When modelling tissue-level cardiac electrophysiology, a continuum approximation to the discrete cell-level equations, known as the bidomain equations, is often used to maintain computational tractability. The bidomain equations are derived from the discrete equations using a mathematical technique known as homogenisation. As part of this derivation conductivity tensors are specified for use in the continuum model. Analysing the derivation of the bidomain equations allows us to investigate how microstructure, in particular gap junctions that electrically connect cells, affect tissue-level conductivity properties and model solutions. We perform two distinct but related strands of investigation in this thesis. In the first, we consider the effect of including gap junctions on the results of both discrete and continuum simulations, and identify when the continuum model fails to be a good approximation to the discrete model. Secondly, we perform a comprehensive study into how cell-level microstructure properties, such as cell shape, impact the homogenised conductivities to be used in a tissue-level continuum model. This will allow us to predict how the onset of a disease or a change in cellular microstructure will affect the propagation of action potentials. To do this, we first derive a modified version of the bidomain equations that explicitly takes gap junctions into account. We then derive analytic solutions for the homogenised conductivity tensors on a simplified two-dimensional geometry and find that diseased gap junctions have a large impact on the results of homogenisation. On this same geometry we compare the results of discrete and continuum simulations and find a significant discrepancy between model conduction velocities when we introduce gap junctions with lower coupling strength, or when we consider elongated cells. From this, we conclude that the bidomain equations are less likely to give an accurate representation of the underlying discrete system when modelling diseased states whose symptoms include reduced gap junction coupling or an increase in myocyte length. We then use a more realistic two-dimensional geometry and numerically approximate the homogenised conductivity tensors on this geometry. We discover that the packing of cells has a substantial effect on conduction, with a brick-wall geometry particularly beneficial for fast propagation, and that gap junctions also have a large effect on conduction. Finally, we consider a three-dimensional cellular geometry and show that the effect of changing gap junction properties is different when compared to two dimensions, and discover that the anisotropy ratios of the tissue are highly sensitive to changes in gap junction parameters. Overall, we conclude that gap junctions and cell structure have a large effect on discrete and continuum model results, and on homogenised conductivity calculations in tissue-level cardiac electrophysiology.

Dans les dernières décennies, l'impact dû à l'altération de la microstructure du tissu cardiaque dans la survenue de troubles arythmiques (syndrome de Brugada, fibrillation auriculaire, syndromes de repolarisation précoce…) est de plus en plus étudié. Les données expérimentales relatives au fonctionnement et aux régulations intervenant aux échelles cellulaires et subcellulaires (jonctions communiquantes, rôle de certains canaux ioniques) sont de plus en plus nombreuses, et fournissent un cadre adapté aux numériciens pour développer ou affiner des modèles et en valider les comportements. Dans cette thèse, nous proposons le développement et l'étude d'un modèle « microscopique » prenant en compte la géométrie individuelle des cellules et les jonctions communiquantes entre elles. Le modèle vise à comprendre la propagation du potentiel d'action au sein d'un réseau de cellules. Nous établissons ce modèle via une étude du comportement des ions dans les cellules. Ce comportement, décrit par diverses équations de la physique microscopique (électrostatique...), fournit un cadre à partir duquel, en effectuant quelques analyses dimensionnelles et une étude asymptotique, nous dérivons le modèle susmentionné. Puis, nous démontrons l'existence d'une solution à ce modèle à l'aide d'un processus de discrétisation en temps « semi-implicite » et de théorèmes de compacité. Nous proposons ensuite un ensemble de simulations dont l'objet est de comprendre la propagation des potentiels d'action entres cellules au sein d'un réseau, et en particulier le rôle des jonctions communiquantes. Nous étudions différents modèles de jonctions communiquantes, dont un non-linéaire et dépendant du temps. Cette thèse ouvre de nombreuses perspectives, à courte échéance des comparaisons à des observations expérimentales chez la souris, et à plus long terme de recherche sur les mécanismes de propagation à l'échelle cellulaire et leurs impact sur les troubles du rythme cardiaque. / During the last decades, studies regarding the prospective impact of the alterations at the microscopic scale of the heart tissue in the appearance of arrhythmias (Brugada's syndrome, atrial fibrillation, early repolarization syndrome...) have been more numerous. The amount of experimental data regarding the behaviors and regulations that occur at a cellular and a subcellular (gap junctions, role of specific ionic channels) is increasing and these data provide an adapted frame for the computational mathematicians to develop or improve models and confirm their behaviour. In this thesis, we developed and studied a ``microscopic'' model taking into account the individual geometry of the cells and the gap junctions between them. This model is designed to enhance our understanding of the action potential propagation in a network of cells. We extracted this model using a study of the ions movements in the cells. These movements, described by various microscopic physics equations (electrostatic...), and some dimensional analysis, including an asymptotic study, allow us to derive the model. We then show that the problem described by such a model has a solution, via a semi-implicit time discretization process and compacity arguments. Afterwards, we offer numerous simulations in order to enhance our understanding of the action potential propagation between the cells of various networks. We specifically customize the gap junction models we use (a geometric one, a linear one and a non-linear one) to enhance our comprehension. This thesis introduces many questions. On the short-term, on the comparison between experimental data observed on mice cells and our results. On the long-term regarding the mechanisms regulating the action potential propagation, and their impact on the alterations of the cardiac rhythm.

