A transofmration/weighting model for estimating Michaelis-Menten parameters
There has been considerable disagreement about how best to estimate the parameters in Michaelis-Menten models. We point out that many fitting methods are based on different stochastic models, being weighted least squares estimates after appropriate transformation. We propose a flexible model that can be used to help determine the proper transformation and choice of weights. The method is illustrated by examples.