedance. While EIS is easy to carry out by means of extracellular electrodes, it is challenging to achieve the uniform current distribution required for accurate measurements within miniaturized cell culture channels. Then, it may be erroneously assumed that the entire cell culture area contributes equally to the measurement leading to TEER calculation errors. This can partially explain the large disparity of TEER values reported for identical cell types. Here, a numerical study is presented to elucidate this issue in some cell cultures previously reported and to propose a geometric correction factor (GCF) to correct this error and be applied retrospectively. This study was also used to optimize a tetrapolar configuration especially suitable for performing accurate EIS measurements in microfluidic channels; importantly, it implements minimal electrode coverage so that the cells can be visualised alongside TEER analysis. A modular perfusion chamber with integrated electrodes was developed based on this optimal configuration. The device comprises a disposable porous membrane where the barrier tissue is formed and two reusable plates where the electrodes are located. Therefore, the tissue on the membrane can be assembled into the system to be measured and exposed to flow—not only to apply a fluid mechanical stimuli but also to continuously supply nutrients and remove waste. Additionally, the concentration of NaCl in both sides of the tissue can be estimated from the electrical conductance measured with the same integrated electrodes in a bipolar configuration. An in vitro model of the renal tubule was used to validate the measurement system. As a result, the concentration of NaCl was estimated from the conductance enabling in-line measurement of the transepithelial chemical gradient of NaCl, which is a primary function of the renal tubule. The development of MPS with multiple interconnected biological barriers will expand the technology to recapitulate more complex organ-level functions. Unfortunately, there are multiple technical challenges to reproduce several biological barriers in a single device while maintaining a particular controlled microenvironment for each cell type. Here, it is presented a novel microfluidic device where 1) multiple cell types that are arranged in side-by-side compartments are interconnected with microgrooves and where 2) multiple barrier tissues are measured through metal electrodes that are buried under the microgrooves. As a proof-of-concept, the device was used to mimic the structure of the blood-retinal barrier (BRB) including the inner and the outer barriers. Both barriers were successfully formed in the device and monitored in real time, demonstrating its great potential for application to organ-on-achip technology.
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Dreiwi, Hanan Ali. "Using transfer function analysis in modelling biological invasions." Thesis, University of Exeter, 2012. http://hdl.handle.net/10036/3749.
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This thesis is made up of seven chapters and two appendices. Chapter 1 provides an introduction whilst Chapter 7 o ers a conclusion. In Chapter 2 we provide preliminaries on population projection models and robustness analysis. In Chapter 3 we introduce a stage-structured model in a context of biological invasions. Using a Transfer Function Approach, we provide a detailed analysis of the invasion model where the existence and local stability of all possible equilibria are characterised in terms of the underlying parameters of the model. In Chapter 4, a Lyapunov function approach is used to estimate the basin of attraction for each equilibrium. In Chapter 5, harvesting is incorporated into the model and we speci cally examine the e ect of harvesting on whether one or both of the species are eliminated. In Chapter 6 we introduce a novel technique to measure the possibility of invasion in non-normal systems where the traditional invasion exponent technique is unreliable.
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Basse, Britta. "Case studies in mathematical modelling for biological conservation." Thesis, University of Canterbury. Mathematics & Statistics, 1999. http://hdl.handle.net/10092/4804.
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The use of mathematical modelling as a tool for investigating selected topics in conservation
biology is the focus of this thesis.
A continuous system of partial and ordinary differential equations model the age structured
population dynamics of a cohort of endemic, threatened New Zealand North Island
brown kiwi, Apteryx mantelli. Critical predation and recruitment rates of immature birds are
estimated. Stoats, Mustela erminea, are the main predator of immature kiwi. A refinement
to the model allows the calculation of acceptable stoat densities. In order to reduce stoats
to this critical density, a linear system of ordinary differential equations, representing an
acute secondary poisoning regime, is solved. An optimal secondary poisoning scheme, which
minimises the number of prey poisoned and the amount of poison used, is found. The minimum
area required for pest control is estimated by simulating the dispersal of sub-adult kiwi
using a discrete random walk approach. Simulations and a discrete age structured model are
used to investigate pulsed management strategies for both kiwi and kokako, Callaeas cinerea
wilsoni. Finally, a two dimensional discrete random walk is generalised and a continuous
diffusion equation is derived. A diffusion equation is incorporated into a S1 R (Susceptible,
Infected, Recovered) model representing the natural spread of Rabbit Haemorrhagic Disease
from a point source in rabbit, Oryctolagus cuniculus cuniculus, populations. The speed
for the virus, dependant on certain model parameters, is found and the minimum initial
population density, below which the wave of infection will not travel, is estimated.
All specific models discussed throughout the thesis are generic by nature and can be
applied to a diverse range of subjects.
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Bernardini, Francesco. "Membrane systems for molecular computing and biological modelling." Thesis, University of Sheffield, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.425607.
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Thompson-Walsh, Christopher David. "Semantics and extension of a biological modelling language." Thesis, University of Cambridge, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648266.
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Sorokina, Oxana. "Understanding biological timing by modelling simple circadian clocks." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/14456.
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Pinto, Mark Alexander. "Modelling of biological systems using multidimensional population balances." Thesis, Imperial College London, 2008. http://hdl.handle.net/10044/1/8502.
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Biological systems are intrinsically heterogeneous and, consequently, their mathematical descriptions should account for this heterogeneity as it often influences the dynamic behaviour of the individual cells. For example, in the cell cycle dependent production ofproteins, it is necessary to account for the distribution of the individual cells with respect to their position in the cell cycle as this has a strong influence on protein production. A second notable example is the formation of cancerous cells. In this case, the failure of regulatory mechanisms results in the transition of somatic cells to their cancerous state. Therefore, in developing the corresponding mathematical model, it is necessary to consider both the different states of the cells as well as their regulation. In this regard, the population balance equation is the ideal mathematical framework to capture cell population heterogeneity as it elegantly takes into account the distribution of cell populations with respect to their intracellular state together with the phenomena of cell birth, division, differentiation and recombination. Recent developments in solution algorithms together with the exponential increase in computational abilities now permit the efficient solution of one-dimensional population balance models which attribute the heterogeneity of cell populations to differences in the age or mass of individual cells. The inherent complexity of biological systems implies that the differentiation of cells based on a single characteristic alone may not be sufficient to capture the underlying biological phenomena. Therefore, current research is focussing on the development of multi-dimensional population balances that consider the differentiation of cells based on multiple characteristics, most notably, the state of cells with respect to key intracellular metabolites. However, conventional numerical techniques are inefficient for the solution of the formulated population balance models and this warrants the development of novel, tailor-made algorithms. This thesis presents one such solution algorithm and demonstrates its application to the study of several biological systems. The algorithm developed herein employs a finite-volume technique to convert the partial-differential equation comprising the population balance model into a set of ordinary differential equations. A two-tier technique based on the solution technique for inhomogeneous differential equations is then developed to solve the system of ordinary differential equations. This approach has two main advantages: (a) the decomposition technique considerably reduces the stiffness of the system of equations enabling more efficient solution, and (b) semianalytical solutions for the integrals employed in the modelling of cell division and differentiation can be obtained further reducing computation times. Further improvements in solution efficiency are obtained by the formulation of a two-level discretisation algorithm. In this approach, processes such as cell growth which are more sensitive to the discretisation are solved using a fine grid whereas less sensitive processes such as cell' division - which are usually more computationally expensive - are solved using a coarse grid at a higher level. Thus, further improvements are obtained in the efficiency of the technique. The solution algorithm is applied to various multi-dimensional population balance models of biological systems. The technique is first demonstrated on models of oscillatory dynamics in yeast glycolysis, cell-cycle related oscillations in eukaryotes, and circadian oscillations in crayfish. A model of cell division and proliferation control in eukaryotes is an example of a second class of problems where extracellular phenomena influence the behaviour of cells. As a third case for demonstration, a hybrid model of biopolymer accumulation in bacteria is formulated. In this case, cybernetic modelling principles are used to account for intracellular competitions while the population balance framework takes into consideration the heterogeneity of the cell population. Another important aspect in the formulation ofmulti-dimensional population balances is the development of the intracellular models themselves. While research in the biological sciences is permitting the formulation of detailed dynamic models of various bioprocesses, the accurate estimation of the kinetic parameters in these models can be difficult due to the unavailability of sufficient experimental data. This can result in considerable parametric uncertainty as is demonstrated on a simple cybernetic' model of biopolymer accumulation in bacteria. However, it is shown that, via the use of systems engineering tools, experiments can be designed that permit the accurate estimation of all model parameters even when measurements pertaining to all modelled quantities are unavailable.
