Electrophysiological Modeling of Channelrhodophsin-2 in Cardiac Cells

Purpose: With the recent interest in Channelrhodopshin-2 (Chr2) in neurological experiments, researchers have begun to investigate the utility of light-activated ion channels in other electrically active cell types, including human embryonic stem cell-derived cardiomyocytes (Abilez et al. 2010). However, the impact of Chr2 in action potential synchronization in cardiac cells is not yet fully realized, as neuronal and cardiac cells differ in electrical behavior. In the past, baseline electrophysiological models for normal neuronal and cardiac cells have been developed and recent attempts have been made to characterize Chr2 in neuron excitation control. However, these approaches do not capture the resulting ion channel current, nor have they been adapted for cardiac cells. By characterizing Chr2 currents within existing cell models, simulations can be conducted concurrently with experiments for principle validation and experiment optimization.Methods: A kinetic model for Chr2 activation (Nikolic et al. 2006) was extended to an ion current formulation from current-voltage comparisons in the literature. This current was introduced into a ventricular cell model (ten Tusscher et al. 2003) and embedded in an implicit non-linear finite element framework (Wong et al. 2010) to perform simulations at cellular, tissue, and organ levels.Results: To illustrate the features of our novel light-activated cell model, we present selected examples to show the benefits of concurrent modeling. At the cellular level, we explore the impact of photostimulation strength, duration, and frequency in Chr2-manipulated ventricular cells. At the tissue level, we evaluate the feasibility of using such manipulated cells as pacemakers in the heart.Conclusion: By “transducing” cell models with Chr2, we can not only virtually probe characteristics of light-activated functional cells for novel applications of Chr2 in cardiac cells and other electrically active cells, but also optimize experiments by qualitatively predicting experimental results.