Variability in cardiac electrophysiology: Using experimentally-calibrated populations of models to move beyond the single virtual physiological human paradigm

• Published in: Progress in biophysics and molecular biology 2016-01, Vol.120 (1-3), p.115-127
• Main Authors: Muszkiewicz, Anna, Britton, Oliver J., Gemmell, Philip, Passini, Elisa, Sánchez, Carlos, Zhou, Xin, Carusi, Annamaria, Quinn, T. Alexander, Burrage, Kevin, Bueno-Orovio, Alfonso, Rodriguez, Blanca
• Format: Article
• Language: English
• Subjects: Action potential; Arrhythmias; Calibration; Cardiac electrophysiology; Electrophysiological Phenomena; Heart - physiology; Humans; In silico high-throughput screening; Models, Cardiovascular; Physiological variability; Populations of models; User-Computer Interface
• ISSN: 0079-6107; 1873-1732
• DOI: 10.1016/j.pbiomolbio.2015.12.002

Physiological variability manifests itself via differences in physiological function between individuals of the same species, and has crucial implications in disease progression and treatment. Despite its importance, physiological variability has traditionally been ignored in experimental and computational investigations due to averaging over samples from multiple individuals. Recently, modelling frameworks have been devised for studying mechanisms underlying physiological variability in cardiac electrophysiology and pro-arrhythmic risk under a variety of conditions and for several animal species as well as human. One such methodology exploits populations of cardiac cell models constrained with experimental data, or experimentally-calibrated populations of models. In this review, we outline the considerations behind constructing an experimentally-calibrated population of models and review the studies that have employed this approach to investigate variability in cardiac electrophysiology in physiological and pathological conditions, as well as under drug action. We also describe the methodology and compare it with alternative approaches for studying variability in cardiac electrophysiology, including cell-specific modelling approaches, sensitivity-analysis based methods, and populations-of-models frameworks that do not consider the experimental calibration step. We conclude with an outlook for the future, predicting the potential of new methodologies for patient-specific modelling extending beyond the single virtual physiological human paradigm.