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Pitcher, Jannette. "Modelling biological responses to environmental variables in wetlands." Thesis, Pitcher, Jannette (1999) Modelling biological responses to environmental variables in wetlands. PhD thesis, Murdoch University, 1999. https://researchrepository.murdoch.edu.au/id/eprint/42298/.
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Eutrophication and hydrologic changes resulting from increased agricultural and industrial activities have contributed to the degradation and loss of wetlands worldwide. The need to understand the response of wetland components to changing environmental conditions and to assess and predict ecological consequences of these changes has therefore intensified. Ecological models are proving to be valuable tools in managing these threatened ecosystems.
A dynamic, spatially-explicit simulation model was developed to study the effects of different environmental conditions on the dynamics of wetland vegetation. Factors important to vegetation dynamics included in the model are: water regime, sediment type, light and nutrient availability, and competition for resources. The model uses an individual-based modelling approach, in which vegetation communities are simulated as collections of individual plants on a transect through a wetland.
Plants included in the model are common in wetlands on the Swan Coastal Plain near Perth, Western Australia. They include two emergent macrophytes, Baumea articulata and Typha orientalis and a .fringing tree species, Melaleuca preissiana. Emergent macrophytes are characterised by life history characteristics, responses to environmental conditions and morphological attributes. Plants grow according to the resources (light, nutrients and water) they acquire, and photosynthates are allocated according to plant species, developmental stage and environmental conditions.
The model is generic and can be used at different wetlands. A range of abiotic and biotic data is required. Input data include simulation run time, transect dimensions, elevation, nutrient, water, temperature and light levels, soil type and distribution, and vegetation composition and distribution. The model provides spatial and temporal information on the responses of wetland vegetation on a transect basis (e.g. plant distribution, biomass, number and type of plants) and on an individual plant basis (e.g. height, leaf area and biomass).
The model was validated by comparing model results with field and remote sensing data, and information from the literature. Several scenarios were simulated, including: 1) potential effects of different environmental conditions (e.g. water, light and nutrient regimes); 2) historical events; 3) impacts of different management strategies; and 4) development of artificial wetlands under differing planting densities and environmental conditions. Detailed results are presented for the scenarios simulated. In general, model results from the scenarios simulated were in good agreement with field and remote sensing data and information from the literature.
The model has been successfully applied to studying the response of individual plants and plant communities under different environmental conditions. It provided accurate and realistic representations of the compositional and distribution vegetation changes associated with different management strategies and environmental conditions.
The usefulness of the model was demonstrated for studying responses of vegetation to environmental change, and for improving understanding of wetland vegetation dynamics. The model therefore has important implications for the conservation and management of wetland vegetation.
The model was developed to simulate the dynamics of wetland vegetation and is proving to be an effective tool for studying the spatial and temporal responses of wetland vegetation to natural and human-induced environmental changes.
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Hall, Cameron Luke. "Modelling of some biological materials using continuum mechanics." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/37244/1/Cameron_Hall_Thesis.pdf.
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Continuum mechanics provides a mathematical framework for modelling the physical stresses experienced by a material. Recent studies show that physical stresses play an important role in a wide variety of biological processes, including dermal wound healing, soft tissue growth and morphogenesis. Thus, continuum mechanics is a useful mathematical tool for modelling a range of biological phenomena. Unfortunately, classical continuum mechanics is of limited use in biomechanical problems. As cells refashion the �bres that make up a soft tissue, they sometimes alter the tissue's fundamental mechanical structure. Advanced mathematical techniques are needed in order to accurately describe this sort of biological `plasticity'. A number of such techniques have been proposed by previous researchers. However, models that incorporate biological plasticity tend to be very complicated. Furthermore, these models are often di�cult to apply and/or interpret, making them of limited practical use. One alternative approach is to ignore biological plasticity and use classical continuum mechanics. For example, most mechanochemical models of dermal wound healing assume that the skin behaves as a linear viscoelastic solid. Our analysis indicates that this assumption leads to physically unrealistic results. In this thesis we present a novel and practical approach to modelling biological plasticity. Our principal aim is to combine the simplicity of classical linear models with the sophistication of plasticity theory. To achieve this, we perform a careful mathematical analysis of the concept of a `zero stress state'. This leads us to a formal de�nition of strain that is appropriate for materials that undergo internal remodelling. Next, we consider the evolution of the zero stress state over time. We develop a novel theory of `morphoelasticity' that can be used to describe how the zero stress state changes in response to growth and remodelling. Importantly, our work yields an intuitive and internally consistent way of modelling anisotropic growth. Furthermore, we are able to use our theory of morphoelasticity to develop evolution equations for elastic strain. We also present some applications of our theory. For example, we show that morphoelasticity can be used to obtain a constitutive law for a Maxwell viscoelastic
uid that is valid at large deformation gradients. Similarly, we analyse a morphoelastic model of the stress-dependent growth of a tumour spheroid. This work leads to the prediction that a tumour spheroid will always be in a state of radial compression and circumferential tension. Finally, we conclude by presenting a novel mechanochemical model of dermal wound healing that takes into account the plasticity of the healing skin.
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Ellery, Adam J. "Modelling transport through biological environments that contain obstacles." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/106798/1/Adam_Ellery_Thesis.pdf.
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Transport through biological environments that are densely crowded with obstacles is often classified as anomalous, rather than Fickian diffusion. Researchers often describe these transport processes using either a random walk model or a fractional order differential equation model. To explore these ideas, we simulate transport through a crowded environment that is populated by impenetrable immobile obstacles. Our work suggests that it may be inappropriate to model transport through a crowded environment using these standard approaches.
We develop a new analytical method for modelling the transport of an agent through a crowded environment. Using our new method, we calculate the exact long-time diffusivity as well as the crossover time, which is the time scale required for the transport process to effectively become Fickian. Finally, we extend our new model to include interactions between the motile agent and the obstacles such as adhesion and repulsion.
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De, Haast James Andrew. "Modelling South African cold-water coral habitats." Master's thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/31361.
