%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, J�rgen %A Spiess, Antje %A Radde, Nicole E. %D 2019 %J AIChE Journal %K Carlo, Markov Monte analysis, carboligation, chain enzyme enzymes estimation, kinetics, likelihood, parameter profile residual thiamine-diphosphate-dependent unchecked %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, __markedentry = {[pfitz:6]}, 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 = {2020-11-06T17:44:13.000+0100}, author = {Eisenkolb, Ina and Jensch, Antje and Eisenkolb, Kerstin and Kramer, Andrei and Buchholz, Patrick C. F. and Pleiss, J�rgen and Spiess, Antje and Radde, Nicole E.}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/253953f281ff55dddfdaa365c22282835/bib2ist}, doi = {10.1002/aic.16866}, eprint = {https://aiche.onlinelibrary.wiley.com/doi/pdf/10.1002/aic.16866}, interhash = {4aca8773eed70a784642cd9ba45c8c3d}, intrahash = {53953f281ff55dddfdaa365c22282835}, journal = {AIChE Journal}, keywords = {Carlo, Markov Monte analysis, carboligation, chain enzyme enzymes estimation, kinetics, likelihood, parameter profile residual thiamine-diphosphate-dependent unchecked}, number = {n/a}, pages = {e16866}, timestamp = {2020-11-06T16:44:13.000+0100}, 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 }