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Estimating kinetic constants in the Michaelis–Menten model from one enzymatic assay using Approximate Bayesian Computation
Author(s) -
Tomczak Jakub M.,
WęglarzTomczak Ewelina
Publication year - 2019
Publication title -
febs letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.593
H-Index - 257
eISSN - 1873-3468
pISSN - 0014-5793
DOI - 10.1002/1873-3468.13531
Subject(s) - michaelis–menten kinetics , computation , bayesian probability , approximate bayesian computation , mathematics , enzyme kinetics , kinetic energy , chemistry , enzyme , biological system , statistics , computer science , biochemistry , algorithm , physics , biology , enzyme assay , artificial intelligence , quantum mechanics , inference , active site
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.