Mathematical models of electric activity in cardiac tissue are becoming increasingly powerful tools in the study of cardiac arrhythmias. Considered here are mathematical models based on ordinary differential equations (ODEs) and partial differential equations (PDEs) that describe the behaviour of this electrical activity. Generating an efficient numerical solution of these models is a challenging task, and in fact the physiological accuracy of tissue-scale models is often limited by the efficiency of the numerical solution process. In this thesis, we discuss two sets of experiments that test ideas for making the numerical solution process more efficient. In the first set of experiments, we examine the numerical solution of four single cell cardiac electrophysiological models, which consist solely of ODEs. We study the efficiency of using implicit-explicit Runge-Kutta (IMEX-RK) splitting methods to solve these models. We find that variable step-size implementations of IMEX-RK methods (ARK3 and ARK5) that take advantage of Jacobian structure clearly outperform most methods commonly used in practice for two of the models, and they outperform all methods commonly used in practice for the remaining models. In the second set of experiments, we examine the solution of the bidomain model, a model consisting of both ODEs and PDEs that are typically solved separately. We focus these experiments on numerical methods for the solution of the two PDEs in the bidomain model. The most popular method for this task, the Crank-Nicolson method, produces unphysical oscillations; we propose a method based on a second-order L-stable singly diagonally implicit Runge-Kutta (SDIRK) method to eliminate these oscillations.<p> We find that although the SDIRK method is able to eliminate these unphysical oscillations, it is only more efficient for crude error tolerances.

Mathematical models of electric activity in cardiac tissue are becoming increasingly powerful tools in the study of cardiac arrhythmias. Considered here are mathematical models based on ordinary differential equations (ODEs) and partial differential equations (PDEs) that describe the behaviour of this electrical activity. Generating an efficient numerical solution of these models is a challenging task, and in fact the physiological accuracy of tissue-scale models is often limited by the efficiency of the numerical solution process. In this thesis, we discuss two sets of experiments that test ideas for making the numerical solution process more efficient. In the first set of experiments, we examine the numerical solution of four single cell cardiac electrophysiological models, which consist solely of ODEs. We study the efficiency of using implicit-explicit Runge-Kutta (IMEX-RK) splitting methods to solve these models. We find that variable step-size implementations of IMEX-RK methods (ARK3 and ARK5) that take advantage of Jacobian structure clearly outperform most methods commonly used in practice for two of the models, and they outperform all methods commonly used in practice for the remaining models. In the second set of experiments, we examine the solution of the bidomain model, a model consisting of both ODEs and PDEs that are typically solved separately. We focus these experiments on numerical methods for the solution of the two PDEs in the bidomain model. The most popular method for this task, the Crank-Nicolson method, produces unphysical oscillations; we propose a method based on a second-order L-stable singly diagonally implicit Runge-Kutta (SDIRK) method to eliminate these oscillations.<p> We find that although the SDIRK method is able to eliminate these unphysical oscillations, it is only more efficient for crude error tolerances.

