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Microelectrode Array Recording of Sinoatrial Node Firing Rate to Identify Intrinsic Cardiac Pacemaking Defects in Mice
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Computational analysis of the human sinus node action potential: model development and effects of mutations
Alan Fabbri1, Matteo Fantini1, Ronald Wilders2
1Computational Physiopathology Unit, Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy.
The Journal of Physiology
|February 11, 2017
Summary
Researchers developed a mathematical model of human sinoatrial node (SAN) pacemaker cells. This model accurately simulates electrical activity and predicts heart rate changes due to ion channel mutations, aiding drug development for heart rate modulation.
Area of Science:
- Computational Biology
- Cardiac Electrophysiology
- Mathematical Modeling
Background:
- The sinoatrial node (SAN) is the heart's natural pacemaker.
- Previous models primarily focused on rabbit SAN cells due to extensive data.
- Understanding human SAN cell function is crucial for treating cardiac arrhythmias.
Purpose of the Study:
- To construct a comprehensive mathematical model of human SAN pacemaker cell electrical activity.
- To validate the model against experimental data and clinical observations of ion channelopathies.
- To investigate the role of the 'funny current' (If) in human SAN pacing.
Main Methods:
- Adapted the Severi-DiFrancesco model of rabbit SAN cells for human physiology.
- Incorporated recent electrophysiological data from isolated human SAN pacemaker cells.
- Simulated effects of mutations in HCN4, SCN5A, and KCNQ1 genes to model ion channelopathies.
Main Results:
- The human SAN model accurately reproduces experimentally recorded action potentials and calcium transients.
- Simulated ion channelopathies correlate with observed clinical changes in heart rate.
- The 'funny current' (If) plays a similar modulatory role in human SAN cells as in rabbit cells, despite lower amplitude.
Conclusions:
- The developed human SAN cell model provides a robust tool for studying cardiac pacemaking.
- The model successfully predicts the impact of genetic mutations on heart rate.
- This model can facilitate the design of novel experiments and the development of heart-rate modulating therapies.
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