Grammar-guided symbolic regression as tool for model development of enzymatic reactions
Citation Link: https://doi.org/10.15480/882.18891
Publikationstyp
Journal Article
Date Issued
2026-09-23
Sprache
English
TORE-DOI
Journal
Volume
21
Article Number
100346
Citation
Digital chemical engineering 21: 100346 (2026)
Publisher DOI
Scopus ID
Publisher
Elsevier
The transition of the chemical industry toward renewable biocatalysis requires robust kinetic models to scale and optimize enzymatic processes. However, deriving these models typically relies on manual, trial-and-error fitting of predefined equations, while data-driven machine learning approaches often produce accurate but physically meaningless “black-box” models. Here, we present an adaptive kinetic model evaluation framework that automates the discovery of enzymatic rate laws directly from batch progress curves. By employing Grammar-Guided Symbolic Regression (GGSR) the algorithm autonomously constructs rate equations using only physically permissible kinetic building blocks. The framework’s reliability was first validated against 13 synthetic kinetic mechanisms, confirming structural and parametric identifiability. The pipeline was then applied to a real experimental case study of glucose-fructose isomerization using Streptomyces murinus isomerase. The algorithm successfully recovered a reversible kinetic equation featuring an apparent uncompetitive-type interaction term providing specific activities and Michaelis constants that align well with literature. This work demonstrates that GGSR can bridge the gap between data-driven automation and mechanistically interpretable biochemical engineering.
Subjects
Grammar-Guided Symbolic Regression
Kinetic modelling
Enzyme reactions
Isomerase
DDC Class
660.284: Chemical Reactors
572: Biochemistry
Publication version
publishedVersion
Loading...
Name
1-s2.0-S2772508126000591-main-1.pdf
Type
Main Article
Size
1.43 MB
Format
Adobe PDF