Myokit

Citing Myokit

If you're using Myokit in your research, please cite:

All feedback, positive and negative, is welcome! If you are using Myokit for research or education, please let us know by emailing Michael Clerx (michael@myokit.org).

Myokit locations. Click to enlarge.

Publications using Myokit

1. Beneficial normalization of cardiac repolarixation by carnitine in transgenic short QT syndrome type 1 rabbit models Bodi et al. 2024 Cardiovascular Research doi: 10.1093/cvr/cvae149
2. Learning the Hodgkin-Huxley Model with Operator Learning Techniques Centofanti et al. 2024 arxiv doi: 10.48550/arXiv.2406.02173
3. Single-cell ionic current phenotyping elucidates non-canonical features and predictive potential of cardiomyocytes during automated drug experiments Clark et al. 2024 Journal of Physiology doi: 10.1113/jp285120
4. Single-cell ionic current phenotyping explains stem cell-derived cardiomyocyte action potential morphology Clark et al. 2024 AJPHeart doi: 10.1152/ajpheart.00063.2024
5. Novel Gain-of-Function Mutation in the Kv11.1 Channel Found in Patient with Brugada Syndrome and Mild QTc Shortening Abramochkin et al. 2024 Biochemistry (Moscow) doi: 10.1134/s000629792403012x
6. The impact of high frequency-based stability on the onset of action potentials in neuron models Cerpa et al. 2024 arxiv doi: 10.48550/arXiv.2402.05886
7. Chi: A Python package for treatment response modelling Augustin 2024 Journal of Open Source Software doi: 10.21105/joss.05925
8. Optimization of a Cardiomyocyte Model Illuminates Role of Increased INaL in Repolarization Reserve Fullerton et al. 2024 AJPHeart doi: ajpheart.00553.2023
9. Non-Invasive Electroanatomical Mapping; A State-Space Approach for Myocardial Current Density Estimation Engelhardt et al. 2023 Bioengineering doi: 10.3390/bioengineering10121432
10. Boundary Integral Formulation of the Cell-by-Cell Model of Cardiac Electrophysiology De Souza et al. 2023 Engineering Analysis with Boundary Elements doi: j.enganabound.2023.10.021
11. Model-driven optimal experimental design for calibrating cardiac electrophysiology models Lei et al. 2023 Computer Methods and Programs in Biomedicine doi: 10.1016/j.cmpb.2023.107690
12. A concept for myocardial current density estimation with magnetoelectric sensors Engelhardt et al. 2023 Current Directions in Biomedical Engineering doi: 10.1515/cdbme-2023-1023
13. Leak current, even with gigaohm seals, can cause misinterpretation of stem cell-derived cardiomyocyte action potential recordings Clark et al. 2023 Europace doi: 10.1093/europace/euad243
14. Modeling the functional heterogeneity and conditions for the occurrence of microreentry in procedurally created atrial fibrous tissue Kalinin et al. 2023 Journal of Applied Physics doi: 10.1063/5.0151624
15. Mathematical Modelling of Leptin-Induced Effects on Electrophysiological Properties of Rat Cardiomyocytes and Cardiac Arrhythmias Nesterova et al. 2023 Mathematics doi: 10.3390/math11040874
16. Filter inference; A scalable nonlinear mixed effects inference approach for snapshot time series data Augustin et al. 2023 PLOS Computational Biology doi: 10.1371/journal.pcbi.1011135
17. Electrophysiological and calcium-handling development during long-term culture of human-induced pluripotent stem cell-derived cardiomyocytes Seibertz et al. 2023 Basic Research in Cardiology doi: 10.1007/s00395-022-00973-0
18. ActionPytential; An open source tool for analyzing and visualizing cardiac action potential data Arpadffy-Lovas & Nagy 2023 Heliyon doi: 10.1016/j.heliyon.2023.e14440
19. Importance of modelling hERG binding in predicting drug-induced action potential prolongations for drug safety assessment Farm et al. 2023 Frontiers in Pharmacology doi: 10.3389/fphar.2023.1110555
20. In silico analysis of the dynamic regulation of cardiac electrophysiology by Kv11.1 ion-channel trafficking Meier et al. 2023 Journal of Physiology doi: 10.1113/JP283976
21. Improving the hERG model fitting using a deep learning-based method Song et al. 2023 Frontiers in Physiology doi: 10.3389/fphys.2023.1111967
22. Modelling the Effect of Intracellular Calcium in the Rundown of L-Type Calcium Current Agrawal et al. 2022 Computing in Cardiology 2022 doi: 10.22489/CinC.2022.051
23. Normalisation of Action Potential Data Recorded with Sharp Electrodes Maximises Its Utility for Model Development Barral et al. 2022 Computing in Cardiology 2022 doi: 10.22489/CinC.2022.356
24. Derivative-based Inference for Cell and Channel Electrophysiology Models Clerx et al. 2022 Computing in Cardiology 2022 doi: 10.22489/CinC.2022.287
