Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

On the estimation errors of KM and V from time-course experiments using the Michaelis–Menten equation

Wylie Stroberg, Santiago Schnell
doi: https://doi.org/10.1101/068015
Wylie Stroberg
aDepartment of Molecular & Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Santiago Schnell
aDepartment of Molecular & Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
bDepartment of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
cBrehm Center for Diabetes Research, University of Michigan Medical School, Ann Arbor, MI 48105, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

The conditions under which the Michaelis–Menten equation accurately captures the steady-state kinetics of a simple enzyme-catalyzed reaction is contrasted with the conditions under which the same equation can be used to estimate parameters, KM and V, from progress curve data. Validity of the underlying assumptions leading to the Michaelis–Menten equation are shown to be necessary, but not sufficient to guarantee accurate estimation of KM and V. Detailed error analysis and numerical “experiments” show the required experimental conditions for the independent estimation of both KM and V from progress curves. A timescale, tQ, measuring the portion of the time course over which the progress curve exhibits substantial curvature provides a novel criterion for accurate estimation of KM and V from a progress curve experiment. It is found that, if the initial substrate concentration is of the same order of magnitude as KM, the estimated values of the KM and V will correspond to their true values calculated from the microscopic rate constants of the corresponding mass-action system, only so long as the initial enzyme concentration is less than KM.

Footnotes

  • Email addresses: stroberg{at}umich.edu (Wylie Stroberg), schnells{at}umich.edu (Santiago Schnell)

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
Back to top
PreviousNext
Posted September 06, 2016.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
On the estimation errors of KM and V from time-course experiments using the Michaelis–Menten equation
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
On the estimation errors of KM and V from time-course experiments using the Michaelis–Menten equation
Wylie Stroberg, Santiago Schnell
bioRxiv 068015; doi: https://doi.org/10.1101/068015
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
On the estimation errors of KM and V from time-course experiments using the Michaelis–Menten equation
Wylie Stroberg, Santiago Schnell
bioRxiv 068015; doi: https://doi.org/10.1101/068015

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Biochemistry
Subject Areas
All Articles
  • Animal Behavior and Cognition (3512)
  • Biochemistry (7352)
  • Bioengineering (5329)
  • Bioinformatics (20277)
  • Biophysics (10026)
  • Cancer Biology (7749)
  • Cell Biology (11319)
  • Clinical Trials (138)
  • Developmental Biology (6440)
  • Ecology (9958)
  • Epidemiology (2065)
  • Evolutionary Biology (13336)
  • Genetics (9362)
  • Genomics (12592)
  • Immunology (7714)
  • Microbiology (19046)
  • Molecular Biology (7447)
  • Neuroscience (41063)
  • Paleontology (300)
  • Pathology (1231)
  • Pharmacology and Toxicology (2139)
  • Physiology (3164)
  • Plant Biology (6866)
  • Scientific Communication and Education (1274)
  • Synthetic Biology (1898)
  • Systems Biology (5318)
  • Zoology (1089)