Hypertrophic cardiomyopathy (HCM) is the most common monogenic disorder and the leading cause of sudden arrhythmic death in children and young adults. It is typically asymptomatic and first manifests itself during cardiac arrest, making it a challenge to diagnose in advance. Computational models can explore and reveal underlying molecular mechanisms in cardiac electrophysiology by allowing researchers to alter various parameters such as tissue size or ionic current amplitudes. The goal of this thesis is to develop a computational model in MATLAB and to determine if this model can accurately indicate cases of hypertrophic cardiomyopathy. This goal is achieved by combining a three-dimensional network of the bidomain model with the Beeler-Reuter model and then by manually varying the thickness of that tissue and recording the resulting membrane potential with respect to time. The results of this analysis demonstrated that the developed model is able to depict variations in tissue thickness through the difference in membrane potential recordings. A one-way ANOVA analysis confirmed that the membrane potential recordings of the different thicknesses were significantly different from one another. This study assumed continuum behavior, which may not be indicative of diseased tissue. In the future, such a model might be validated through in vitro experiments that measure electrical activity in hypertrophied cardiac tissue. This model may be useful in future applications to study the ionic mechanisms related to hypertrophic cardiomyopathy or other related cardiac diseases.

<p>Cardiac arrhythmias triggered by both reentrant and focal sources are closely correlated with regions of tissue characterized by significant structural heterogeneity. Experimental and modeling studies of electrical activity in the heart have shown that local microscopic heterogeneities which average out at the macroscale in healthy tissue play a much more important role in diseased and aging cardiac tissue which have low levels of coupling and abnormal or reduced membrane excitability. However, it is still largely unknown how various combinations of microheterogeneity in the intracellular and interstitial spaces affect wavefront propagation in these critical regimes. </p> <p>This thesis uses biophysically realistic 1-D and 2-D computer models to investigate how heterogeneity in the interstitial and intracellular spaces influence both the initiation of ectopic beats and the escape of multiple ectopic beats from a poorly coupled region of tissue into surrounding well-coupled tissue. An approximate discrete monodomain model that incorporates local heterogeneity in both the interstitial and intracellular spaces was developed to represent the tissue domain. </p> <p>The results showed that increasing the effective interstitial resistivity in poorly coupled fibers alters the distribution of electrical load at the microscale and causes propagation to become more like that observed in continuous fibers. In poorly coupled domains, this nearly continuous state is modulated by cell length and is characterized by decreased gap junction delay, sustained conduction velocity, increased sodium current, reduced maximum upstroke velocity, and increased safety factor. In inhomogeneous fibers with adjacent well-coupled and poorly coupled regions, locally increasing the effective interstitial resistivity in the poorly coupled region reduces the size of the focal source needed to generate an ectopic beat, reduces dispersion of repolarization, and delays the onset of conduction block that is caused by source-load mismatch at the boundary between well-coupled and poorly-coupled regions. In 2-D tissue models, local increases in effective interstitial resistivity as well as microstructural variations in cell arrangement at the boundary between poorly coupled and well-coupled regions of tissue modulate the distribution of maximum sodium current which facilitates the unidirectional escape of focal beats. Variations in the distribution of sodium current as a function of cell length and width lead to directional differences in the response to increased effective interstitial resistivity. Propagation in critical regimes such as the ectopic substrate is very sensitive to source-load interactions and local increases in maximum sodium current caused by microheterogeneity in both intracellular and interstitial structure.</p> / Dissertation