25. Grapefruit Flavonoid Naringenin Sex-Dependently Modulates Action Potential in an In Silico Human Ventricular Cardiomyocyte Model Sutanto et al. 2022 Antioxidants doi: 10.3390/antiox11091672
26. Models of the cardiac L-type calcium current: A quantitative review Agrawal et al. 2022 Wires Mechanisms of Disease doi: 10.1002/wsbm.1581
27. Ion channel model reduction using manifold boundaries Whittaker et al. 2022 Journal of the Royal Society: Interface doi: 10.1098/rsif.2022.0193
28. Disruption of a Conservative Motif in the C-Terminal Loop of the KCNQ1 Channel Causes LQT Syndrome Karlova et al. 2022 International journal of molecular sciences doi: 10.3390/ijms23147953
29. A parameter representing missing charge should be considered when calibrating action potential models Barral et al. 2022 Frontiers in Physiology doi: 10.3389/fphys.2022.879035
30. Integrative Computational Modeling of Cardiomyocyte Calcium Handling and Cardiac Arrhythmias Current Status and Future Challenges Sutanto & Heijman 2022 Cells doi: 10.3390/cells11071090
31. Treatment response prediction: Is model selection unreliable? Augustin et al. 2022 bioRxiv doi: 10.1101/2022.03.19.483454
32. Novel insights into the electrophysiology of murine cardiac macrophages: relevance of voltage-gated potassium channels Simon-Chica et al. 2022 Cardiovascular Research doi: 10.1093/cvr/cvab126
33. Individual Contributions of Cardiac Ion Channels on Atrial Repolarization and Reentrant Waves: A Multiscale In-Silico Study Sutanto 2022 Journal of Cardiovascular Development and Disease doi: 10.3390/jcdd9010028
34. Spatiotemporal approximation of cardiac activation and recovery isochrones Cluitmans et al. 2021 Journal of Electrocardiology doi: 10.1016/j.jelectrocard.2021.12.007
35. Anatomical Model of Rat Ventricles to Study Cardiac Arrhythmias under Infarction Injury Rokeakh et al. 2021 Mathematics doi: 10.3390/math9202604
36. Sex Differences in Drug-Induced Arrhythmogenesis Peirlinck et al. 2021 Frontiers in Physiology doi: 10.3389/fphys.2021.708435
37. Electrophysiological characterization of the hERG R56Q LQTS variant and targeted rescue by the activator RPR260243 Kemp et al. 2021 Journal of General Physiology doi: 10.1085/jgp.202112923
38. Immediate and delayed response of simulated human atrial myocytes to clinically-relevant hypokalemia Clerx et al. 2021 Frontiers in Physiology doi: 10.3389/fphys.2021.651162
39. Cellular Mechanisms of the Anti-Arrhythmic Effect of Cardiac PDE2 Overexpression Wagner et al. 2021 International journal of molecular sciences doi: 10.3390/ijms22094816
40. Caveolin3 Stabilizes McT1-Mediated Lactate/Proton Transport in Cardiomyocytes Peper et al. 2021 Circulation Research doi: 10.1161/CIRCRESAHA.119.316547
41. Evolution of mathematical models of cardiomyocyte electrophysiology Amuzescu et al. 2020 Mathematical Biosciences doi: 10.1016/j.mbs.2021.108567
42. Beta-Adrenergic Receptor Stimulation Limits the Cellular Proarrhythmic Effects of Chloroquine and Azithromycin Sutanto et al. 2020 Frontiers in Physiology doi: 10.3389/fphys.2020.587709
43. Acute effects of alcohol on cardiac electrophysiology and arrhythmogenesis: Insights from multiscale in silico analyses Sutanto et al. 2020 Journal of Molecular and Cellular Cardiology doi: 10.1016/j.yjmcc.2020.07.007
44. In-silico analysis of aging mechanisms of action potential remodeling in human atrial cardiomyocites Nesterova et al. 2020 Longevity Interventions 2020 doi: 10.1051/bioconf/20202201025
45. Self-restoration of cardiac excitation rhythm by anti-arrhythmic ion channel gating Majumder et al. 2020 eLife doi: 10.7554/eLife.55921
46. Reducing complexity and unidentifiability when modelling human atrial cells Houston et al. 2020 Philosophical Transactions of the Royal Society A doi: 10.1098/rsta.2019.0339
47. Considering discrepancy when calibrating a mechanistic electrophysiology model Lei et al. 2020 Philosophical Transactions of the Royal Society A doi: 10.1098/rsta.2019.0349
48. Accounting for variability in ion current recordings using a mathematical model of artefacts in voltage-clamp experiments Lei et al. 2020 Philosophical Transactions of the Royal Society A doi: 10.1098/rsta.2019.0348
49. Calibration of ionic and cellular cardiac electrophysiology models Whittaker, Clerx et al. 2020 WIREs Systems Biology and Medicine doi: 10.1002/wsbm.1482
50. Temporal irregularity quantification and mapping of optical action potentials using wave morphology similarity O'Shea et al. 2020 Progress in Biophysics and Molecular Biology doi: 10.1016/j.pbiomolbio.2019.12.004
