Authors
Pei-Chi Yang, Mao-Tsuen Jeng, Deborah K Lieu, Regan L Smithers, Gonzalo Hernandez-Hernandez, L Fernando Santana, Colleen E Clancy
Published in
eLife. Volume 15. Jul 23, 2026. Epub Jul 23, 2026.
Abstract
Individual variability shapes how diseases manifest, how patients respond to therapy and how rare phenotypes arise. Conventional experimental approaches obscure variation by averaging which limits mechanistic insight and predictive accuracy. We present a computational framework that builds digital twins of human-induced pluripotent stem cell-derived cardiomyocytes from a single optimized voltage clamp experiment. The framework depends on massive synthetic datasets comprising simulated cells that span broad ionic and electrophysiological ranges. These synthetic data make it possible to control parameters precisely, explore biological variability comprehensively, and train models beyond the limits of experimental data. A neural network trained on synthetic data then inferred biophysical parameters from experimental recordings from live cells, reproducing distinct electrophysiological features. Our study unites computational modeling, data simulation, and learning to enable scalable, precise, individualized cardiac electrophysiology modeling and can be readily extended to any electrically active cell type.
PMID:
42489674
Bibliographic data and abstract were imported from PubMed on 23 Jul 2026.
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