Estimation of initial rate from discontinuous progress data

Abstract

When using discontinuous assay of reactions, initial rates are often estimated from a limited number of time points. There has been no detailed study of how best to do this. In this work, time courses were simulated by different theoretical equations (including strong product inhibition, first order, Michaelis–Menten and truly linear), but with random error addition to each data point. Various methods were tested to fit an initial rate to the data, and the result compared with the known “true” value. Fitting a simple quadratic generally gives initial rates as accurate as any other curve, and is better than a linear fit if there are about 8 or more time points. For fewer points a linear fit gives less variable and often more accurate rates. The absolute contribution to data point error has a major impact on rate accuracy, and often dominates that due to curvature, so that sampling to at least 10% conversion is preferred. The accuracy of a linear fit can be improved by methods that reject some later points based on curvature tests. Awareness of these effects can help avoid rate inaccuracies of 10% or more due to poor methods of data analysis.