Dominic G Whittaker
Ion channel model reduction using manifold boundaries
Whittaker, Dominic G; Wang, Jiahui; Shuttleworth, Joseph; Venkateshappa, Ravichandra; Kemp, Jake M; Claydon, Thomas W; Mirams, Gary R
Authors
Jiahui Wang
Joseph Shuttleworth
Ravichandra Venkateshappa
Jake M Kemp
Thomas W Claydon
Prof. GARY MIRAMS GARY.MIRAMS@NOTTINGHAM.AC.UK
Professor of Mathematical Biology
Abstract
Mathematical models of voltage-gated ion channels are used in basic research, industrial and clinical settings. These models range in complexity, but typically contain numerous variables representing the proportion of channels in a given state, and parameters describing the voltage-dependent rates of transition between states. An open problem is selecting the appropriate degree of complexity and structure for an ion channel model given data availability. Here, we simplify a model of the cardiac human Ether-à-go-go Related Gene (hERG) potassium ion channel, which carries cardiac IKr, using the manifold boundary approximation method (MBAM). The MBAM approximates high-dimensional model-output manifolds by reduced models describing their boundaries, resulting in models with fewer parameters (and often variables). We produced a series of models of reducing complexity starting from an established 5-state hERG model with 15 parameters. Models with up to 3 fewer states and 8 fewer parameters were shown to retain much of the predictive capability of the full model and were validated using experimental hERG1a data collected in HEK293 cells at 37°C. The method provides a way to simplify complex models of ion channels that improves parameter identifiability and will aid in future model development.
Journal Article Type | Article |
---|---|
Acceptance Date | Jul 19, 2022 |
Online Publication Date | Aug 10, 2022 |
Publication Date | Aug 10, 2022 |
Deposit Date | Aug 10, 2022 |
Publicly Available Date | Aug 15, 2022 |
Journal | Journal of the Royal Society Interface |
Print ISSN | 1742-5689 |
Electronic ISSN | 1742-5662 |
Peer Reviewed | Peer Reviewed |
Volume | 19 |
Issue | 193 |
Article Number | 20220193 |
DOI | https://doi.org/10.1098/rsif.2022.0193 |
Public URL | https://nottingham-repository.worktribe.com/output/7578208 |
Publisher URL | https://royalsocietypublishing.org/doi/10.1098/rsif.2022.0193 |
Files
Rsif.2022.0193
(3.5 Mb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
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