A novel parameter estimation from the linearized Michaelis-Menten equation at low substrate concentrations

Parameter estimation using a direct solution of the integrated Michaelis-Menten equation.

A novel method of estimating enzyme kinetic parameters is presented using the Lambert omega function coupled with nonlinear regression. Explicit expressions for the substrate and product concentrations in the integrated Michaelis-Menten equation were obtained using the omega function which simplified kinetic parameter estimation as root-solving and numerical integration of the Michaelis-Menten ...

The 'double' Michaelis-Menten equation: estimation of parameters.

When the concentration dependence of an enzymic reaction or a transport process can be described by the sum of two Michaelis-Menten terms, reliable data that relate reaction rate and substrate concentration can be obtained even when as much as 70 per cent of substrate was consumed during the assay. Each data pair consists of the average reaction rate during an assay and the concentration where ...

Estimation Aspects of the Michaelis – Menten Model

• This paper studies the Michaelis–Menten model (MM), which plays an important role in pharmacokinetics, from a theoretical as well as a computational point of view. An analytical method for the nonlinear least squares estimation of the MM is introduced. It is proved that the MM model has not a unique parameter estimation (through the nonlinear least squares), and there is not a unique optimal ...

Theory on the rate equation of Michaelis-Menten type single- substrate enzyme catalyzed reactions

ESTIMATING THE PARAMETERS OF THE MICHAElIS - MENTEN EQUATION USING SAS

The Michaelis-Menten equation, a rectangular hyperbola with two ~arameters ~o b~ specified, . occurs prominently 1n enzyme k,netlcs." Often, ~n consulting situations we need to (i) obtain estlmates standard errors, and confidence intervals for the unknown parameters, which define this nonlinear model for one set of experimental conditions and (ii) compare the parameter estimates under different...