Deep learning approach to parameter optimization for physiological models.

ABSTRACT: Inferring nonlinear dynamics and parameters in biological data modeling is challenging. Standard parameter optimization methods are difficult to constrain to biological ranges, especially for nonlinear models. We propose a novel method to evaluate and improve putative models using neural networks to simultaneously address biological modeling, parametrization, and parameter inference. As an example, utilizing data from clinical frequently sampled intravenous glucose tolerance testing, we introduce two physiological lipolysis models of glucose, insulin, and free fatty acids dynamics. Parameter values are obtained via optimization from the limited clinical data. We then generate simulated data from the model by sampling parameters within physiological ranges while ensuring that the joint para

SUBMITTER: Duan X 

PROVIDER: S-EPMC12553359 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature