Application of iterative robust model-based optimal experimental design for the calibration of biocatalytic models
Van Daele, Timothy ; Gernaey, Krist V. LU ; Ringborg, Rolf H. ; Börner, Tim LU ; Heintz, Søren ; Van Hauwermeiren, Daan ; Grey, Carl LU ; Krühne, Ulrich ; Adlercreutz, Patrick LU
and Nopens, Ingmar
(2017)
In Biotechnology Progress
33(5).
p.1278-1293
- Abstract
The aim of model calibration is to estimate unique parameter values from available experimental data, here applied to a biocatalytic process. The traditional approach of first gathering data followed by performing a model calibration is inefficient, since the information gathered during experimentation is not actively used to optimize the experimental design. By applying an iterative robust model-based optimal experimental design, the limited amount of data collected is used to design additional informative experiments. The algorithm is used here to calibrate the initial reaction rate of an ω-transaminase catalyzed reaction in a more accurate way. The parameter confidence region estimated from the Fisher Information Matrix is compared... (More)
The aim of model calibration is to estimate unique parameter values from available experimental data, here applied to a biocatalytic process. The traditional approach of first gathering data followed by performing a model calibration is inefficient, since the information gathered during experimentation is not actively used to optimize the experimental design. By applying an iterative robust model-based optimal experimental design, the limited amount of data collected is used to design additional informative experiments. The algorithm is used here to calibrate the initial reaction rate of an ω-transaminase catalyzed reaction in a more accurate way. The parameter confidence region estimated from the Fisher Information Matrix is compared with the likelihood confidence region, which is not only more accurate but also a computationally more expensive method. As a result, an important deviation between both approaches is found, confirming that linearization methods should be applied with care for nonlinear models.
(Less)
- author
- Van Daele, Timothy
; Gernaey, Krist V.
LU
; Ringborg, Rolf H.
; Börner, Tim
LU
; Heintz, Søren
; Van Hauwermeiren, Daan
; Grey, Carl
LU
; Krühne, Ulrich
; Adlercreutz, Patrick
LU
and Nopens, Ingmar
- organization
- publishing date
- 2017-09-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- biocatalysis, curvature, Fisher Information Matrix, robust model-based optimal experimental design, ω-transaminase
- in
- Biotechnology Progress
- volume
- 33
- issue
- 5
- pages
- 16 pages
- publisher
- The American Chemical Society (ACS)
- external identifiers
-
- pmid:28675693
- wos:000416712300013
- scopus:85031506720
- ISSN
- 8756-7938
- DOI
- 10.1002/btpr.2515
- language
- English
- LU publication?
- yes
- id
- 99afc703-cc05-4d1a-9a90-02441bde7af4
- date added to LUP
- 2017-10-27 08:01:47
- date last changed
- 2024-03-17 23:33:28
@article{99afc703-cc05-4d1a-9a90-02441bde7af4,
abstract = {{<p>The aim of model calibration is to estimate unique parameter values from available experimental data, here applied to a biocatalytic process. The traditional approach of first gathering data followed by performing a model calibration is inefficient, since the information gathered during experimentation is not actively used to optimize the experimental design. By applying an iterative robust model-based optimal experimental design, the limited amount of data collected is used to design additional informative experiments. The algorithm is used here to calibrate the initial reaction rate of an ω-transaminase catalyzed reaction in a more accurate way. The parameter confidence region estimated from the Fisher Information Matrix is compared with the likelihood confidence region, which is not only more accurate but also a computationally more expensive method. As a result, an important deviation between both approaches is found, confirming that linearization methods should be applied with care for nonlinear models.</p>}},
author = {{Van Daele, Timothy and Gernaey, Krist V. and Ringborg, Rolf H. and Börner, Tim and Heintz, Søren and Van Hauwermeiren, Daan and Grey, Carl and Krühne, Ulrich and Adlercreutz, Patrick and Nopens, Ingmar}},
issn = {{8756-7938}},
keywords = {{biocatalysis; curvature; Fisher Information Matrix; robust model-based optimal experimental design; ω-transaminase}},
language = {{eng}},
month = {{09}},
number = {{5}},
pages = {{1278--1293}},
publisher = {{The American Chemical Society (ACS)}},
series = {{Biotechnology Progress}},
title = {{Application of iterative robust model-based optimal experimental design for the calibration of biocatalytic models}},
url = {{http://dx.doi.org/10.1002/btpr.2515}},
doi = {{10.1002/btpr.2515}},
volume = {{33}},
year = {{2017}},
}