Optimal design of dynamic experiments for scalar-on-function linear models with application to a biopharmaceutical study

TL;DR

This paper develops a Bayesian optimal experimental design framework for experiments involving functional profile variables within scalar-on-function linear models, demonstrated through a biopharmaceutical application.

Contribution

It introduces a novel Bayesian design approach for experiments with functional variables, enabling control over model complexity and application to real bioreactor data.

Findings

Effective design of experiments with functional variables achieved

Application demonstrated on bioreactor feeding strategies

Framework allows finite-dimensional representation of profile variables

Abstract

A Bayesian optimal experimental design framework is developed for experiments where settings of one or more variables, referred to as profile variables, can be functions. For this type of experiment, a design consists of combinations of functions for each run of the experiment. Within a scalar-on-function linear model, profile variables are represented through basis expansions. This allows finite-dimensional representation of the profile variables and optimal designs to be found. The approach enables control over the complexity of the profile variables and model. The method is illustrated on a real application involving dynamic feeding strategies in an Ambr250 modular bioreactor system.