Please use this identifier to cite or link to this item: http://dx.doi.org/10.18419/opus-10821
Authors: Eisenkolb, Ina
Jensch, Antje
Eisenkolb, Kerstin
Kramer, Andrei
Buchholz, Patrick C. F.
Pleiss, Jürgen
Spiess, Antje
Radde, Nicole
Title: Modeling of biocatalytic reactions: a workflow for model calibration, selection, and validation using Bayesian statistics
Issue Date: 2019
metadata.ubs.publikation.typ: Zeitschriftenartikel
metadata.ubs.publikation.seiten: 13, 7
metadata.ubs.publikation.source: AIChE journal 66 (2020), e16866
URI: http://elib.uni-stuttgart.de/handle/11682/10838
http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-108383
http://dx.doi.org/10.18419/opus-10821
ISSN: 1547-5905
Abstract: We present a workflow for kinetic modeling of biocatalytic reactions which combines methods from Bayesian learning and uncertainty quantification for model calibration, model selection, evaluation, and model reduction in a consistent statistical frame-work. Our workflow is particularly tailored to sparse data settings in which a considerable variability of the parameters remains after the models have been adapted to available data, a ubiquitous problem in many real-world applications. Our workflow is exemplified on an enzyme-catalyzed two-substrate reaction mechanism describing the symmetric carboligation of 3,5-dimethoxy-benzaldehyde to (R)-3,3',5,5'-tetramethoxybenzoin catalyzed by benzaldehyde lyase from Pseudomonas fluorescens. Results indicate a substrate-dependent inactivation of enzyme, which is in accordance with other recent studies.
Appears in Collections:07 Fakultät Konstruktions-, Produktions- und Fahrzeugtechnik

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