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

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.

Citation

Whittaker, D. G., Wang, J., Shuttleworth, J., Venkateshappa, R., Kemp, J. M., Claydon, T. W., & Mirams, G. R. (2022). Ion channel model reduction using manifold boundaries. Journal of the Royal Society, Interface, 19(193), Article 20220193. https://doi.org/10.1098/rsif.2022.0193

• 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
• Publisher: The Royal Society
• 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