Clinical Translation of Cardiac Models
The ability to measure the heart, its shape, its structure and its function across multiple spatial and temporal scales continues to grow. Interpreting this data remains challenging. Computational biophysical models of the heart allow us to quantitatively link and interpret these large disparate data sets within the context of known cardiac physiology and invariable physical constraints. Within these models, we can infer unobservable states, propose and test new hypothesis and predict how systems will respond to challenges increasing our ability to interrogate and understand biological systems. We are increasingly applying this approach to modelling human hearts to investigate clinical applications. In this presentation, I will give an overview on our modelling work simulating cardiac function, how we are using models of individual patients to study cardiac resynchronisation therapy and how we are using simulations to characterise the anatomy and pathophysiology of atrial fibrillation patients.
Uncertainty Quantification for Ion Channel and Action Potential Models
Cardiac models are beginning to be used in designing patient specific therapies and establishing the pro-arrhythmic risk of new pharmaceuticals. These applications are safety critical and simulation predictions must be associated with relevant uncertainties to be useful in decision making. In this talk I will highlight the uncertainties involved in ion channel and action potential modelling in terms of parameters as well as model equations/structure. We have been using the hERG/IKr potassium channel as an example with important implications for prediction of drug-induced block effects. Linking the design of experimental protocols to parameterisation and model selection is fundamental to making progress, and separation of training/calibration/fitting experiments from testing/validation experiments is another crucial step. We need to unambiguously describe the set of experiments that need to be done to build and test a particular model, ensuring that these experiments inform us about all aspects that need to be calibrated, and describe the fitting/inference algorithms by which we use the resulting data to parameterise a model. I will also show some of our efforts to publish this process in a reproducible manner via the Cardiac Electrophysiology Web Lab.