Model-guided concurrent data assimilation for calibrating cardiac ion-channel kinetics.

Potassium channels (K_v) are responsible for repolarizing the action potential in cardiomyocytes. There is a variety of K_v isoforms and corresponding currents (e.g. I_Kto, I_Kslow1, I_Kslow2) that contribute to different phases of repolarization. Because only the sum of their activities can be measured in the form of currents (I_Ksum), there is a need to delineate individual K⁺ currents. Most existing studies make inference of K_v activities via curve-fitting procedures but encounter certain limitations as follows: (1) curve-fitting decomposition only relies on the shape of K⁺ current traces, which does not discern the underlying kinetics; (2) I_Ksum traces can only be fitted for one clamp voltage at each time, and then analyzed in a population-averaged way later. This paper presents a novel concurrent data assimilation method to calibrate biophysics-based models and delineate kinetics of K_v isoforms with multiple voltage-clamp responses simultaneously. The proposed method is evaluated and validated with whole-cell I_Ksum recordings from wild-type and chronically glycosylation-deficient cardiomyocytes. Experimental results show that the proposed method effectively handles multiple-response data and describes glycosylation-conferred perturbations to K_v isoforms. Further, we develop a graphical-user-interface (GUI) application that provides an enabling tool to biomedical scientists for data-driven modeling and analysis of K_v kinetics in various heart diseases. [ABSTRACT FROM AUTHOR]