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Cold-water corals are found globally but little is known about energy flows associated with these habitats. The degree to which the benthic ecosystems containing cold-water corals are linked to the overlying pelagic ecosystems is also poorly understood. Observational studies have indicated that fish abundance is greater in the waters surrounding coldwater coral reefs compared to nearby waters over barren seafloor, implying enhanced productivity in the cold-water coral ecosystems. Support for this hypothesis is tested in this study using a customised Ecopath with Ecosim model. The model is applied to Childs Bank, a region on the west coast of South Africa located in the southern Benguela eastern boundary ecosystem. A previously constructed Ecopath model of the southern Benguela was modified to represent the main groups of organisms found on Childs Bank and additional groups were added to better represent the main groups associated with cold-water coral. In total, including the additional compartments, the model ecosystem consisted of 34 living compartments and three non-living compartments. Three novel living compartments were considered in the model: Cold-water corals, Sponges and Tube-worms. Two additional non-living compartments comprised Coral skeleton and Coral mucus. The Ecopath model was balanced by assuming that the three additional living groups had the same production to biomass ratios as the Macrobenthos group. The production to consumption ratio of Sponges and Cold-water corals were sourced from literature. An unconstrained non-linear minimisation function was used to solve for the biomass of the Sponge and Cold-water coral groups as their production was needed for the Ecopath model to balance; thus a biomass estimate was required for both these groups. The balanced Ecopath model was used in an Ecosim model, which was applied to three scenarios designed to investigate whether trophic links in the cold-water coral ecosystems could account for increased fish abundance: scenario 1, the removal of both cold-water coral and cold-water coral skeleton; scenario 2, changes in fishing pressure on small pelagic fish; scenario 3, the removal of coldwater coral skeleton without damage to the living coral. Scenario 3 is an artificial scenario designed to isolate the effects of cold-water coral skeleton loss from the trophic interactions from the living cold-water corals. None of the scenarios produced results with notable changes in biomasses of groups in the wider ecosystem. It is thus hypothesised that enhanced fish production results from cold-water corals changing the local oceanographic conditions through their physical structure rather than primarily by their trophic interactions.
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Myerscough, Mary Ruth. "A chemotactic model of biological pattern formation." Thesis, University of Oxford, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.329983.
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Lee, Jae-Young. "A coupled physical-biological model for the Clyde Sea." Thesis, Edinburgh Napier University, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.247319.
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Bown, James Louis. "Issues of scale in individual-based models : applications in fungal and plant community dynamics." Thesis, Abertay University, 2000. https://rke.abertay.ac.uk/en/studentTheses/87bed9b3-454c-48ac-bbf6-ca6058179af8.
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The central question addressed in this thesis is whether descriptions of the dynamics of ecological systems at one scale may be effectively used as descriptions of the dynamics of ecological systems at larger scales. This question is addressed in the context of the dynamics of fungal communities. A simple experimental system and complementary theoretical approach, in the form of an individual-based (cellular automaton) model, is presented. Experimental results derived from small-scale systems are used to quantify parameters of the model; results from large-scale experimental systems serve to test the model. The theoretical analyses clearly demonstrate that the dynamics observed are a result of both local and non-local features of the experimental system. In cases such as this the immediate extrapolation of results derived from xperiments conducted out of the context of the community to represent system scale behaviour is not possible. In response to this observation a generic framework is developed to allow the consideration of effects at a range of scales through contextual parameterisation of localised dynamics. The framework is directed toward plant systems where a large body of experimental data exists, and may be parameterised by that experimental data. It represents the essential features of individual interactions in terms of competition for space and resource, and the behaviour of a given plant is described in terms of functional traits. Model runs demonstrate complex community patterns suggestive of a known biological phenomena, succession, that arises as a consequence of the coupling between the community and environment. This coupling may allow the long-term coexistence of species through some particular balance in individual function (traits) across the community. A search mechanism is determined to allow combinations of trait values at the scale of the individual to be assessed for a particular community-scale phenomenon. Initial results demonstrate that this mechanism may identify and converge on combinations of trait values that give rise to, in this case, a simple measure of diversity. The manner in which the generic framework developed may be applied to further the investigation into fungal community dynamics is addressed.
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Iyaniwura, Sarafa Adewale. "Mathematical modelling of partially absorbing boundaries in biological systems." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/58907.
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This project presents a mathematical framework for identifying partially permeable biological boundaries, and estimating the rate of absorption of diffusing objects at such a boundary based on limited experimental data. We used partial differential equations (PDEs) to derive probability distribution functions for finding a particle performing Brownian motion in a region. These distribution functions can be fit to data to infer the existence of a boundary. We also used the probability distribution functions together with maximum likelihood estimation to estimate the rate of absorption of objects at the boundaries, based on simulated data. Furthermore, we consider a switching boundary and provide a technique for approximating the boundary with a partially permeable boundary.Science, Faculty of
Graduate
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Liao, Shuohao. "High-dimensional problems in stochastic modelling of biological processes." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:2d710a16-e790-47eb-8670-a4dcdd86f143.
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Stochastic modelling of gene regulatory networks provides an indispensable tool for understanding how random events at the molecular level influence cellular functions. A common challenge of stochastic models is to calibrate a large number of model parameters against the experimental data. Another difficulty is to study how the behaviour of a stochastic model depends on its parameters, i.e. whether a change in model parameters can lead to a significant qualitative change in model behaviour (bifurcation). This thesis addresses such computational challenges by a tensor-structured computational framework. After a background introduction in Chapter 1, Chapter 2 derives the order of convergence in volume size between the stationary distributions of the exact chemical master equation (CME) and its continuous Fokker-Planck approximation (CFPE). It also proposes the multi-scale approaches to address the failure of the CFPE in capturing the noise-induced multi-stability of the CME distribution. Chapter 3 studies the numerical solution of the high-dimensional CFPE using the tensor train and the quantized-TT data formats. In Chapter 4, the tensor solutions are applied to study the parameter estimation, robustness, sensitivity and bifurcation structures of stochastic reaction networks. A Matlab implementation of the proposed methods/algorithms is available at http://www.stobifan.org.
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Grosfils, Aline. "First principles and black box modelling of biological systems." Doctoral thesis, Universite Libre de Bruxelles, 2007. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210677.
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Living cells and their components play a key role within biotechnology industry. Cell cultures and their products of interest are used for the design of vaccines as well as in the agro-alimentary field. In order to ensure optimal working of such bioprocesses, the understanding of the complex mechanisms which rule them is fundamental. Mathematical models may be helpful to grasp the biological phenomena which intervene in a bioprocess. Moreover, they allow prediction of system behaviour and are frequently used within engineering tools to ensure, for instance, product quality and reproducibility.
Mathematical models of cell cultures may come in various shapes and be phrased with varying degrees of mathematical formalism. Typically, three main model classes are available to describe the nonlinear dynamic behaviour of such biological systems. They consist of macroscopic models which only describe the main phenomena appearing in a culture. Indeed, a high model complexity may lead to long numerical computation time incompatible with engineering tools like software sensors or controllers. The first model class is composed of the first principles or white box models. They consist of the system of mass balances for the main species (biomass, substrates, and products of interest) involved in a reaction scheme, i.e. a set of irreversible reactions which represent the main biological phenomena occurring in the considered culture. Whereas transport phenomena inside and outside the cell culture are often well known, the reaction scheme and associated kinetics are usually a priori unknown, and require special care for their modelling and identification. The second kind of commonly used models belongs to black box modelling. Black boxes consider the system to be modelled in terms of its input and output characteristics. They consist of mathematical function combinations which do not allow any physical interpretation. They are usually used when no a priori information about the system is available. Finally, hybrid or grey box modelling combines the principles of white and black box models. Typically, a hybrid model uses the available prior knowledge while the reaction scheme and/or the kinetics are replaced by a black box, an Artificial Neural Network for instance.
