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arXiv · 2110.09115

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

Damianos Michaelides·Maria Adamou·David C. Woods·Antony M. Overstall
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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.

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Damianos Michaelides, Maria Adamou, David C. Woods, Antony M. Overstall. 2021-10-18. Optimal design of dynamic experiments for scalar-on-function linear models with application to a biopharmaceutical study. https://arxiv.org/abs/2110.09115

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