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

Research output: Contribution to journalArticle


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.


  • Timothy Van Daele
  • Krist V. Gernaey
  • Rolf H. Ringborg
  • Tim Börner
  • Søren Heintz
  • Daan Van Hauwermeiren
  • Carl Grey
  • Ulrich Krühne
  • Patrick Adlercreutz
  • Ingmar Nopens
External organisations
  • Ghent University
  • Technical University of Denmark
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Biocatalysis and Enzyme Technology


  • biocatalysis, curvature, Fisher Information Matrix, robust model-based optimal experimental design, ω-transaminase
Original languageEnglish
Pages (from-to)1278-1293
Number of pages16
JournalBiotechnology Progress
Issue number5
Publication statusPublished - 2017 Sep 1
Publication categoryResearch