Optimal design of dynamic experiments for scalar-on-function linear models with application to a biopharmaceutical study
Methodology
2025-05-23 v3
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.
Keywords
Cite
@article{arxiv.2110.09115,
title = {Optimal design of dynamic experiments for scalar-on-function linear models with application to a biopharmaceutical study},
author = {Damianos Michaelides and Maria Adamou and David C. Woods and Antony M. Overstall},
journal= {arXiv preprint arXiv:2110.09115},
year = {2025}
}