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 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.
%0 Journal Article
%1 ist:eisenkolb19a
%A Eisenkolb, Ina
%A Jensch, Antje
%A Eisenkolb, Kerstin
%A Kramer, Andrei
%A Buchholz, Patrick C. F.
%A Pleiss, Jürgen
%A Spiess, Antje
%A Radde, Nicole E.
%D 2019
%J AIChE Journal
%K thiamine-diphosphate-dependent kinetics, Markov chain profile enzymes residual from:ist_bib Monte Carlo, parameter estimation, analysis, likelihood, carboligation, enzyme
%N n/a
%P e16866
%R 10.1002/aic.16866
%T Modeling of biocatalytic reactions: A workflow for model calibration, selection, and validation using Bayesian statistics
%U https://aiche.onlinelibrary.wiley.com/doi/abs/10.1002/aic.16866
%V n/a
%X 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 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.
@article{ist:eisenkolb19a,
abstract = {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 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.},
added-at = {2021-05-18T15:32:43.000+0200},
author = {Eisenkolb, Ina and Jensch, Antje and Eisenkolb, Kerstin and Kramer, Andrei and Buchholz, Patrick C. F. and Pleiss, J{\"u}rgen and Spiess, Antje and Radde, Nicole E.},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/29c9caff91e346c4f891c3d194544856d/istbib},
doi = {10.1002/aic.16866},
eprint = {https://aiche.onlinelibrary.wiley.com/doi/pdf/10.1002/aic.16866},
groups = {pfitz:6},
interhash = {4aca8773eed70a784642cd9ba45c8c3d},
intrahash = {9c9caff91e346c4f891c3d194544856d},
journal = {AIChE Journal},
keywords = {thiamine-diphosphate-dependent kinetics, Markov chain profile enzymes residual from:ist_bib Monte Carlo, parameter estimation, analysis, likelihood, carboligation, enzyme},
number = {n/a},
pages = {e16866},
timestamp = {2021-05-18T13:32:43.000+0200},
title = {Modeling of biocatalytic reactions: A workflow for model calibration, selection, and validation using Bayesian statistics},
url = {https://aiche.onlinelibrary.wiley.com/doi/abs/10.1002/aic.16866},
volume = {n/a},
year = 2019
}