Among these numerous models, which one has to be used to obtain the best possible representation of a bioprocess? We attempt to answer this question in the first part of this work. On the basis of two simulated bioprocesses and a real experimental one, two model kinds are analysed. First principles models whose reaction scheme and kinetics can be determined thanks to systematic procedures are compared with hybrid model structures where neural networks are used to describe the kinetics or the whole reaction term (i.e. kinetics and reaction scheme). The most common artificial neural networks, the MultiLayer Perceptron and the Radial Basis Function network, are tested. In this work, pure black box modelling is however not considered. Indeed, numerous papers already compare different neural networks with hybrid models. The results of these previous studies converge to the same conclusion: hybrid models, which combine the available prior knowledge with the neural network nonlinear mapping capabilities, provide better results.
From this model comparison and the fact that a physical kinetic model structure may be viewed as a combination of basis functions such as a neural network, kinetic model structures allowing biological interpretation should be preferred. This is why the second part of this work is dedicated to the improvement of the general kinetic model structure used in the previous study. Indeed, in spite of its good performance (largely due to the associated systematic identification procedure), this kinetic model which represents activation and/or inhibition effects by every culture component suffers from some limitations: it does not explicitely address saturation by a culture component. The structure models this kind of behaviour by an inhibition which compensates a strong activation. Note that the generalization of this kinetic model is a challenging task as physical interpretation has to be improved while a systematic identification procedure has to be maintained.
The last part of this work is devoted to another kind of biological systems: proteins. Such macromolecules, which are essential parts of all living organisms and consist of combinations of only 20 different basis molecules called amino acids, are currently used in the industrial world. In order to allow their functioning in non-physiological conditions, industrials are open to modify protein amino acid sequence. However, substitutions of an amino acid by another involve thermodynamic stability changes which may lead to the loss of the biological protein functionality. Among several theoretical methods predicting stability changes caused by mutations, the PoPMuSiC (Prediction Of Proteins Mutations Stability Changes) program has been developed within the Genomic and Structural Bioinformatics Group of the Université Libre de Bruxelles. This software allows to predict, in silico, changes in thermodynamic stability of a given protein under all possible single-site mutations, either in the whole sequence or in a region specified by the user. However, PoPMuSiC suffers from limitations and should be improved thanks to recently developed techniques of protein stability evaluation like the statistical mean force potentials of Dehouck et al. (2006). Our work proposes to enhance the performances of PoPMuSiC by the combination of the new energy functions of Dehouck et al. (2006) and the well known artificial neural networks, MultiLayer Perceptron or Radial Basis Function network. This time, we attempt to obtain models physically interpretable thanks to an appropriate use of the neural networks.
Doctorat en sciences appliquées
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Lamb, Angharad. "Mathematical Modelling of the Biological Stress Response to Chronium." Thesis, University of Nottingham, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.517846.
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Turner, Stephen. "Mathematical modelling of cancer invasion and biological cell movement." Thesis, Heriot-Watt University, 2002. http://hdl.handle.net/10399/438.
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Walker, Gillian Claire. "Modelling the propagation of terahertz radiation in biological tissue." Thesis, University of Leeds, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.406881.
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Lones, Michael Adam. "Enzyme genetic programming : modelling biological evolvability in genetic programming." Thesis, University of York, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399653.
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Lewis, Miranda Claire. "Mathematical modelling of the growth of soft biological tissues." Thesis, University of Southampton, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.436982.
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Chung, Andy Heung Wing. "Novel mathematical and computational approaches for modelling biological systems." Thesis, University of Sussex, 2016. http://sro.sussex.ac.uk/id/eprint/60405/.
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This work presents the development, analysis and subsequent simulations of mathematical models aimed at providing a basis for modelling atherosclerosis. This cardiovascular disease is characterized by the growth of plaque in artery walls, forming lesions that protrude into the lumen. The rupture of these lesions contributes greatly to the number of cases of stroke and myocardial infarction. These are two of the main causes of death in the UK. Any work to understand the processes by which the disease initiates and progresses has the ultimate aim of limiting the disease through either its prevention or medical treatment and thus contributes a relevant addition to the growing body of research. The literature supports the view that the cause of atherosclerotic lesions is an in inflammatory process-succinctly put, excess amounts of certain biochemical species fed into the artery wall via the bloodstream spur the focal accumulation of extraneous cells. Therefore, suitable components of a mathematical model would include descriptions of the interactions of the various biochemical species and their movement in space and time. The models considered here are in the form of partial differential equations. Specifically, the following models are examined: first, a system of reaction-diffusion equations with coupling between surface and bulk species; second, a problem of optimisation to identify an unknown boundary; and finally, a system of advection-reaction-diffusion equations to model the assembly of keratin networks inside cells. These equations are approximated and solved computationally using the finite element method. The methods and algorithms shown aim to provide more accurate and efficient means to obtain solutions to such equations. Each model in this work is extensible and with elements from each model combined, they have scope to be a platform to give a fuller model of atherosclerosis.
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Sweeney, Paul William. "Realistic numerical image-based modelling of biological tissue substrates." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10049410/.
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The development of preclinical tools to study fluid transport within biological tissue is critical to understanding not only the progression of disease, but the role of the microenvironment in healthy tissue. The limited availability of experimental data across all length scales provides scope for the development of mathematical models to simulate fluid transport throughout the microvasculature and surrounding tissue. Here, the novel REANIMATE (REAlistic Numerical Image-based Modelling of biologicAl Tissue substratEs) platform is developed which, guided by both ex vivo and in vivo imaging data, simulates fluid and solute transport in silico, based on real-world tissue substrates. In this thesis, the intravascular flow model of Fry et al. (2012) and and oxygen transport model of Secomb et al. (2004) are applied to an in vivo cortical microvascular network containing the locations of fluorescently-labelled vascular smooth muscle cells. The simulated results provide insights into the mechanisms underpinning local regulation of cerebral blood flow which would be inaccessible in a conventional experimental setting. Secondly, a transvascular model is developed to simulate the effective transport of fluid through the vasculature and into the interstitium. parameterised against in vivo perfusion data, the model is applied to two ex vivo colorectal tumour datasets to investigate the role of vascular heterogeneity in elevated interstitial fluid pressure within tumours. Next, this platform is used to simulate the steady-state fluid dynamics in a further two murine xenograft models of human colorectal carcinoma, allowing for the prediction of heterogeneous delivery of specific therapeutic agents to be compared with that observed in vivo. Finally, developing upon work by Shipley and Chapman (2010), a discrete-continuum model is developed which allows for the approximation of fluid transport through tissue in the absence of experimental data on tissue-specific vascular micro-structures, thereby providing additional information unavailable in the traditional experimental setting.
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Degasperi, Andrea. "Multi-scale modelling of biological systems in process algebra." Thesis, University of Glasgow, 2011. http://theses.gla.ac.uk/2946/.
