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Prog Biophys Mol Biol. 2016 Jan;120(1-3):100-14. doi: 10.1016/j.pbiomolbio.2015.12.008. Epub 2015 Dec 23.

Myokit: A simple interface to cardiac cellular electrophysiology.

Author information

1
Department of Data Science and Knowledge Engineering, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands; Department of Cardiology, Cardiovascular Research Institute Maastricht, Maastricht University Medical Center, PO Box 5800, 6202 AZ, Maastricht, The Netherlands. Electronic address: michael.clerx@maastrichtuniversity.nl.
2
Department of Data Science and Knowledge Engineering, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands. Electronic address: pieter.collins@maastrichtuniversity.nl.
3
Department of Data Science and Knowledge Engineering, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands. Electronic address: enno.delange@maastrichtuniversity.nl.
4
Department of Cardiology, Cardiovascular Research Institute Maastricht, Maastricht University Medical Center, PO Box 5800, 6202 AZ, Maastricht, The Netherlands. Electronic address: p.volders@maastrichtuniversity.nl.

Abstract

Myokit is a new powerful and versatile software tool for modeling and simulation of cardiac cellular electrophysiology. Myokit consists of an easy-to-read modeling language, a graphical user interface, single and multi-cell simulation engines and a library of advanced analysis tools accessible through a Python interface. Models can be loaded from Myokit's native file format or imported from CellML. Model export is provided to C, MATLAB, CellML, CUDA and OpenCL. Patch-clamp data can be imported and used to estimate model parameters. In this paper, we review existing tools to simulate the cardiac cellular action potential to find that current tools do not cater specifically to model development and that there is a gap between easy-to-use but limited software and powerful tools that require strong programming skills from their users. We then describe Myokit's capabilities, focusing on its model description language, simulation engines and import/export facilities in detail. Using three examples, we show how Myokit can be used for clinically relevant investigations, multi-model testing and parameter estimation in Markov models, all with minimal programming effort from the user. This way, Myokit bridges a gap between performance, versatility and user-friendliness.

KEYWORDS:

Cardiac action potential; Computational models; Ion channels; Simulation; Software tools

[Indexed for MEDLINE]

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