Application of Iterative Robust Model-based Optimal Experimental Design for the Calibration of Biocatalytic Models

Timothy Van Daele, Krist V. Gernaey, Rolf Hoffmeyer Ringborg, Tim Börner, Søren Heintz, Daan Van Hauwermeiren, Carl Grey, Ulrich Krühne, Patrick Adlercreutz, Ingmar Nopens

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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 optimise 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 catalysed 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 a more accurate, but also a computationally more expensive method. As a result, an important deviation between both approaches is found, confirming that linearisation methods should be applied with care for nonlinear models. This article is protected by copyright. All rights reserved.
Original languageEnglish
JournalBiotechnology Progress
Volume33
Issue number5
Pages (from-to)1278–1293
ISSN8756-7938
DOIs
Publication statusPublished - 2017

Keywords

  • Biocatalysis
  • Fisher information matrix
  • Curvature
  • Robust model-based optimal experimental design
  • ω-transaminase

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