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Embracing uncertainty and variabiilty in the design of cardiac computational models

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FHTW01 - Uncertainty quantification for cardiac models

The traditional paradigm for developing cardiac computational cell models utilizes data from multiple cell types, species, laboratories, and experimental conditions to create a composite model. While such models can accurately represent data in limited biological scenarios, their ability to predict behavior outside of a narrow dynamic window is limited. This talk will describe the rationale behind using novel electrophysiological protocols that aim to densely sample the dynamics of intact cardiac myocytes. The cell-specific  information-rich data from such protocols are then fit using global parameter optimization algorithms to tune multiple model parameters simultaneously. By so doing, this approach yields cell models that fit wide-ranging and variable cellular behavior, making them better suited for physiological and pathophysiological predictions.

This talk is part of the Isaac Newton Institute Seminar Series series.

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