Unlocking data sets by calibrating populations of models to data density: A study in atrial electrophysiology.

ABSTRACT: The understanding of complex physical or biological systems nearly always requires a characterization of the variability that underpins these processes. In addition, the data used to calibrate these models may also often exhibit considerable variability. A recent approach to deal with these issues has been to calibrate populations of models (POMs), multiple copies of a single mathematical model but with different parameter values, in response to experimental data. To date, this calibration has been largely limited to selecting models that produce outputs that fall within the ranges of the data set, ignoring any trends that might be present in the data. We present here a novel and general methodology for calibrating POMs to the distributions of a set of measured values in a data set. We dem

SUBMITTER: Lawson BAJ 

PROVIDER: S-EPMC5770172 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature