BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Talks.cam//talks.cam.ac.uk// X-WR-CALNAME:Talks.cam BEGIN:VEVENT SUMMARY:Sampling Methods for Exploring Between Subject Variability in Card iac Electrophysiology Experiments - Kevin Burrage (Queensland University o f Technology) DTSTART:20160407T100000Z DTEND:20160407T104500Z UID:TALK65368@talks.cam.ac.uk CONTACT:INI IT DESCRIPTION:Co-authors: C. C. Drovandi (QUT)\, N. Cusimano (QUT)\, S. Psal tis (QUT)\, A. N. Pettitt (QUT)\, P. Burrage (QUT)

Betw een-subject and within-subject variability is ubiquitous in biology and ph ysiology and understanding and dealing with this is one of the biggest cha llenges in medicine. At the same time it is difficult to investigate this variability by experiments alone. A recent modelling and simulation approa ch\, known as population of models (POM)\, allows this exploration to take place by building a mathematical model consisting of multiple parameter s ets calibrated against experimental data. However\, finding such sets with in a high-dimensional parameter space of complex electrophysiological mode ls is computationally challenging. By placing the POM approach within a st atistical framework\, we develop a novel and efficient algorithm based on sequential Monte Carlo (SMC). We compare the SMC approach with Latin hyper cube sampling (LHS)\, a method commonly adopted in the literature for obta ining the POM\, in terms of efficiency and output variability in the prese nce of a drug block through an in-depth investigation via the Beeler-Reute r cardiac electrophysiological model. We show improved efficiency via SMC and that it produces similar responses to LHS when making out-of-sample pr edictions in the presence of a simulated drug block. LOCATION:Seminar Room 1\, Newton Institute END:VEVENT END:VCALENDAR