Action potential–based calibration of line‑specific electrophysiological models explains divergent calcium‑handling phenotypes in iPSC‑cardiomyocytes
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Patient-specific cardiac electrophysiology models are difficult to calibrate due to high parameter dimensionality and scarce direct measurements. We present an AP‑driven approach that isolates the calcium‑handling subsystem, avoiding full optimization of all ionic currents. Using synchronized optical mapping of action potentials (AP) and Ca²⁺ transients in two iPSC‑cardiomyocyte lines—healthy m34Sk3 and HCM11 (MYBPC3 mutation)—we fed the recorded APs directly into the model as a forcing input and optimized six key calcium‑handling parameters (dominant currents and compartment volumes, identified by sensitivity analysis) to match the measured Ca²⁺ transients. This yielded line‑specific models and uncovered the mechanism of diastolic calcium accumulation in HCM11 cells despite identical AP waveforms. Both models were validated against independent patch‑clamp measurements of L‑type calcium current and in free‑running membrane potential simulations; we further demonstrated that coordinated modulation of these parameters can induce analogous calcium dysregulation in any iPSC‑CM. Intrinsic parameter scatter reflected biological variability rather than technical noise, providing a data‑driven alternative to the arbitrary variation common in population modeling. This proof‑of‑concept establishes AP‑driven calibration as an experimentally grounded strategy for building physiologically relevant iPSC‑CM models from optical mapping data, with implications for mechanistic studies of calcium dysregulation and patient‑specific in silico modeling.This dataset contains raw optical mapping data from the HCM11 iPSC-CMs line (data on m34Sk3 taken from previous publications), as well as synthesis data (patch-clamp and free-running 2D modeling)Funding:Ministry of Science and Higher Education of the Russian Federation (project No. FSMG-2026-0018) and M.F. Vladimirsky Moscow Regional Clinical Research Institute state grant #61.
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