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There is a growing interest in combining different levels of detail of biological phenomena into unique multi-scale models that represent both biochemical details and higher order structures such as cells, tissues or organs. The state of the art of multi-scale models presents a variety of approaches often tailored around specific problems and composed of a combination of mathematical techniques. As a result, these models are difficult to build, compose, compare and analyse. In this thesis we identify process algebra as an ideal formalism to multi-scale modelling of biological systems. Building on an investigation of existing process algebras, we define process algebra with hooks (PAH), designed to be a middle-out approach to multi-scale modelling. The distinctive features of PAH are: the presence of two synchronisation operators, distinguishing interactions within and between scales, and composed actions, representing events that occur at multiple scales. A stochastic semantics is provided, based on functional rates derived from kinetic laws. A parametric version of the algebra ensures that a model description is compact. This new formalism allows for: unambiguous definition of scales as processes and interactions within and between scales as actions, compositionality between scales using a novel vertical cooperation operator and compositionality within scales using a traditional cooperation operator, and relating models and their behaviour using equivalence relations that can focus on specified scales. Finally, we apply PAH to define, compose and relate models of pattern formation and tissue growth, highlighting the benefits of the approach.
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Jankovic, Masha. "Modelling biological invasions : population cycles, waves and time delays." Thesis, University of Leicester, 2015. http://hdl.handle.net/2381/31392.
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Biological invasions are rapidly gaining importance due to the ever-increasing number of introduced species. Alongside the plenitude of empirical data on invasive species there exists an equally broad range of mathematical models that might be of use in understanding biological invasions. This thesis aims to address several issues related to modelling invasive species and provide insight into their dynamics. Part I (Chapter 2) documents a case study of the gypsy moth, Lymantria dispar, invasion in the US. We propose an alternative hypothesis to explain the patchiness of gypsy moth spread entailing the interplay between dispersal, predation or a viral infection and the Allee effect. Using a reaction-diffusion framework we test the two models (prey-predator and susceptible-infected) and predict qualitatively similar patterns as are observed in natural populations. As high density gypsy moth populations cause the most damage, estimating the spread rate would be of help in any suppression strategy. Correspondingly, using a diffusive SI model we are able to obtain estimates of the rate of spread comparable to historical data. Part II (Chapters 3, 4 and 5) is more methodological in nature, and in a single species context we examine the effect of an ubiquitous phenomenon influencing population dynamics time delay. In Chapter 3 we show that contrary to the general opinion, time delays are not always destabilising, using a delay differential equation with discrete time delay. The concept of distributed delay is introduced in Chapter 4 and studied through an integrodifferential model. Both Chapters 3 and 4 focus on temporal dynamics of populations, so we further this consideration to include spatial effects in Chapter 5. Using two different representations of movement, we show that the onset of spatiotemporal chaos in the wake of population fronts is possible in a single species model.
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Montagud, Aquino Arnau. "Modelling and analysis of biological systems to obtain biofuels." Doctoral thesis, Universitat Politècnica de València, 2012. http://hdl.handle.net/10251/17319.
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Esta tesis se centra en la construcción y usos de los modelos metabólicos a escala genómica para obtener biocombustibles de manera eficiente, como etanol e hidrógeno. Como organismo objetivo, se ha elegido a la cianobacteria Synechocystis sp. PCC6803. Este organismo ha sido estudiado como una potencial plataforma de producción alimentada por fotones, dada su capacidad de crecer solamente a partir de dióxido de carbono y fotones. Esta tesis versa acerca de los métodos para modelar, analizar, estimar y predecir el comportamiento del metabolismo de las células. La principal meta es extraer conocimiento de los diferentes aspectos biológicos de un organismo con el fin de utilizarlo para un objetivo industrial pertinente.
Esta tesis ha sido estructurada en capítulos organizados de acuerdo con las sucesivas tareas que terminan con la construcción de una célula in silico que se comporta, idealmente, como la que está basada en el carbono. Este proceso suele comenzar con los archivos de anotación del genoma y termina con un modelo metabólico a escala genómica capaz de integrar datos -ómicos. El primer objetivo de la presente tesis es la reconstrucción de un modelo del metabolismo de esta cianobacteria que tenga en cuenta todas las reacciones presentes en la misma. Esta reconstrucción tenía que ser lo suficientemente flexible como para permitir el crecimiento en las distintas condiciones ambientales bajo las cuales este organismo crece en la naturaleza, así como permitir la integración de diferentes niveles de información biológica. Una vez que se cumplió este requisito, se pudieron simular variaciones ambientales y estudiar sus efectos desde una perspectiva de sistema. Se han estudiado hasta cinco diferentes condiciones de crecimiento en este modelo metabólico y sus diferencias han sido evaluadas.
La siguiente tarea fue definir estrategias de producción para sopesar la viabilidad de este organismo como una plataforma de producción. Se simularon perturbaciones genéticas para eThis thesis is focused on the construction and uses of genome-scale metabolic models to efficiently obtain biofuels, such as ethanol and hydrogen. As a target organism, cyanobacterium Synechocystis sp. PCC6803 was chosen. This organism has been studied as a potential photon-fuelled production platform, for its ability to grow only from carbon dioxide, water and photons. This dissertation verses about methods to model, analyse, estimate and predict the metabolic behaviour of cells. Principal goal is to extract knowledge from the different biological aspects of an organism in order to use it for an industrial relevant objective. This dissertation has been structured in chapters accordingly organized as the successive tasks that end up building an in silico cell that behaves as the carbon-based one. This process usually starts with the genome annotation files and ends up with a genome-scale metabolic model able to integrate ¿omics data. First objective of present thesis is to reconstruct a model of this cyanobacteria¿s metabolism that accounts for all the reactions present in it. This reconstruction had to be flexible enough as to allow growth under the different environmental conditions under which this organism grows in nature as well as to allow the integration of different levels of biological information. Once this requisite was met, environmental variations could be simulated and their effect studied under a system-wide perspective. Up to five different growth conditions were simulated on this metabolic model and differences were evaluated. Following assignment was to define production strategies to weigh this organism¿s viability as a production platform. Genetic perturbations were simulated to design strains with an enhanced production of three industrially-relevant metabolites: succinate, ethanol and hydrogen. Resulting sets of genetic modifications for the overproduction of those metabolites are, thus, proposed. Moreover, functional reactions couplings were studied and weighted to their metabolite production importance. Finally, genome-scale metabolic models allow establishing integrative approaches to include different types of data that help to find regulatory hotspots that can be targets of genetic modification. Such regulatory hubs were identified upon light/dark shifts and general metabolism operational principles inferred. All along this process, blind spots in Synechocystis sp. PCC6803 metabolism, and more importantly, blind spots in our understanding of it, are revealed. Overall, the work presented in this thesis unveils the industrial capabilities of cyanobacterium Synechocystis sp. PCC6803 to evolve interesting metabolites as a clean production platform.
