Application of iterative robust model‐based optimal experimental design for the calibration of biocatalytic models

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 experimentat...

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Published inBiotechnology progress Vol. 33; no. 5; pp. 1278 - 1293
Main Authors Van Daele, Timothy, Gernaey, Krist V., Ringborg, Rolf H., Börner, Tim, Heintz, Søren, Van Hauwermeiren, Daan, Grey, Carl, Krühne, Ulrich, Adlercreutz, Patrick, Nopens, Ingmar
Format Journal Article
LanguageEnglish
Published United States Wiley Subscription Services, Inc 01.09.2017
Subjects
Algorithms
Biocatalysis
Biocatalysis and Enzyme Technology
Biokatalys och enzymteknik
Biotechnology - standards
Calibration
Computer Simulation
curvature
Design optimization
Engineering and Technology
Experimentation
Fisher information
Fisher Information Matrix
Industrial Biotechnology
Industriell bioteknik
Iterative methods
Kinetics
Models, Biological
Research Design - standards
robust model-based optimal experimental design
Teknik
Transaminases - metabolism
ω-transaminase
Fisher Information Matrix
biocatalysis
robust model-based optimal experimental design
ω-transaminase
curvature
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Summary: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
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ISSN:8756-7938
1520-6033
DOI:10.1002/btpr.2515