Analysis of algebraic weighted least-squares estimators for enzyme parameters.
An algorithm for the least-squares estimation of enzyme parameters Km and Vmax. is proposed and its performance analysed. The problem is non-linear, but the algorithm is algebraic and does not require initial parameter estimates. On a spreadsheet program such as MINITAB, it may be coded in as few as ten instructions. The algorithm derives an intermediate estimate of Km and Vmax. appropriate to data with a constant coefficient of variation and then applies a single reweighting. Its performance using simulated data with a variety of error structures is compared with that of the classical reciprocal transforms and to both appropriately and inappropriately weighted direct least-squares estimators. Three approaches to estimating the standard errors of the parameter estimates are discussed, and one suitable for spreadsheet implementation is illustrated.
Jones
M E
ME
School of Medicine, Flinders University of South Australia, Adelaide.
D000465:Algorithms; D004798:Enzymes / Q000378:metabolism*; D014644:Genetic Variation; D007700:Kinetics; D008433:Mathematics; D016012:Poisson Distribution; D012044:Regression Analysis
D004798:Enzymes