Esta tesis es centra en la construcció i els usos del models metabòlics a escala genòmica per a obtenir eficientment biocombustibles, com etanol i hidrogen. Com a organisme diana, s¿elegí el cianobacteri Synechocystis sp. PCC6803. Aquest organisme ha segut estudiat com una plataforma de producció nodrida per fotons, per la seva habilitat per créixer a partir únicament de diòxid de carboni, aigua i fotons. Aquesta tesi versa sobre mètodes per a modelitzar, analitzar, estimar i predir el comportament metabòlic de cèl¿lules. La principal meta és extreure coneixement del diferents aspectes biològics d¿un organisme de manera que s¿usen per a un objectiu industrial rellevant. La tesi ha segut estructurada en capítols organitzats d¿acord a les successives tasques que acaben construint una cèl¿lula in silico que es comporta, idealment, com la que està basada en carboni. Aquest procés generalment comença amb els arxius de l¿anotació del genoma i acaba amb un model metabòlic a escala genòmica capaç d¿integrar dades ¿òmiques. El primer objectiu de la present tesi és la reconstrucció d¿un model del metabolisme d¿aquest cianobacteri que tinga en compte totes les reaccions que hi estan presents. Esta reconstrucció havia de ser prou flexible com per permetre la simulació del creixement en les diferents condicions ambientals en les quals aquest cianobacteri creix en la natura, així com permetre la integració de diferents nivells d¿informació biològica. Una vegada que aquest requisit fou assolit, es pogueren simular variacions ambientals i estudiar els seus efectes amb una perspectiva de sistema. S¿han simulat fins a cinc condicions de creixement en este model metabòlic i les seves diferències han segut avaluades. La següent tasca fou definir estratègies de producció per a valorar la viabilitat d¿aquest organisme com a plataforma de producció. Es simularen pertorbacions genètiques per al disseny de soques amb producció millorada de metabòlits de rellevància industrial: succinat, etanol i hidrogen. Així, es proposen conjunts de modificacions genètiques per a la sobreproducció d¿aquests metabòlits. També s'han estudiat reaccions acoblades funcionalment i s¿ha ponderat la seva importància en la producció de metabòlits. Finalment, els models metabòlics a escala genòmica permeten establir criteris per integrar diferents tipus de dades que ens ajuden a trobar punts importants de regulació. Eixos centres reguladors, que poden ser objecte de modificacions genètiques, han segut investigats baix canvis dràstics d¿il¿luminació i s¿han inferit principis operacionals del metabolisme. Al llarg d'aquest procés, s¿han revelat punts cecs al metabolisme de Synechocystis sp. PCC6803 i, el més important, punts cecs en la nostra comprensió d'aquest metabolisme. En general, el treball presentat en aquesta tesi dona a conèixer les capacitats industrials del cianobacteri Synechocystis sp. PCC6803 per a produir metabòlits d'interès, tot sent una plataforma de producció neta i sostenible.
Montagud Aquino, A. (2012). Modelling and analysis of biological systems to obtain biofuels [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17319
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Rocca, Alexandre. "Formal methods for modelling and validation of biological models." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAM028/document.
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L’objectif de cette thèse est la modélisation et l’étude de systèmes biologiques par l’intermédiaire de méthodes formelles. Les systèmes biologiques démontrent des comportements continus mais sont aussi susceptibles de montrer des changements abrupts dans leur dynamiques. Les équations différentielles ordinaires, ainsi que les systèmes dynamiques hybrides, sont deux formalismes mathématiques utilisés pour modéliser clairement de tels comportements. Un point critique de la modélisation de systèmes biologiques est la recherche des valeurs des paramètres du modèle afin de reproduire de manière précise un ensemble de données expérimentales. Si aucun jeu de paramètres valides n’est trouvé, il est nécessaire de réviser le modèle. Une possibilité est alors de remplacer un paramètre, ou un ensemble de paramètres, définissant un processus biologique par une fonctiondépendante du temps.Dans le cadre de cette thèse, nous exposons tout d’abord une méthode pour la révision de modèles hybrides. Pour cela, nous proposons une approche gloutonne appliquée à une méthode de contrôle optimal utilisant les mesures d’occupations etla relaxation convexe. Ensuite, nous étudions comment analyser les propriétés dynamiques d’un modèle à temps discret en utilisant la simulation ensembliste. Dans cet objectif, nous proposons deux méthodes basées sur deux outils mathématiques.La première méthode, qui se repose sur les polynômes de Bernstein, est une extension aux systèmes dynamiques hybrides, de l’outil de calcul ensembliste Sapo [1]. La seconde méthode utilise les représentations de Krivine-Stengle [2] pour permettre l’analyse d’atteignaiblité de systèmes dynamiques polynomiaux. Enfin, nous proposons aussi une méthodologie pour générer des systèmes dynamiques hybrides modélisant des protocoles biologiques expérimentaux. Les méthodes précédemment proposées sont appliquées sur divers études biologiques. Nous étudions tout d’abord un modèle de la production d’hémoglobinedurant la différentiation des érythrocytes dans la moelle [3]. Pour permettre la construction de ce modèle, nous avons dans un premier temps généré un ensemble de jeux de paramètres valides à l’aide d’une méthode de type Monte-Carlo. Dans un second temps, nous avons appliqué la méthode de révision de modèle afin de reproduire plus précisément les données expérimentales [4]. Nous proposons aussi un modèle préliminaire des effets à faibles doses du Cadmium sur la réponse du métabolisme à différentes étapes de la vie d’un rat. Enfin, nous appliquons les techniques d’analyse ensembliste pour la validation d’hypothèses sur un modèle d’homéostasie du fer [6] dans le cas où des paramètres varient dans de larges intervalles.Dans cette thèse, nous montrons aussi que le protocole associé à l’étude de la production d’hémoglobine, ainsi que le protocole étudiant l’intégration du Cadmium durant la vie d’un rat, peuvent être formalisés comme des systèmes dynamiques hybrides, et servent ainsi de preuves de concepts pour notre méthode de modélisation de protocoles expérimentauxThe purpose of this thesis is the modelling and analysis of biological systems with mechanistic models (in opposition with black-box models).In particular we use two mathematical formalisms for mechanism modelling: hybrid dynamical systems and polynomial Ordinary Differential Equations (ODEs).Biological systems modelling give rise to numerous problem and in this work we address three of them.First, the parameters in the differential equations are often uncertain or unknown.Consequently, we aim at generating a subset of valid parameter sets such that the models satisfy constraints deducted from some experimental data.This problem is addressed in the literature under the denomination of parameter synthesis, parameter estimation, parameter fitting, or parameter identification following the context.Then, if no valid parameter is found, one solution is to revise the model. This can be done by substituting a law in place of a constant parameter.In the literature, models with uncertain parts are known as grey models, and their studies can be found under the term of model identification.Finally, it may be necessary to ensure the correctness of the built models using validation, or verification, methods for a continuous over-approximation of the determined valid parameters.In this thesis we study the parameter synthesis problem, in the Haemoglobin production model case study, using an adaptation of the classical method based on Monte-Carlo sampling, and numerical simulations.To perform model revision of hybrid dynamical systems we propose an iterative scheme of an optimal control method based on occupation measures, and convex relaxations.Finally, we assess the quality of a model using set-based simulations, and reachability analysis.For this purpose, we propose two methods: the first one, which relies on Bernstein expansion, is an extension for hybrid dynamical systems of the reachability tool sapo , while the other uses Krivine-Stengle representations to perform the reachability analysis of polynomial ODEs.We also provide a methodology to generate hybrid dynamical systems modelling biological experimental protocols.All of these proposed methods were applied in different case studies.We first propose a model of haemoglobin production during the differentiation of an erythrocyte in the bone marrow.To develop this model we first applied the Monte-Carlo based parameters synthesis, followed by the model revision to correctly fit to the experimental data.We also propose a hybrid model of Cadmium flux in rats in the context of an experimental protocol, as well as a preliminary study of the effect of low dose Cadmium on a Glucose response.Finally, we apply the reachability analysis techniques for the validation on large parameters set of the iron homoeostasis model developed by Nicolas Mobilia during his Phd.We note the haemoglobin production model, as well as the glucose reponse model can be formalised, in their experimental context, as hybrid dynamical systems. Thus, they serve as proof of concept for the methodology of biological experimental protocols modelling
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Chan, David Chung Chan. "Design, synthesis and biological evaluation of novel antifolates against Pneumocystis carinii." Thesis, University of Nottingham, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.250580.
