We demonstrate how Population Markov Chain Monte Carlo techniques may be used to sample from the complex posterior distributions which arise when estimating parameters over nonlinear mechanistic mathematical models of biological processes given noisy data. Further, we show how the samples obtained may be employed, using a Power Posteriors method, to accurately calculate the marginal likelihoods and Bayes factors over such models.