Identifying Bayesian Optimal Experiments for Uncertain Biochemical Pathway Models

TL;DR

This paper introduces a Bayesian optimal experimental design method to enhance the predictive accuracy of pharmacodynamic models of biochemical pathways, especially under uncertainty and limited data, by identifying experiments that most effectively reduce prediction uncertainty.

Contribution

It presents a novel Bayesian approach for designing optimal experiments to improve model predictions in uncertain biochemical pathway models.

Findings

Method effectively quantifies uncertainty in drug performance predictions.

Simulated experiments demonstrate improved model calibration.

Provides a framework for guided experimental planning in biology.

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 and a quantitative measure of which prospective laboratory experiment will optimally reduce prediction uncertainty in the PD model. The methods proposed here provide a way forward for uncertainty quantification and guided experimental design for models of novel biological pathways.