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Tsafnat, Guy Computer Science & Engineering Faculty of Engineering UNSW. "Abstraction and representation of fields and their applications in biomedical modelling." Awarded by:University of New South Wales. School of Computer Science and Engineering, 2006. http://handle.unsw.edu.au/1959.4/24207.
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Computer models are used extensively to investigate biological systems. Many of these systems can be described in terms of fields???spatially- and temporally- varying scalar, vector and tensor properties defined over domains. For example, the spatial variation of muscle fibers is a vector field, the spatial and temporal variation in temperature of an organ is a scalar field, and the distribution of stress across muscle tissue is a tensor field. In this thesis I present my research on how to represent fields in a format that allows researchers to store and distribute them independently of models and to investigate and manipulate them intuitively. I also demonstrate how the work can be applied to solving and analysing biomedical models. To represent fields I created a two-layer system. One layer, called the Field Representation Language (FRL), represents fields by storing numeric, analytic and meta data for storage and distribution. The focus of this layer is efficiency rather than usability. The second layer, called the Abstract Field Layer (AFL), provides an abstraction of fields so that they are easier for researchers to work with. This layer also provides common operations for manipulating fields as well as transparent conversion to and from FRL representations. The applications that I used to demonstrate the use of AFL and FRL are (a) a fields visualisation toolkit, (b) integration of models from different scales and solvers, and (c) a solver that uses AFL internally. The layered architecture facilitated the development of tools that use fields. A similar architecture may also prove useful for representations of other modelled entities.
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Raju, Rajesh Kumar. "Computational Modelling of Noncovalent Interactions in Chemical and Biological Recognition." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.508602.
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Fenwick, John David. "Biological modelling of pelvic radiotherapy : potential gains from conformal techniques." Thesis, Institute of Cancer Research (University Of London), 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.322314.
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Key, H. "The modelling of light attenuation and transmission in biological tissues." Thesis, University of Bristol, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.238177.
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Ortiz, guzman John Erick. "Fast boundary element formulations for electromagnetic modelling in biological tissues." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2017. http://www.theses.fr/2017IMTA0051/document.
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Cette thèse présente plusieurs nouvelles techniques pour la convergence rapide des solutions aux éléments de frontière de problèmes électromagnétiques. Une attention spéciale a été dédiée aux formulations pertinentes pour les solutions aux problèmes électromagnétiques dans les tissus biologiques à haute et basse fréquence. Pour les basses fréquences, de nouveaux schémas pour préconditionner et accélérer le problème direct de l'électroencéphalographie sont présentés dans cette thèse. La stratégie de régularisation repose sur une nouvelle formule de Calderon, obtenue dans cette thèse, alors que l'accélération exploite le paradigme d'approximation adaptive croisée (ACA). En ce qui concerne le régime haute fréquence, en vue d'applications de dosimétrie, l'attention de ce travail a été concentrée sur l'étude de la régularisation de l'équation intégrale de Poggio-Miller-Chang-Harrington-Wu-Tsai (PMCHWT) à l'aide de techniques hiérarchiques. Le travail comprend une analyse complète de l'équation pour des géométries simplement et non-simplement connectées. Cela a permis de concevoir une nouvelle stratégie de régularisation avec une base hiérarchique permettant d'obtenir une équation pour les milieux pénétrable stable pour un large spectre de fréquence. Un cadre de travail propédeutique de discrétisation et une bibliothèque de calcul pour des thèmes de recherches sur les techniques de Calderon en 2D sont proposés en dernière partie de cette thèse. Cela permettra d'étendre nos recherches à l'imagerie par tomographieThis thesis presents several new techniques for rapidly converging boundary element solutions of electromagnetic problems. A special focus has been given to formulations that are relevant for electromagnetic solutions in biological tissues both at low and high frequencies. More specifically, as pertains the low-frequency regime, this thesis presents new schemes for preconditioning and accelerating the Forward Problem in Electroencephalography (EEG). The regularization strategy leveraged on a new Calderon formula, obtained in this thesis work, while the acceleration leveraged on an Adaptive-Cross-Approximation paradigm. As pertains the higher frequency regime, with electromagnetic dosimetry applications in mind, the attention of this work focused on the study and regularization of the Poggio-Miller-Chang-Harrington-Wu-Tsai (PMCHWT) integral equation via hierarchical techniques. In this effort, a complete analysis of the equation for both simply and non-simply connected geometries has been obtained. This allowed to design a new hierarchical basis regularization strategy to obtain an equation for penetrable media which is stable in a wide spectrum of frequencies. A final part of this thesis work presents a propaedeutic discretization framework and associated computational library for 2D Calderon research which will enable our future investigations in tomographic imaging
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Ongaro, Federica. "Theoretical and numerical modelling of biologically inspired composite materials." Thesis, Queen Mary, University of London, 2017. http://qmro.qmul.ac.uk/xmlui/handle/123456789/30826.
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The cellular nature of many biological materials, providing them with low density, high strength and high toughness, have fascinated many researchers in the field of botany and structural biology since at least one century. Bamboo, sponges, trabecular bone, tooth and honeybee combs are only few examples of natural materials with cellular architecture. It has been widely recognised that the geometric and mechanical characteristics of the microscopic building blocks play a fundamental role on the behavior observed at the macroscale. Up to date, many efforts have been devoted to the analysis of cellular materials with empty cells to predict the structure-property relations that link the macroscopic properties to the mechanics of their underlying microstructure. Surprisingly, notwithstanding the great advantages of the composite solutions in nature, in the literature a limited number of investigations concern cellular structures having the internal volumes of the cells filled with fluids, fibers or other bulk materials as commonly happens in biology. In particular, a continuum model has not been derived and explicit formulas for the effective elastic constants and constitutive relations are currently not available. To provide a contribution in this limitedly explored research area, this thesis describes the mathematical formulation and modelling technique leading to explicit expressions for the macroscopic elastic constants and stress-strain relations of biologically inspired composite cellular materials. Two examples are included. The first deals with a regular hexagonal architecture inspired by the biological parenchyma tissue. The second concerns a mutable cellular structure, composed by mutable elongated hexagonal cells, inspired by the hygroscopic keel tissue of the ice plant Delosperma nakurense. In both cases, the predicted results are found to be in very good agreement with the available data in the literature. Then, by taking into account the benefits offered by the complex hierarchical organisation of many natural systems, the attention is focused on the potential value of adding structural hierarchy into two-dimensional composite cellular materials having a self-similar hierarchical architecture, in the first case, and different levels with different cell topologies, in the second. In contrast to the traditional cellular materials with empty cells, the analysis reveals that, in the cell-filled configuration, introducing levels of hierarchy leads to an improvement in the specific stiffness. Finally, to offer concrete and relevant tools to engineers for developing future generations of materials with enhanced performance and unusual functionalities, a novel strategy to obtain a honeycomb with mutable cells is proposed. The technique, based on the ancient Japanese art of kirigami, consists in creating a pattern of cuts into a flat sheet of starting material, which is then stretched to give a honeycomb architecture. It emerges a vast range of effective constants that the so-called kirigami honeycomb structures can be designed with, just by changing the value of the applied stretch.
