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

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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.; Ringborg, Rolf H.; Börner, Tim; Heintz, Søren; Van Hauwermeiren, Daan; Grey, Carl; Krühne, Ulrich; Adlercreutz, Patrick; Nopens, Ingmar.

In: Biotechnology Progress, Vol. 33, No. 5, 01.09.2017, p. 1278-1293.

Research output: Contribution to journalArticle

Harvard

Van Daele, T, Gernaey, KV, Ringborg, RH, Börner, T, Heintz, S, Van Hauwermeiren, D, Grey, C, Krühne, U, Adlercreutz, P & Nopens, I 2017, 'Application of iterative robust model-based optimal experimental design for the calibration of biocatalytic models', Biotechnology Progress, vol. 33, no. 5, pp. 1278-1293. https://doi.org/10.1002/btpr.2515

APA

Van Daele, T., Gernaey, K. V., Ringborg, R. H., Börner, T., Heintz, S., Van Hauwermeiren, D., ... Nopens, I. (2017). Application of iterative robust model-based optimal experimental design for the calibration of biocatalytic models. Biotechnology Progress, 33(5), 1278-1293. https://doi.org/10.1002/btpr.2515

CBE

MLA

Vancouver

Van Daele T, Gernaey KV, Ringborg RH, Börner T, Heintz S, Van Hauwermeiren D et al. Application of iterative robust model-based optimal experimental design for the calibration of biocatalytic models. Biotechnology Progress. 2017 Sep 1;33(5):1278-1293. https://doi.org/10.1002/btpr.2515

Author

Van Daele, Timothy ; Gernaey, Krist V. ; Ringborg, Rolf H. ; Börner, Tim ; Heintz, Søren ; Van Hauwermeiren, Daan ; Grey, Carl ; Krühne, Ulrich ; Adlercreutz, Patrick ; Nopens, Ingmar. / Application of iterative robust model-based optimal experimental design for the calibration of biocatalytic models. In: Biotechnology Progress. 2017 ; Vol. 33, No. 5. pp. 1278-1293.

RIS

TY - JOUR

T1 - Application of iterative robust model-based optimal experimental design for the calibration of biocatalytic models

AU - Van Daele, Timothy

AU - Gernaey, Krist V.

AU - Ringborg, Rolf H.

AU - Börner, Tim

AU - Heintz, Søren

AU - Van Hauwermeiren, Daan

AU - Grey, Carl

AU - Krühne, Ulrich

AU - Adlercreutz, Patrick

AU - Nopens, Ingmar

PY - 2017/9/1

Y1 - 2017/9/1

N2 - 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.

AB - 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.

KW - biocatalysis

KW - curvature

KW - Fisher Information Matrix

KW - robust model-based optimal experimental design

KW - ω-transaminase

U2 - 10.1002/btpr.2515

DO - 10.1002/btpr.2515

M3 - Article

VL - 33

SP - 1278

EP - 1293

JO - Biotechnology Progress

T2 - Biotechnology Progress

JF - Biotechnology Progress

SN - 1520-6033

IS - 5

ER -