Quantification of protein levels in biological matrices such as serum or plasma frequently relies on the techniques of immunoassay or bioassay. The relevant statistical problem is that of non-linear calibration, where one estimates analyte concentration in an unknown sample from a calibration curve fit to known standard concentrations. This paper discusses a general framework for calibration curve fit to known standard concentrations. This paper discusses a general framework for calibration inference, that of the non-linear mixed effects model. Within this framework, we consider two issues in depth: accurate characterization of intra-assay variation, and the use of empirical Bayes methods in calibration. We show that proper characterization of intra-assay variability requires pooling of information across several assay runs. Simulation work indicates that use of empirical Bayes methods may afford considerable gain in efficiency; one must weigh this gain against practical considerations in the implementation of Bayesian techniques. We illustrate the methods discussed using a cell-based bioassay for the recombinant hormone relaxin.