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Modeling of biocatalytic reactions: A workflow for model calibration, selection, and validation using Bayesian statistics
Author(s) -
Eisenkolb Ina,
Jensch Antje,
Eisenkolb Kerstin,
Kramer Andrei,
Buchholz Patrick C. F.,
Pleiss Jürgen,
Spiess Antje,
Radde Nicole E.
Publication year - 2020
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.16866
Subject(s) - workflow , bayesian probability , selection (genetic algorithm) , calibration , model selection , computer science , substrate (aquarium) , bayes' theorem , bayesian inference , chemistry , enzyme catalysis , data mining , biochemical engineering , biological system , machine learning , artificial intelligence , catalysis , mathematics , engineering , statistics , database , organic chemistry , oceanography , geology , biology
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 framework. 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.