Cette thèse est dédiée à l'analyse mathématique et la simulation numérique des équations intervenant dans la modélisation de l’électrophysiologie cardiaque. D'abord, nous donnons une justification mathématique rigoureuse du processus d’homogénéisation périodique à l’aide de la méthode d'éclatement périodique. Nous considérons des conductivités électriques tensorielles qui dépendent de l’espace et des modèles ioniques non linéaires physiologiques et phénoménologiques. Nous montrons l'existence et l'unicité d’une solution du modèle microscopique en utilisant une approche constructive de Faedo- Galerkin suivie par un argument de compacité dans L2. Ensuite, nous montrons la convergence de la suite de solutions du problème microscopique vers la solution du problème macroscopique. À cause des termes non linéaires sur la variété oscillante, nous utilisons l’opérateur d’éclatement sur la surface et un argument de compacité de type Kolmogorov pour les modèles phénoménologiques et de type Minty pour les modèles physiologiques. En outre, nous considérons le modèle monodomaine couplé au modèle physiologique de Beeler-Reuter. Nous proposons un schéma volumes finis et nous analysons sa convergence. D'abord, nous dérivons la formulation variationnelle discrète correspondante et nous montrons l'existence et l'unicité de sa solution. Par compacité, nous obtenons la convergence de la solution discrète. Comme le schéma TPFA (two point flux approximation) est inefficace pour approcher les flux diffusifs avec des tenseurs anisotropes, nous proposons et analysons, ensuite, un schéma combiné non-linéaire qui préserve le principe de maximum. Ce schéma est basé sur l’utilisation d’un flux numérique de Godunov pour le terme de diffusion assurant que les solutions discrètes soient bornées sans restriction sur le maillage du domaine spatial ni sur les coefficients de transmissibilité. Enfin, dans la perspective d'étudier la solvabilité des modèles électromécaniques couplés avec des modèles ioniques physiologiques, nous considérons un modèle avec une description linéarisée de la réponse élastique passive du tissu cardiaque, une linéarisation de la contrainte d'incompressibilité et une approximation tronquée des diffusivités non linéaires intervenant dans les équations du modèle bidomaine. La preuve utilise des approximations par des systèmes non-dégénérés et la méthode Faedo-Galerkin suivie par un argument de compacité. / This thesis is concerned with the mathematical analysis and numerical simulation of cardiac electrophysiology models. We use the unfolding method of homogenization to rigorously derive the macroscopic bidomain equations. We consider tensorial and space dependent conductivities and physiological and simplified ionic models. Using the Faedo-Galerkin approach followed by compactness, we prove the existence and uniqueness of solution to the microscopic bidomain model. The convergence of a sequence of solutions of the microscopic model to the solution of the macroscopic model is then obtained. Due to the nonlinear terms on the oscillating manifold, the boundary unfolding operator is used as well as a Kolmogorov compactness argument for the simplified models and a Minty type argument for the physiological models. Furthermore, we consider the monodomain model coupled to Beeler- Reuter's ionic model. We propose a finite volume scheme and analyze its convergence. First, we show existence and uniqueness of its solution. By compactness, the convergence of the discrete solution is obtained. Since the two-point flux approximation (TPFA) scheme is inefficient in approximating anisotropic diffusion fluxes, we propose and analyze a nonlinear combined scheme that preserves the maximum principle. In this scheme, a Godunov approximation to the diffusion term ensures that the solutions are bounded without any restriction on the transmissibilities or on the mesh. Finally, in view of adressing the solvability of cardiac electromechanics coupled to physiological ionic models, we considered a model with a linearized description of the passive elastic response of cardiac tissue, a linearized incompressibility constraint, and a truncated approximation of the nonlinear diffusivities appearing in the bidomain equations. The existence proof is done using nondegenerate approximation systems and the Faedo-Galerkin method followed by a compactness argument.

This thesis investigates the high-order hierarchical finite element method, also known as the finite element p-version, as a computationally-efficient technique for generating numerical solutions to the cardiac monodomain equation. We first present it as a uniform-order method, and through an a priori error bound we explain why the associated cardiac cell model must be thought of as a PDE and approximated to high-order in order to obtain the accuracy that the p-version is capable of. We perform simulations demonstrating that the achieved error agrees very well with the a priori error bound. Further, in terms of solution accuracy for time taken to solve the linear system that arises in the finite element discretisation, it is more efficient that the state-of-the-art piecewise linear finite element method. We show that piecewise linear FEM actually introduces quite significant amounts of error into the numerical approximations, particularly in the direction perpendicular to the cardiac fibres with physiological conductivity values, and that without resorting to extremely fine meshes with elements considerably smaller than 70 micrometres, we can not use it to obtain high-accuracy solutions. In contrast, the p-version can produce extremely high accuracy solutions on meshes with elements around 300 micrometres in diameter with these conductivities. Noting that most of the numerical error is due to under-resolving the wave-front in the transmembrane potential, we also construct an adaptive high-order scheme which controls the error locally in each element by adjusting the finite element polynomial basis degree using an analytically-derived a posteriori error estimation procedure. This naturally tracks the location of the wave-front, concentrating computational effort where it is needed most and increasing computational efficiency. The scheme can be controlled by a user-defined error tolerance parameter, which sets the target error within each element as a proportion of the local magnitude of the solution as measured in the H^1 norm. This numerical scheme is tested on a variety of problems in one, two and three dimensions, and is shown to provide excellent error control properties and to be likely capable of boosting efficiency in cardiac simulation by an order of magnitude. The thesis amounts to a proof-of-concept of the increased efficiency in solving the linear system using adaptive high-order finite elements when performing single-thread cardiac simulation, and indicates that the performance of the method should be investigated in parallel, where it can also be expected to provide considerable improvement. In general, the selection of a suitable preconditioner is key to ensuring efficiency; we make use of a variety of different possibilities, including one which can be expected to scale very well in parallel, meaning that this is an excellent candidate method for increasing the efficiency of cardiac simulation using high-performance computing facilities.

