Identifying Bayesian optimal experiments for uncertain biochemical pathway models.

ABSTRACT: Pharmacodynamic (PD) models are mathematical models of cellular reaction networks that include drug mechanisms of action. These models are useful for studying predictive therapeutic outcomes of novel drug therapies in silico. However, PD models are known to possess significant uncertainty with respect to constituent parameter data, leading to uncertainty in the model predictions. Furthermore, experimental data to calibrate these models is often limited or unavailable for novel pathways. In this study, we present a Bayesian optimal experimental design approach for improving PD model prediction accuracy. We then apply our method using simulated experimental data to account for uncertainty in hypothetical laboratory measurements. This leads to a probabilistic prediction of drug performance an

SUBMITTER: Isenberg NM 

PROVIDER: S-EPMC11219779 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature