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.

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生物催化反应的建模：使用贝叶斯统计量进行模型校准，选择和验证的工作流程

我们提出了一种生物催化反应动力学建模的工作流程，该流程结合了贝叶斯学习和不确定性量化的方法，用于在一致的统计框架中进行模型校准，模型选择，评估和模型简化。我们的工作流程专为稀疏数据设置而量身定制，其中，在将模型调整为可用数据之后，参数的可变性仍然很大，这是许多实际应用中普遍存在的问题。我们的工作流程以酶催化的两种底物反应机理为例，描述了3.5-二甲氧基-苯甲醛对称荧光与苯丙酮假单胞菌的苯甲醛裂解酶催化的（R）-3,3'，5,5'-四甲氧基安息香的对称羰基化反应。。结果表明酶的底物依赖性失活，这与其他最近的研究一致。