Dissecting Heart Failure mechanisms by integrating in vivo and in vitro data within customised in silico models
Lead Research Organisation:
University of Oxford
Department Name: Computer Science
Abstract
Heart Failure (HF) is defined by the heart?s reduced ability to pump blood due to a drop in left ventricular (LV) systolic and diastolic function. With the improved survival after a heart attack and the expansion of the U.K.?s elderly population, HF is rapidly becoming an epidemic accounting for a significant mortality and morbidity burden.
Recently, the strength of new experimental techniques has contributed several new pieces to the mechanistic puzzle that underpins the clinical syndrome of HF. Despite these efforts, however, our knowledge of this important process remains fragmented, hampering the identification of robust targets for therapeutic intervention.
The discipline of computational cardiac physiology offers an exciting approach to address this issue by quantitatively describing the physiological behavior of the heart using mathematical and computational models. The development of such models presents the ability capture the complex and multi-factorial cause and effect relationships which link underlying patho-physiological mechanisms. Furthermore, the heart is arguable the most advanced example of an integrated model and as such represents exciting tool with which to focus, in the case of HF, on an important disease.
Combining this computational technology with state of the art in-vivo and in-vitro experimental techniques we aim to assimilate multiple data sets to test our understanding of HF mechanisms within a consistent model. Our preliminary experimental measurements indicate that in HF individual cardiac cells may continue to contract normally, however, it is a change in the way they are connected in heart tissue which may be adversely affecting contraction. Applying our integrated experimental and computational approach we will be able to test this hypothesis and in doing so indicate a potentially brand new direction for the development of new treatment strategies for this disease.
Recently, the strength of new experimental techniques has contributed several new pieces to the mechanistic puzzle that underpins the clinical syndrome of HF. Despite these efforts, however, our knowledge of this important process remains fragmented, hampering the identification of robust targets for therapeutic intervention.
The discipline of computational cardiac physiology offers an exciting approach to address this issue by quantitatively describing the physiological behavior of the heart using mathematical and computational models. The development of such models presents the ability capture the complex and multi-factorial cause and effect relationships which link underlying patho-physiological mechanisms. Furthermore, the heart is arguable the most advanced example of an integrated model and as such represents exciting tool with which to focus, in the case of HF, on an important disease.
Combining this computational technology with state of the art in-vivo and in-vitro experimental techniques we aim to assimilate multiple data sets to test our understanding of HF mechanisms within a consistent model. Our preliminary experimental measurements indicate that in HF individual cardiac cells may continue to contract normally, however, it is a change in the way they are connected in heart tissue which may be adversely affecting contraction. Applying our integrated experimental and computational approach we will be able to test this hypothesis and in doing so indicate a potentially brand new direction for the development of new treatment strategies for this disease.
Technical Summary
Heart Failure (HF) is defined by the heart?s reduced ability to pump blood due to a drop in left ventricular (LV) systolic and diastolic function. With the improved survival from ischaemic heart disease and the resulting expansion of the elderly population, HF is rapidly becoming an epidemic accounting for a significant mortality and morbidity burden in both the industrialised and developing world. Recently, the combined strength of novel experimental techniques and genetically modified animal models has contributed several new pieces to the mechanistic puzzle that underpins the clinical syndrome of HF. Despite these efforts, however, our knowledge of this important process remains fragmented, hampering the identification of robust targets for therapeutic intervention. It is generally accepted that abnormal intracellular Ca2+ handling and reduced myocyte contractility account for the depressed LV systolic and diastolic function in failing hearts. However, it has become apparent that impaired LV performance can also occur in the absence of obvious abnormalities at the single myocyte level. This illustrates the importance of integrating single cell data to their three-dimensional structural and mechanical environment.
Computational modelling approaches, which have arguably been most successfully applied to the heart, provide the ability to perform exactly this type of multi-scale integration. Specifically, existing models now enable the linking of intracellular Ca2+ handling mechanisms to cardiomyocyte characteristics and whole heart pump function. However, these models are currently of limited use for the direct interpretation of myocardial function and electrophysiology in the mouse (the most widely used experimental model of human cardiomyopathy and heart failure) due to model parameters being based on inhomogeneous data.
Our aim in this proposal is to integrate in-vitro cell measurements with in-vivo invasive measurement and imaging techniques (MR and confocal) to parameterize and fit the cellular and organ models, respectively, using consistent data sets collected from a well-established and widely used murine model of HF following a 30-40% infarction of the LV wall (obtained by proximal ligation of the left anterior descendent coronary artery). Using this model we will focus on addressing the specific question: does the altered spatial distribution of myocyte size, contractility and orientation within the remodelled myocardium contribute to reduced pump function in heart failure, in the absence of significant changes in myocyte contractility and Ca2+ handling? By developing a specific mouse model of excitation-contraction dynamics, we hope to accelerate the development of new treatment strategies and customized clinical care.
Computational modelling approaches, which have arguably been most successfully applied to the heart, provide the ability to perform exactly this type of multi-scale integration. Specifically, existing models now enable the linking of intracellular Ca2+ handling mechanisms to cardiomyocyte characteristics and whole heart pump function. However, these models are currently of limited use for the direct interpretation of myocardial function and electrophysiology in the mouse (the most widely used experimental model of human cardiomyopathy and heart failure) due to model parameters being based on inhomogeneous data.
Our aim in this proposal is to integrate in-vitro cell measurements with in-vivo invasive measurement and imaging techniques (MR and confocal) to parameterize and fit the cellular and organ models, respectively, using consistent data sets collected from a well-established and widely used murine model of HF following a 30-40% infarction of the LV wall (obtained by proximal ligation of the left anterior descendent coronary artery). Using this model we will focus on addressing the specific question: does the altered spatial distribution of myocyte size, contractility and orientation within the remodelled myocardium contribute to reduced pump function in heart failure, in the absence of significant changes in myocyte contractility and Ca2+ handling? By developing a specific mouse model of excitation-contraction dynamics, we hope to accelerate the development of new treatment strategies and customized clinical care.