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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 orcid ; Krühne, Ulrich ; Adlercreutz, Patrick LU orcid 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.

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author
; ; ; ; ; ; ; ; and
organization
publishing date
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
2025-01-07 23:30:31
@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}},
}