Search

Since its original publication, the DynaFit software package [Kuzmic, P. (1996). Program DYNAFIT for the analysis of enzyme kinetic data: Application to HIV proteinase. Anal. Biochem . 237, 260–273] has been used in more than 500 published studies. Most applications have been in biochemistry, especially in enzyme kinetics. This paper describes a number of recently added features and capabilities, in the hope that the tool will continue to be useful to the enzymological community. Fully functional DynaFit continues to be freely available to all academic researchers from http://www.biokin.com .

Publisher Summary This chapter describes the practical robust fit of enzyme inhibition data. The analysis of enzyme inhibition data in the context of preclinical drug screening presents unique challenges to the data analyst. Outliers are data points that are affected by gross errors caused by malfunctioning volumetric equipment, by a human error in data entry, or by countless other possible mishaps. It is shown that Huber's Minimax approach to robust statistical estimation is particularly preferable over the conventional least-squares analysis. The ordinary least square (OLS) estimate of the model parameters is sensitive to the presence of outliers, which has led to the design of various alternatives. All good data points are assigned the same weight in the iteratively reweighted series of LS estimations, exactly as they are in OLS. The practical success of Huber's method applied even to relatively small data sets, such as those arising in preclinical screening, is due to the fact that the method behaves as OLS does if the data are good, but at the same time it gives the least absolute deviation treatment to suspected outliers, while maintaining 95% asymptotic efficiency. It is found that both standardized residuals and leverages play a role in the Huber's method of robust regression analysis, implemented as iteratively re-weighted least squares.