Quantitative real-time PCR (polymerase chain reaction) assays are increasingly used to measure quantities of nucleic acids in samples. They may be used to provide a high-throughput alternative to more traditional biological assays. In this case, a calibration process may be required to convert the PCR measurements onto a more relevant scale. This is most commonly undertaken using simple linear regression. However such calibration models are usually unrealistic since they ignore the various sources of variation associated with the PCR and conventional assays. Taking account of these various sources is necessary if the errors associated with predictions based on the calibration model are to be well estimated. In this paper, we demonstrate a more complete approach to calibration of quantitative PCR. As an example, we develop a Bayesian calibration model for measuring the quantity of the fungus common bunt (Tilletia caries) on wheat seed, based on our understanding of the properties of the assays. As well as illustrating the steps in developing such a model, we show how the fit of the model might be assessed.