Key result
Automated cloud computing pipeline streamlines ion channel Markov modeling for electrophysiologists.
Why the study?
Computational modeling of ion channels provides key insights into electrophysiology, but creating these models requires substantial mathematical and computational background.
This protocol lowers the barrier to entry for experimental electrophysiologists to create complex kinetic models from raw voltage-clamp datasets.
Computational modeling of ion channels provides key insight into experimental electrophysiology results and can be used to connect channel dynamics to emergent phenomena observed at the tissue and organ levels. However, creation of these models requires substantial mathematical and computational background. This tutorial seeks to lower the barrier to creating these models by providing an automated pipeline for creating Markov models of an ion channel kinetics dataset. We start by detailing how to encode sample voltage-clamp protocols and experimental data into the program and its implementation in a cloud computing environment. We guide the reader on how to build a containerized instance, push the machine image, and finally run the routine on cluster nodes. While providing open-source code has become more standard in computational studies, this tutorial provides unprecedented detail on the use of the program and the creation of channel models, starting from inputting the raw experimental data.
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Mangold et al. (2022) studied this question. Automated pipeline for creating Markov models was evaluated. An automated cloud computing pipeline enables the creation of Markov models for ion channel kinetics, lowering the mathematical and computational barriers for experimental electrophysiologists.
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