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

Abstract

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.

Keywords:

• Data assimilation
• simulation modeling
• model calibration
• cardiac potassium channels
• congenital disorders of glycosylation

Disclosure statement

The authors report there are no competing interests to declare.

Additional information

Funding

This work is supported by the National Science Foundation (MCB-1856132 to HY and HK), (MCB-1856199 to EB and AE), (IOS-1146882 and IOS-1660926 to EB), and the American Heart Association (Postdoctoral Fellowship 15POST25710010 to AE). HY and HK would also like to thank the NSF I/UCRC Center for Health Organization Transformation (CHOT) award NSF IIP-1624727 for the support of their research work. Any opinions, findings, or conclusions found in this research are those of the authors and do not necessarily reflect the views of the sponsors.