Estimating kinetic constants in the Michaelis–Menten model from one enzymatic assay using Approximate Bayesian Computation

FEBS Letters

article

Jakub M. Tomczak¹  Ewelina Węglarz-Tomczak² 

[1] Institute of Informatics, Faculty of Science, University of Amsterdam;Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam

关键词: Approximate Bayesian Computation;    Bayesian statistics;    enzymology;    likelihood-free;    Michaelis–Menten kinetics;   

DOI  :  10.1002/1873-3468.13531

来源: John Wiley & Sons Ltd.

【 摘 要 】

The Michaelis–Menten equation is one of the most extensively used models in biochemistry for studying enzyme kinetics. However, this model requires at least a couple (e.g., eight or more) of measurements at different substrate concentrations to determine kinetic parameters. Here, we report the discovery of a novel tool for calculating kinetic constants in the Michaelis–Menten equation from only a single enzymatic assay. As a consequence, our method leads to reduced costs and time, primarily by lowering the amount of enzymes, since their isolation, storage and usage can be challenging when conducting research.

【 授权许可】

Unknown