title: Improving cardiomyocyte model fidelity and utility via dynamic electrophysiology protocols and optimization algorithms.
abstract: Mathematical models of cardiac electrophysiology are instrumental in determining mechanisms of cardiac arrhythmias. However, the foundation of a realistic multiscale heart model is only as strong as the underlying cell model. While there have been myriad advances in the improvement of cellular-level models, the identification of model parameters, such as ion channel conductances and rate constants, remains a challenging problem. The primary limitations to this process include: (1) such parameters are usually estimated from data recorded using standard electrophysiology voltage-clamp protocols that have not been developed with model building in mind, and (2) model parameters are typically tuned manually to subjectively match a desired output. Over the last decade, methods aimed at overcoming these disadvantages have emerged. These approaches include the use of optimization or fitting tools for parameter estimation and incorporating more extensive data for output matching. Here, we review recent advances in parameter estimation for cardiomyocyte models, focusing on the use of more complex electrophysiology protocols and global search heuristics. We also discuss future applications of such parameter identification, including development of cell-specific and patient-specific mathematical models to investigate arrhythmia mechanisms and predict therapy strategies.
authors.affiliation: Greenberg Division of Cardiology, Weill Cornell Medical College, New York, NY, USA.|Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY, USA.
authors.forename: Trine
authors.initials: T
authors.lastname: Krogh-Madsen
authors.affiliation: Pharmacology and Systems Therapeutics, Mount Sinai School of Medicine, New York, NY, USA.
authors.forename: Eric A
authors.initials: EA
authors.lastname: Sobie
authors.affiliation: Greenberg Division of Cardiology, Weill Cornell Medical College, New York, NY, USA.|Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY, USA.|Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY, USA.
authors.forename: David J
authors.initials: DJ
authors.lastname: Christini
mesh_terms: D000465:Algorithms; D000818:Animals; D055724:Electrophysiological Phenomena; D006801:Humans; D008954:Models, Biological; D032383:Myocytes, Cardiac / Q000502:physiology*; D066230:Patient-Specific Modeling