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

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Bibtex

@article{99afc703cc054d1a9a9002441bde7af4,
title = "Application of iterative robust model-based optimal experimental design for the calibration of biocatalytic models",
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 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.",
keywords = "biocatalysis, curvature, Fisher Information Matrix, robust model-based optimal experimental design, ω-transaminase",
author = "{Van Daele}, Timothy and Gernaey, {Krist V.} and Ringborg, {Rolf H.} and Tim B{\"o}rner and S{\o}ren Heintz and {Van Hauwermeiren}, Daan and Carl Grey and Ulrich Kr{\"u}hne and Patrick Adlercreutz and Ingmar Nopens",
year = "2017",
month = "9",
day = "1",
doi = "10.1002/btpr.2515",
language = "English",
volume = "33",
pages = "1278--1293",
journal = "Biotechnology Progress",
issn = "1520-6033",
publisher = "The American Chemical Society",
number = "5",

}