Les neurosciences computationnelles ont permis de développer des outils mathématiques et informatiques permettant la création, puis la simulation de modèles représentant le comportement de certaines composantes de notre cerveau à l’échelle cellulaire. Ces derniers sont utiles dans la compréhension des interactions physiques et biochimiques entre les différents neurones, au lieu d’une reproduction fidèle des différentes fonctions cognitives comme dans les travaux sur l’intelligence artificielle. La construction de modèles décrivant le cerveau dans sa globalité, en utilisant une homogénéisation des données microscopiques est plus récent, car il faut prendre en compte la complexité géométrique des différentes structures constituant le cerveau. Il y a donc un long travail de reconstitution à effectuer pour parvenir à des simulations. D’un point de vue mathématique, les différents modèles sont décrits à l’aide de systèmes d’équations différentielles ordinaires, et d’équations aux dérivées partielles. Le problème majeur de ces simulations vient du fait que le temps de résolution peut devenir très important, lorsque des précisions importantes sur les solutions sont requises sur les échelles temporelles mais également spatiales. L’objet de cette étude est d’étudier les différents modèles décrivant l’activité électrique du cerveau, en utilisant des techniques innovantes de parallélisation des calculs, permettant ainsi de gagner du temps, tout en obtenant des résultats très précis. Quatre axes majeurs permettront de répondre à cette problématique : description des modèles, explication des outils de parallélisation, applications sur deux modèles macroscopiques. / Computational Neuroscience helped develop mathematical and computational tools for the creation, then simulation models representing the behavior of certain components of our brain at the cellular level. These are helpful in understanding the physical and biochemical interactions between different neurons, instead of a faithful reproduction of various cognitive functions such as in the work on artificial intelligence. The construction of models describing the brain as a whole, using a homogenization microscopic data is newer, because it is necessary to take into account the geometric complexity of the various structures comprising the brain. There is therefore a long process of rebuilding to be done to achieve the simulations. From a mathematical point of view, the various models are described using ordinary differential equations, and partial differential equations. The major problem of these simulations is that the resolution time can become very important when important details on the solutions are required on time scales but also spatial. The purpose of this study is to investigate the various models describing the electrical activity of the brain, using innovative techniques of parallelization of computations, thereby saving time while obtaining highly accurate results. Four major themes will address this issue: description of the models, explaining parallelization tools, applications on both macroscopic models.

The work developed in this thesis focusses on the electrical activity of the heart, from the modeling of the action potential originating from cardiac cells and propagating through the heart, as well as its electrical manifestation at the body surface. The study is divided in two main parts: modeling the action potential, and numerical simulations. For modeling the action potential a dimensional and asymptotic analysis is done. The key advance in this part of the work is that this analysis gives the steps to reliably control the action potential. It allows predicting the time/space scales and speed of any action potential that is to say the shape of the action potential and its propagation. This can be done as the explicit relations on all the physiological constants are defined precisely. This method facilitates the integrative modeling of a complete human heart with tissue-specific ionic models. It even proves that using a single model for the cardiac action potential is enough in many situations. For efficient numerical simulations, a numerical method for solving the heart-torso coupling problem is explored according to a level set description of the domains. This is done in the perspective of using directly medical images for building computational domains. A finite element method is then developed to manage meshes not adapted to internal interfaces. Finally, an anisotropic adaptive remeshing methods for unstructured finite element meshes is used to efficiently capture propagating action potentials within complex, realistic two dimensional geometries.