Total least squares fitting Michaelis-Menten enzyme kinetic model function

Authors:
Dragan Jukić;Kristian Sabo;Rudolf Scitovski
Affiliations:
Department of Mathematics, University of Osijek, Gajev trg 6, HR-31 000 Osijek, Croatia;Department of Mathematics, University of Osijek, Gajev trg 6, HR-31 000 Osijek, Croatia;Department of Mathematics, University of Osijek, Gajev trg 6, HR-31 000 Osijek, Croatia
Venue:
Journal of Computational and Applied Mathematics
Year:
2007

Citing 5
Cited 3

A stable and efficient algorithm for nonlinear orthogonal distance regression

SIAM Journal on Scientific and Statistical Computing
Existence of optimal solution for exponential model by least squares

Journal of Computational and Applied Mathematics
Partial linearization of one class of the nonlinear total least squares problem by using the inverse model function

Computing
Least-squares fitting Gompertz curve

Journal of Computational and Applied Mathematics
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)

Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)

Application of gradient descent method to the sedimentary grain-size distribution fitting

Journal of Computational and Applied Mathematics
Total least squares fitting Bass diffusion model

Mathematical and Computer Modelling: An International Journal
On the existence of best Mitscherlich, Verhulst, and West growth curves for generalized least-squares regression

Journal of Computational and Applied Mathematics

Quantified Score

Hi-index	7.29

Visualization

Abstract

The Michaelis-Menten enzyme kinetic model f(x;a,b)=ax/(b+x), a,b0, is widely used in biochemistry, pharmacology, biology and medical research. Given the data (p"i,x"i,y"i), i=1,...,m, m=3, we consider the total least squares (TLS) problem for the Michaelis-Menten model. We show that it is possible that the TLS estimate does not exist. As the main result, we show that the TLS estimate exists if the data satisfy some natural conditions. Some numerical examples are included.