The underlying causes of variability in the electrical activity of hearts from individuals of the same species are not well understood. Understanding this variability is important to enable prediction of the response of individual hearts to diseases and therapies. Current experimental and computational methods for investigating the behaviour of the heart do not incorporate biological variation between individuals. In experimental studies, experimental results are averaged together to control errors and determine the average behaviour of the studied organism. In computational studies, averaged experimental data is usually used to develop models, and these models therefore represent a 'typical' organism, with all information on variability within the species having been lost. In this thesis we develop a methodology for modelling variability between individuals of the same species in cardiac cellular electrophysiology, motivated by the inability of traditional computational modelling approaches to capture experimental variability. A first study is conducted using traditional modelling approaches to investigate potentially pro-arrhythmic abnormalities in rabbit Purkinje fibres. A comparison with experimental recordings highlights their wide variability and the inability of existing computer modelling approaches to capture it. This leads to the development of a novel methodology that integrates the variability observed in experimental data with computational modelling and simulation, by building experimentally-calibrated populations of computational models, that collectively span the variability seen in experimental data. We apply this methodology to construct a population of rabbit Purkinje cell models. We show that our population of models can quantitatively predict the range of responses, not just the average response, to application of the potassium channel blocking drug dofetilide. This demonstrates an important potential application of our methodology, for predicting pro-arrhythmic drug effects in safety pharmacology. We then analyse a data set of experimental recordings from human ventricular tissue preparations, and use this data to develop a population of human ventricular cell models. We apply this population to study how variability between individuals alters the susceptibility of cardiac cells to developing drug-induced repolarisation abnormalities. These abnormalities can increase the chance of fatal arrhythmias, but the mechanisms that determine individual susceptibility are not well-understood.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:711815 |
| Date | January 2015 |
| Creators | Britton, Oliver Jonathan |
| Contributors | Rodriguez, Blanca ; Bueno-Orovio, Alfonso |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://ora.ox.ac.uk/objects/uuid:6299240d-0528-4662-8e1f-5025f39e730f |
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