Four Ways to Fit an Ion Channel Model

Clerx, M.; Beattie, K.A.; Gavaghan, D.J.; Mirams, G.R.

Abstract

© 2019 Biophysical Society Mathematical models of ionic currents are used to study the electrophysiology of the heart, brain, gut, and several other organs. Increasingly, these models are being used predictively in the clinic, for example, to predict the risks and results of genetic mutations, pharmacological treatments, or surgical procedures. These safety-critical applications depend on accurate characterization of the underlying ionic currents. Four different methods can be found in the literature to fit voltage-sensitive ion channel models to whole-cell current measurements: method 1, fitting model equations directly to time-constant, steady-state, and I-V summary curves; method 2, fitting by comparing simulated versions of these summary curves to their experimental counterparts; method 3, fitting to the current traces themselves from a range of protocols; and method 4, fitting to a single current trace from a short and rapidly fluctuating voltage-clamp protocol. We compare these methods using a set of experiments in which hERG1a current was measured in nine Chinese hamster ovary cells. In each cell, the same sequence of fitting protocols was applied, as well as an independent validation protocol. We show that methods 3 and 4 provide the best predictions on the independent validation set and that short, rapidly fluctuating protocols like that used in method 4 can replace much longer conventional protocols without loss of predictive ability. Although data for method 2 are most readily available from the literature, we find it performs poorly compared to methods 3 and 4 both in accuracy of predictions and computational efficiency. Our results demonstrate how novel experimental and computational approaches can improve the quality of model predictions in safety-critical applications.

Citation

Clerx, M., Beattie, K., Gavaghan, D., & Mirams, G. (2019). Four Ways to Fit an Ion Channel Model. Biophysical Journal, 117(12), 2420-2437. https://doi.org/10.1016/j.bpj.2019.08.001

• Journal Article Type: Article
• Acceptance Date: Aug 1, 2019
• Online Publication Date: Aug 6, 2019
• Publication Date: Dec 17, 2019
• Deposit Date: Aug 1, 2019
• Publicly Available Date: Feb 16, 2020
• Journal: Biophysical Journal
• Print ISSN: 0006-3495
• Electronic ISSN: 1542-0086
• Publisher: Biophysical Society
• Peer Reviewed: Peer Reviewed
• Volume: 117
• Issue: 12
• Pages: 2420-2437
• DOI: https://doi.org/10.1016/j.bpj.2019.08.001
• Public URL: https://nottingham-repository.worktribe.com/output/1881708
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0006349519306666
• Related Public URLs: https://www.biorxiv.org/content/10.1101/609875v1