# Identifying Bayesian optimal experiments for uncertain biochemical pathway models

## TL;DR

This paper introduces a Bayesian method to design experiments that reduce uncertainty in predicting drug effects on biochemical pathways.

## Contribution

The novel contribution is a Bayesian optimal experimental design approach for pharmacodynamic models with uncertain parameters.

## Key findings

- The method uses simulated data to account for uncertainty in hypothetical lab measurements.
- It provides probabilistic predictions of drug performance and identifies optimal experiments.
- The approach enables uncertainty quantification and guided experimental design for novel biological pathways.

## 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.