The use of dummy data points when fitting bacterial growth curves.

We consider the problem of fitting mathematical models for bacterial growth and decline to experimental data. Using models which represent the phases of the growth and decline cycle in a piecewise manner, we describe how least-squares fitting can lead to potentially misleading parameter estimates. We show how these difficulties can be overcome by extending a data set to include hypothetical observations (dummy data points) which reflect biological beliefs, and the resulting stabilization of parameter estimates is analysed mathematically. The techniques are illustrated using real and simulated data sets.