51. Four ways to fit an ion channel model Clerx et al. 2019 Biophysical Journal doi: 10.1016/j.bpj.2019.08.001
52. Rapid characterisation of hERG channel kinetics II: temperature dependence Lei et al. 2019 Biophysical Journal doi: 10.1016/j.bpj.2019.07.030
53. Rapid characterisation of hERG channel kinetics I: using an automated high-throughput system Lei et al. 2019 Biophysical Journal doi: 10.1016/j.bpj.2019.07.029
54. Analysis of approaches to building a probability density function for the mathematical model parameters of rat atrial cardiomyocytes Shmarko et al. 2019 AIP Conference Proceedings doi: 10.1063/1.5134405
55. In silico study of the aging of cardiomyocytes in human and canine atriums Nesterova et al. 2019 AIP Conference Proceedings doi: 10.1063/1.5134382
56. Unsupervised learning to analysis of population of models in computational electrophysiological studies Ushenin et al. 2019 SIBIRCON doi: 10.1109/SIBIRCON48586.2019.8958141
57. Maastricht antiarrhythmic drug evaluator (MANTA): a computational tool for better understanding of antiarrhythmic drugs Sutanto et al. 2019 Pharmacological Research doi: 10.1016/j.phrs.2019.104444
58. Modelling the Electrical Activity of the Heart Alonso & Weber dos Santos 2019 In: Cardiovascular Computing - Methodologies and Clinical Applications doi: 10.1007/978-981-10-5092-3_10
59. Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling Cantwell et al. 2019 Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2018.10.015
60. Myocyte Remodeling Due to Fibro-Fatty Infiltrations Influences Arrhythmogenicity De Coster et al. 2018 Frontiers in Physiology doi: 10.3389/fphys.2018.01381
61. Muscarinic type-1 receptors contribute to IK,ACh in human atrial cardiomyocytes and are upregulated in patients with chronic atrial fibrillation Heijman et al. 2017 International Journal of Cardiology doi: 10.1016/j.ijcard.2017.12.050
62. Tailoring Mathematical Models to Stem-Cell Derived Cardiomyocyte Lines Can Improve Predictions of Drug-Induced Changes to Their Electrophysiology Lei et al. 2017 Frontiers in Physiology doi: 10.3389/fphys.2017.00986
63. Spatial Patterns of Excitation at Tissue and Whole Organ Level Due to Early Afterdepolarizations Vandersickel et al. 2017 Frontiers in Physiology doi: 10.3389/fphys.2017.00404
64. Beta-adrenergic receptor stimulation inhibits proarrhythmic alternans in post-infarction border zone cardiomyocytes Tomek et al. 2017 AJPHeart doi: 10.1152/ajpheart.00094.2017
65. Inverse remodelling of K2P3.1 K+ channel expression and action potential duration in left ventricular dysfunction and atrial fibrillation: implications for patient-specific antiarrhythmic drug therapy Schmidt et al. 2017 European Heart Journal doi: 10.1093/eurheartj/ehw559
66. Physiology-based Regularization of the Electrocardiographic Inverse Problem Cluitmans et al. 2017 Medical & Biological Engineering & Computing doi: 10.1007/s11517-016-1595-5
67. Fhf2 gene deletion causes temperature-sensitive cardiac conduction failure Park et al. 2016 Nature Communications doi: 10.1038/ncomms12966
68. In silico evaluation of the potential antiarrhythmic effect of Epigallocatechin-3-Gallate on cardiac channelopathies Boukhabza et al. 2016 Computational and Mathematical Methods in Medicine doi: 10.1155/2016/7861653
69. Bioelectric memory: modeling resting potential bistability in amphibian embryos and mammalian cells Law & Levin 2015 Theoretical Biology and Medical Modelling doi: 10.1186/s12976-015-0019-9
70. Applying novel identification protocols to Markov models of INa Clerx et al. 2015 Computing in Cardiology 2015 Download pre-print
71. Reducing run-times of excitable cell models by replacing computationally expensive functions with splines Clerx & Collins 2014 21st International Symposium on Mathematical Theory of Networks and Systems, July 7-11, 2014, University of Groningen, Groningen, The Netherlands Download author's copy (Copyright IEEE)

Education using Myokit

I am proud and happy to report that Myokit is being used as an educational tool to let students explore cardiac cellular electrophysiology!

The following people have told me they use Myokit in their classes:

• Dr. Mohamed-Yassine Amarouch has used Myokit to teach practical classes on physiology at the University of Sidi Mohamed Ben Abdellah of Fès, Multidisciplinary faculty of Taza.
• Dr. Hannes Todt used Myokit in a course on "Science and Medicine", at the Medical University of Vienna. Myokit was used to simulate experiments which the students were then asked to evaluate.