Modeling of biocatalytic reactions: A workflow for model calibration, selection, and validation using Bayesian statistics

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 0 ,5,5 0tetramethoxybenzoin catalyzed by benzaldehyde lyase from Pseudomonas fluorescens.Results indicate a substrate-dependent inactivation of enzyme, which is in accordance with other recent studies.

Three‐level hybrid modeling for systematic optimization of biocatalytic synthesis: α‐glucosyl glycerol production by enzymatic trans‐glycosylation from sucrose

“…Secondly, a step‐by‐step systematic approach to model parametrization is demonstrated based on fitting analysis with an evolving set of constraints, done on initial rate and time‐course data. In pointing out the essential role of fitting constraints for parameter identification, we take a stand on a debate about the use of initial rates or time courses (progress curves) for determining the parameters of enzyme kinetic models (Eisenkolb et al, 2020 ; Ohs et al, 2019 ; Rakels et al, 1994 ; Straathof, 2001 ; Sun et al, 2015 ). The question is not one of either/or.…”

A protocol for dynamic model calibration

“…In this work, we consider the calibration of ordinary differential equation (ODE) models. ODE models are widely used to describe biological processes, and their calibration has been discussed in protocols for different classes of processes, including gene regulatory circuits [ 32 ], signalling networks [ 26 ], biocatalytic reactions [ 33 ], wastewater treatment [ 34 , 35 ], food processing [ 36 ], biomolecular systems [ 37 ], and cardiac electrophysiology models [ 38 ]. Yet, these protocols focus on individual aspects of the calibration process (relevant for the subdiscipline) and/or lack illustration examples and codes that can be reused.…”

“…The papers [ 34 ] and [ 35 ] focus on parameter subset selection via sensitivity and correlation analysis and on subsequent model optimization. The works of [ 32 ], [ 36 ] and [ 33 ] consider only low-dimensional models and do not provide in-depth discussion of scalability. The paper [ 26 ] neither covers structural identifiability (SI) analysis nor experimental design and describes a prediction uncertainty approach with limited applicability.…”

“…The paper [ 26 ] neither covers structural identifiability (SI) analysis nor experimental design and describes a prediction uncertainty approach with limited applicability. The works of [ 33 ], [ 37 ], [ 38 ] discuss most aspects of the calibration process, but do not provide a step-by-step illustration with an example model and codes. The work of [ 39 ] is tailored to users of the MATLAB software toolbox Data2Dynamics [ 40 ].…”

A protocol for dynamic model calibration

“…In this work, we consider the calibration of ordinary differential equation (ODE) models. ODE models are widely used to describe biological processes, and their calibration has been discussed in protocols for different classes of processes, including gene regulatory circuits [71], signalling networks [29], biocatalytic reactions [20], wastewater treatment [57,103], food processing [88], biomolecular systems [85], and cardiac electrophysiology models [100]. Yet, these protocols focus on individual aspects of the calibration process (relevant for the sub-discipline) and/or lack illustration examples and codes that can be reused.…”