estimator was subsequently shown to be biased. The Lea-Coulson method of the median was introduced in 1949. This method is based on the equation Where: r = median number of colonies on one plate containing the indicator (e.g. rifampicin, sodium chlorate, streptomycin) m = a variable which will be varied, corresponds to the mutations/culture The value of the variable m is adjusted until the total value of the equation is close to 0. Then the mutation rate (probablitity of a mutation/cell/division or generation) can be calculated as one of three formulae: (1) (2) (3) where Nt is the median of the number of viable cells on a non-indicator plate (often LB agar with no additive) The choice of which formula to use depends on at which stage in the cell division that the mutations are expected to occur. This method has since been improved on but these more accurate methods are complex. The Ma-Sandri-Sarkar maximum likelihood estimator is currently the best known estimator. A number of additional methods and estimates from experimental data have been described. Two web-applications for the calculation of the mutation rate are freely available: Falcor and bz-rates. Bz-rates implements a generalized version of the Ma-Sandri-Sarkar maximum likelihood estimator that can take into account the relative differential growth rate between mutant and wild-type cells as well as a generating function estimator that can estimate both the mutation rate and the differential growth rate. A worked example is shown in this paper by Jones et al. Distribution In all these models the mutation rate (μ) and growth rate (β) were assumed to be constant. The model can be easily generalized to relax these and other constraints. These rates are likely to differ in non experimental settings. The models also require that Nt μ >> 1 where Nt is the total number of organisms. This assumption is likely to hold in most realistic or experimental settings. Luria and Delbrück estimated the mutation rate from the equation where β is the cellular growth rate, n0 is the initial number of bacteria in each culture, t is the time, and where Ns is the number of cultures without resistant bacteria and N is the total number of cultures. Lea and Coulson's model differed from the original in that they considered a collection of independent Yule processes (a filtered Poisson process). Numerical comparisons of these two models with realistic values of the