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

Jakub M. Tomczak, Ewelina Węglarz-Tomczak*

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)2742-2750
Number of pages9
JournalFEBS Letters
Volume593
Issue number19
DOIs
Publication statusPublished - 1 Oct 2019
Externally publishedYes

Keywords

  • Approximate Bayesian Computation
  • Bayesian statistics
  • enzymology
  • likelihood-free
  • Michaelis–Menten kinetics

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