Estimation of Michaelis-Meneten Constant and Maximum Rate of Reaction: A Nonlinear Approach

Abstract

Precise estimation of Michaelis-Menten constant (K m and maximum rate of reaction (V max is very significant in studying enzyme inhibition as well as the catalytic efficiency of the immobilized enzyme in electrochemical nanointerface based biosensors. Generally linear regression models such as Lineweaver-Burk plot, Eadie-Hosftee plot, Eadie-Scatchard plot and Hanes-Woolf plot have been used to estimate enzyme kinetic parameters. But, the disadvantage of using linear regression plots is changing error statistics. As an alternative to linear regression models, three nonlinear regression models namely Gauss Newton plot, Chi Square plot and Levenberg-Marquardt fit (LVM) have been used, which fits the data by successive iterations. In this work, a sample data of lactate detecting electrochemical biosensor (Au/Nano-ZnO/LDH) has been considered for the precise estimation of K m and V max by employing nonlinear mathematical tools. Finally regression, residual, standard deviation, coefficient of variation, Chi Square, residual sum of squares and percent average relative error analyses are carried out on the adapted nonlinear mathematical models to choose optimized Michaelis-Menten equation.