Optimal design of dynamic experiments for scalar-on-function linear models with application to a biopharmaceutical study
Optimal design of dynamic experiments for scalar-on-function linear models with application to a biopharmaceutical study
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.
Michaelides, Damianos
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Adamou, Maria
233a3fb9-8b76-4d92-8735-2ea6c405bbbc
Woods, David C.
ae21f7e2-29d9-4f55-98a2-639c5e44c79c
Overstall, Antony M.
c1d6c8bd-1c5f-49ee-a845-ec9ec7b20910
9 January 2026
Michaelides, Damianos
0ae90dec-27e4-4c4f-8b03-6c0783828aff
Adamou, Maria
233a3fb9-8b76-4d92-8735-2ea6c405bbbc
Woods, David C.
ae21f7e2-29d9-4f55-98a2-639c5e44c79c
Overstall, Antony M.
c1d6c8bd-1c5f-49ee-a845-ec9ec7b20910
Michaelides, Damianos, Adamou, Maria, Woods, David C. and Overstall, Antony M.
(2026)
Optimal design of dynamic experiments for scalar-on-function linear models with application to a biopharmaceutical study.
Biometrics, 82 (1), [ujaf169].
(doi:10.1093/biomtc/ujaf169).
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.
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DoE_profiles_paper
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ujaf169
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Accepted/In Press date: 28 November 2025
Published date: 9 January 2026
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Local EPrints ID: 508430
URI: http://eprints.soton.ac.uk/id/eprint/508430
ISSN: 1541-0420
PURE UUID: c7107386-7a03-4a35-8a2c-4306f5835888
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Date deposited: 21 Jan 2026 17:41
Last modified: 22 Jan 2026 02:40
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Author:
Maria Adamou
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