Movatterモバイル変換


[0]ホーム

URL:


Skip to main content
NCBI home page
Search in PMCSearch
As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health.
Learn more:PMC Disclaimer | PMC Copyright Notice
Biochemical Journal logo

Analysis of algebraic weighted least-squares estimators for enzyme parameters.

M E Jones1
1School of Medicine, Flinders University of South Australia, Adelaide.
PMCID: PMC1132043  PMID:1463456

Abstract

An algorithm for the least-squares estimation of enzyme parameters Km and Vmax. is proposed and its performance analysed. The problem is non-linear, but the algorithm is algebraic and does not require initial parameter estimates. On a spreadsheet program such as MINITAB, it may be coded in as few as ten instructions. The algorithm derives an intermediate estimate of Km and Vmax. appropriate to data with a constant coefficient of variation and then applies a single reweighting. Its performance using simulated data with a variety of error structures is compared with that of the classical reciprocal transforms and to both appropriately and inappropriately weighted direct least-squares estimators. Three approaches to estimating the standard errors of the parameter estimates are discussed, and one suitable for spreadsheet implementation is illustrated.

Full text

PDF
533

Selected References

These references are in PubMed. This may not be the complete list of references from this article.

  1. Askelöf P., Korsfeldt M., Mannervik B. Error structure of enzyme kinetic experiments. Implications for weighting in regression analysis of experimental data. Eur J Biochem. 1976 Oct 1;69(1):61–67. doi: 10.1111/j.1432-1033.1976.tb10858.x. [DOI] [PubMed] [Google Scholar]
  2. Atkins G. L., Nimmo I. A. Current trends in the estimation of Michaelis-Menten parameters. Anal Biochem. 1980 May 1;104(1):1–9. doi: 10.1016/0003-2697(80)90268-7. [DOI] [PubMed] [Google Scholar]
  3. Bliss C. I., James A. T. Fitting the rectangular hyperbola. Biometrics. 1966 Sep;22(3):573–602. [PubMed] [Google Scholar]
  4. Cornish-Bowden A., Eisenthal R. Statistical considerations in the estimation of enzyme kinetic parameters by the direct linear plot andother methods. Biochem J. 1974 Jun;139(3):721–730. doi: 10.1042/bj1390721. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. DOWD J. E., RIGGS D. S. A COMPARISON OF ESTIMATES OF MICHAELIS-MENTEN KINETIC CONSTANTS FROM VARIOUS LINEAR TRANSFORMATIONS. J Biol Chem. 1965 Feb;240:863–869. [PubMed] [Google Scholar]
  6. Duggleby R. G. Experimental designs for estimating the kinetic parameters for enzyme-catalysed reactions. J Theor Biol. 1979 Dec 21;81(4):671–684. doi: 10.1016/0022-5193(79)90276-5. [DOI] [PubMed] [Google Scholar]
  7. Jones M. E., Taransky K. Least-squares estimation of enzyme parameters. Comput Biol Med. 1991;21(6):459–464. doi: 10.1016/0010-4825(91)90048-e. [DOI] [PubMed] [Google Scholar]
  8. Jones M. E., Taransky K. Least-squares estimation of enzyme parameters. Comput Biol Med. 1991;21(6):459–464. doi: 10.1016/0010-4825(91)90048-e. [DOI] [PubMed] [Google Scholar]
  9. Storer A. C., Darlison M. G., Cornish-Bowden A. The nature of experimental error in enzyme kinetic measurments. Biochem J. 1975 Nov;151(2):361–367. doi: 10.1042/bj1510361. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. WILKINSON G. N. Statistical estimations in enzyme kinetics. Biochem J. 1961 Aug;80:324–332. doi: 10.1042/bj0800324. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. WILKINSON G. N. Statistical estimations in enzyme kinetics. Biochem J. 1961 Aug;80:324–332. doi: 10.1042/bj0800324. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Biochemical Journal are provided here courtesy ofThe Biochemical Society

ACTIONS

RESOURCES


[8]ページ先頭

©2009-2025 Movatter.jp