Conference abstracts

Session A6 - Mathematical Foundations of Data Assimilation and Inverse Problems

July 10, 14:30 ~ 15:00

Unlocking datasets by calibrating populations of models to data density: a study in atrial electrophysiology

Kevin Burrrage

Queensland University of Technology, the University of Oxford, Australia, UK   -

The understanding of complex physical or biological systems nearly always requires a characterisation of the variability that underpins these processes. In addition, the data used to calibrate such models may also often exhibit considerable variability. A recent approach to deal with these issues has been to calibrate populations of models (POMs), that is multiple copies of a single mathematical model but with different parameter values. To date this calibration has been limited to selecting models that produce outputs that fall within the ranges of the dataset, ignoring any trends that might be present in the data. We present here a novel and general methodology for calibrating POMs to the distributions of a set of measured values in a dataset. We demonstrate the benefits of our technique using a dataset from a cardiac atrial electrophysiology study based on the differences in atrial action potential readings between patients exhibiting sinus rhythm (SR) or chronic atrial fibrillation (cAF) and the Courtemanche-Ramirez-Nattel model for human atrial action potentials. Our approach accurately captures the variability inherent in the experimental population, and allows us to identify the differences underlying stratified data as well as the effects of drug block.

Joint work with Brodie A. J. Lawson (Queensland University of Technology, Brisbane, Australia), Christopher C. Drovandi (Queensland University of Technology, Australia), Nicole Cusimano (Basque Center for Applied Mathematics, Bilbao, Spain), Pamela Burrage (Queensland University of Technology, Australia) and Blanca Rodriguez (University of Oxford, Oxford, United Kingdom).

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FoCM 2017, based on a nodethirtythree design.