Eisenkolb, InaJensch, AntjeEisenkolb, KerstinKramer, AndreiBuchholz, Patrick C. F.Pleiss, JürgenSpiess, AntjeRadde, Nicole2020-04-032020-04-0320191547-59051694077489http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-108383http://elib.uni-stuttgart.de/handle/11682/10838http://dx.doi.org/10.18419/opus-10821We 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.eninfo:eu-repo/semantics/openAccess570article