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Wright, Sarah Natalie. "Integration of metabolic modelling with machine learning to identify mechanisms underlying antibiotic killing." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112492.
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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Biological Engineering, 2017.Cataloged from PDF version of thesis.
Includes bibliographical references (pages. 63-65).
Microbial pathogens are becoming a pressing global health issue due to the rapid appearance of resistant strains, accompanied by slow development of new antibiotics. In order to improve these treatments and engineer novel therapies, it is crucial that we increase our understanding of how these antibiotics interact with cellular metabolism. Evidence is increasingly building that the efficacy of antibiotics relies critically on downstream metabolic effects, in addition to inhibition of primary targets. Here we present a novel computational pipeline to expedite investigation of these effects: we combine computational modelling of metabolic networks with data from experimental screens on antibiotic susceptibility to identify metabolic vulnerabilities that can enhance antibiotic efficacy. This approach utilizes genome-scale metabolic models of bacterial metabolism to simulate the reaction-level response of cellular metabolism to a metabolite counter screen. The simulated results are then integrated with experimentally determined antibiotic sensitivity measurements using machine learning. Following integration, a mechanistic understanding of the phenotype-level antibiotic sensitivity results can be extracted. These mechanisms further support the role of metabolism in the mechanism of action of antibiotic lethality. Consistent with current understanding, application of the pipeline to M. tuberculosis identified cysteine metabolism, ATP synthase, and the citric acid cycle as key pathways in determining antibiotic efficacy. Additionally, roles for metabolism of aromatic amino acids and biosynthesis of polyprenoids were identified as pathways meriting further investigation.
by Sarah Natalie Wright.
M. Eng.
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Kavvada, Klairi M. "Modelling the gastric epithelium for testing of new chemical entities." Thesis, Aston University, 2004. http://publications.aston.ac.uk/11029/.
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A cell culture model of the gastric epithelial cell surface would prove useful for biopharmaceutical screening of new chemical entities and dosage forms. A successful model should exhibit tight junction formation, maintenance of differentiation and polarity. Conditions for primary culture of guinea-pig gastric mucous epithelial cell monolayers on Tissue Culture Plastic (TCP) and membrane insects (Transwells) were established. Tight junction formation for cells grown on Transwells for three days was assessed by measurement of transepithelial resistance (TEER) and permeability of mannitol and fluorescein. Coating the polycarbonate filter with collagen IV, rather with collagen I, enhanced tight junction formation. TEER for cells grown on Transwells coated with collagen IV was close to that obtained with intact guinea-pig gastric epithelium in vitro. Differentiation was assessed by incorporation of [3H] glucosamine into glycoprotein and by activity of NADPH oxidase, which produces superoxide. Both of these measures were greater for cells grown on filters coated with collagen I than for cells grown on TCP, but no major difference was found between cells grown on collagens I and IV. However, monolayers grown on membranes coated with collagen IV exhibited apically polarized secretion of mucin and superoxide. The proportion of cells, which stained positively for mucin with periodic Schiff reagent, was greater than 95% for all culture conditions. Gastric epithelial monolayers grown on Transwells coated with collagen IV were able to withstand transient (30 min) apical acidification to pH 3, which was associated with a decrease in [3H] mannitol flux and an increase in TEER relative to pH 7.4. The model was used to provide the first direct demonstration that an NSAID (indomethacin) accumulated in gastric epithelial cells exposed to low apical pH. In conclusion, guinea-pig epithelial cells cultured on collagen IV represent a promising model of the gastric surface epithelium suitable for screening procedures.
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Comellas, Sanfeliu Ester. "Numerical modelling of the growth and remodelling phenomena in biological tissues." Doctoral thesis, Universitat Politècnica de Catalunya, 2016. http://hdl.handle.net/10803/436906.
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Living biological tissues are complex structures that have the capacity of evolving in response to external loads and environmental stimuli. The adequate modelling of soft biological tissue behaviour is a key issue in successfully reproducing biomechanical problems through computational analysis.
This study presents a general constitutive formulation capable of representing the behaviour of these tissues through finite element simulation. It is based on phenomenological models that, used in combination with the generalized mixing theory, can numerically reproduce a wide range of material behaviours.
First, the passive behaviour of tissues is characterized by means of hyperelastic and finite-strain damage models. A new generalized damage model is proposed, providing a flexible and versatile formulation that can reproduce a wide range of tissue behaviour. It can be particularized to any hyperelastic model and requires identifying only two material parameters.
Then, the use of these constitutive models with generalized mixing theory in a finite-strain framework is described and tools to account for the anisotropic behaviour of tissues are put forth.
The active behaviour of tissues is characterized through constitutive models capable of reproducing the growth and remodelling phenomena. These are built on the hyperelastic and damage formulations described above and, thus, represent the active extension of the passive tissue behaviour. A growth model considering biological availability is used and extended to include directional growth. In addition, a novel constitutive model for homeostatic-driven turnover remodelling is presented and discussed. This model captures the stiffness recovery that occurs in healing tissues, understood as a recovery or reversal of damage in the tissue, which is driven by both mechanical and biochemical stimuli.
Finally, the issue of correctly identifying the material parameters for computational modelling is addressed. An inverse method using optimization techniques is developed to facilitate the identification of these parameters.Els teixits biològics vius són estructures complexes que tenen la capacitat d'evolucionar en resposta a càrregues externes i estímuls ambientals. El modelat adequat del comportament del teixit biològic tou és un tema clau per poder reproduir amb èxit problemes biomecànics mitjançant anàlisi computacional. Aquest estudi presenta una formulació constitutiva general capaç de representar el comportament d'aquests teixits mitjançant la simulació amb elements finits. Es basa en models fenomenològics que, usats en combinació amb la teoria de mescles generalitzada, permeten reproduir numèricament un ampli ventall de comportaments materials. Primer, el comportament passiu dels teixits es caracteritza amb models hiperelàstics i de dany en grans deformacions. Es proposa un model generalitzat de dany que proporciona una formulació versàtil i flexible per poder reproduir una extensa gamma de conductes de teixits. Pot ser particularitzat amb qualsevol model hiperelàstic i requereix identificar tan sols dos paràmetres materials. Llavors, es descriu l'ús d'aquests models constitutius en conjunt amb la teoria generalitzada de mescles, desenvolupada en el marc de grans deformacions, i es presenten eines que permeten incorporar les propietats anisòtropes dels teixits. El comportament actiu dels teixits es caracteritza mitjançant models constitutius capaços de reproduir els fenòmens de creixement i remodelació. Aquests es construeixen sobre les formulacions d'hiperelasticitat i dany descrites anteriorment i, per tant, suposen l'extensió activa del comportament passiu del teixit. Es fa servir un model de creixement que té en compte la disponibilitat biològica de l'organisme, que després s'amplia per incloure dany direccional en el model. També es presenta i analitza un nou model constitutiu per al remodelat per renovació tendint a l’homeòstasi (homeostatic-driven turnover remodelling). Aquest model captura la recuperació de rigidesa que s'observa en teixits que es guareixen. Aquí, el remodelat s'entén com la recuperació o inversió del dany en el teixit i és motivat tant per estímuls mecànics com bioquímics. Finalment, s'aborda el tema de la identificació correcta dels paràmetres materials per al modelat computacional. Es desenvolupa un mètode invers que fa ús de tècniques d'optimització per facilitar la identificació d'aquests paràmetres