Reproducible model development in the Cardiac Electrophysiology Web Lab

Daly, Aidan C.; Clerx, Michael; Beattie, Kylie A.; Cooper, Jonathan; Gavaghan, David J.; Mirams, Gary R.

Abstract

The modelling of the electrophysiology of cardiac cells is one of the most mature areas of systems biology. This extended concentration of research effort brings with it new challenges, foremost among which is that of choosing which of these models is most suitable for addressing a particular scientific question. In a previous paper, we presented our initial work in developing an online resource for the characterisation and comparison of electrophysiological cell models in a wide range of experimental scenarios. In that work, we described how we had developed a novel protocol language that allowed us to separate the details of the mathematical model (the majority of cardiac cell models take the form of ordinary differential equations) from the experimental protocol being simulated. We developed a fully-open online repository (which we termed the Cardiac Electrophysiology Web Lab) which allows users to store and compare the results of applying the same experimental protocol to competing models. In the current paper we describe the most recent and planned extensions of this work, focused on supporting the process of model building from experimental data. We outline the necessary work to develop a machine-readable language to describe the process of inferring parameters from wet lab datasets, and illustrate our approach through a detailed example of fitting a model of the hERG channel using experimental data. We conclude by discussing the future challenges in making further progress in this domain towards our goal of facilitating a fully reproducible approach to the development of cardiac cell models.

Citation

Daly, A. C., Clerx, M., Beattie, K. A., Cooper, J., Gavaghan, D. J., & Mirams, G. R. (2018). Reproducible model development in the Cardiac Electrophysiology Web Lab. Progress in Biophysics and Molecular Biology, 139, 3-14. https://doi.org/10.1016/j.pbiomolbio.2018.05.011

• Journal Article Type: Article
• Acceptance Date: May 23, 2018
• Online Publication Date: May 26, 2018
• Publication Date: 2018-11
• Deposit Date: May 29, 2018
• Publicly Available Date: Jul 18, 2019
• Journal: Progress in Biophysics and Molecular Biology
• Print ISSN: 0079-6107
• Electronic ISSN: 0079-6107
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 139
• Pages: 3-14
• DOI: https://doi.org/10.1016/j.pbiomolbio.2018.05.011
• Public URL: https://nottingham-repository.worktribe.com/output/934006
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0079610718300257