Sampling Methods for Exploring Between Subject Variability in Cardiac Electrophysiology Experiments
48 mins 32 secs,
313.56 MB,
WebM
640x360,
29.97 fps,
44100 Hz,
882.11 kbits/sec
Share this media item:
Embed this media item:
Embed this media item:
About this item
| Description: |
Burrage, K (Queensland University of Technology)
Thursday 7th April 2016 - 11:00 to 11:45 |
|---|
| Created: | 2016-04-12 11:00 |
|---|---|
| Collection: | Stochastic Dynamical Systems in Biology: Numerical Methods and Applications |
| Publisher: | Isaac Newton Institute |
| Copyright: | Burrage, K |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
|
| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | Co-authors: C. C. Drovandi (QUT), N. Cusimano (QUT), S. Psaltis (QUT), A. N. Pettitt (QUT), P. Burrage (QUT)
Between-subject and within-subject variability is ubiquitous in biology and physiology and understanding and dealing with this is one of the biggest challenges in medicine. At the same time it is difficult to investigate this variability by experiments alone. A recent modelling and simulation approach, known as population of models (POM), allows this exploration to take place by building a mathematical model consisting of multiple parameter sets calibrated against experimental data. However, finding such sets within a high-dimensional parameter space of complex electrophysiological models is computationally challenging. By placing the POM approach within a statistical framework, we develop a novel and efficient algorithm based on sequential Monte Carlo (SMC). We compare the SMC approach with Latin hypercube sampling (LHS), a method commonly adopted in the literature for obtaining the POM, in terms of efficiency and output variability in the presence of a drug block through an in-depth investigation via the Beeler-Reuter cardiac electrophysiological model. We show improved efficiency via SMC and that it produces similar responses to LHS when making out-of-sample predictions in the presence of a simulated drug block. |
|---|---|
Available Formats
| Format | Quality | Bitrate | Size | |||
|---|---|---|---|---|---|---|
| MPEG-4 Video | 640x360 | 1.94 Mbits/sec | 707.03 MB | View | Download | |
| WebM * | 640x360 | 882.11 kbits/sec | 313.56 MB | View | Download | |
| iPod Video | 480x270 | 522.12 kbits/sec | 185.60 MB | View | Download | |
| MP3 | 44100 Hz | 249.81 kbits/sec | 88.86 MB | Listen | Download | |
| Auto | (Allows browser to choose a format it supports) | |||||