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Application of iterative robust model‐based optimal experimental design for the calibration of biocatalytic models
Author(s) -
Van Daele Timothy,
Gernaey Krist V.,
Ringborg Rolf H.,
Börner Tim,
Heintz Søren,
Van Hauwermeiren Daan,
Grey Carl,
Krühne Ulrich,
Adlercreutz Patrick,
Nopens Ingmar
Publication year - 2017
Publication title -
biotechnology progress
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.572
H-Index - 129
eISSN - 1520-6033
pISSN - 8756-7938
DOI - 10.1002/btpr.2515
Subject(s) - fisher information , calibration , linearization , computer science , experimental data , design of experiments , optimal design , iterative and incremental development , iterative method , design matrix , mathematical optimization , algorithm , confidence region , matrix (chemical analysis) , process (computing) , nonlinear system , data mining , confidence interval , mathematics , machine learning , statistics , linear model , chemistry , physics , software engineering , quantum mechanics , chromatography , operating system
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. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:1278–1293, 2017