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

Jakub M. Tomczak, Ewelina Węglarz-Tomczak

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

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Enzyme Assays
Assays
Enzyme kinetics
Biochemistry
Kinetic parameters
Enzymes
Substrates
Costs
Costs and Cost Analysis
Research

Keywords

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

Cite this

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Estimating kinetic constants in the Michaelis–Menten model from one enzymatic assay using Approximate Bayesian Computation. / Tomczak, Jakub M.; Węglarz-Tomczak, Ewelina.

In: FEBS Letters, Vol. 593, No. 19, 01.10.2019, p. 2742-2750.

Research output: Contribution to JournalArticleAcademicpeer-review

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