Both mechanistic and empirical modelling techniques are employed in systems biology. The former construct models whose structure explicitly describes components of the biological system under investigation, while the latter make predictions on the strength of patterns in the data. Although empirical models such as Gaussian process regression (GPR) do not directly help us to elucidate the processes that generated a given data set, they can nevertheless form part of a strategy for testing and investigating hypotheses and mechanistic models. , In our work, we exploit the predictive power of GPR in order to generate plausible simulated data sets from experimentally obtained time-course data. This amounts to a parametric bootstrap (in which the parametric model is a multivariate normal) that implicitly takes into account the time-dependence in the data. Having obtained bootstrap samples, we fit mechanistic models to both the original and simulated data. The variability amongst these fitted models reveals the sensitivity of the fit to uncertainty in the data. We use this approach to investigate the effects of data uncertainty upon parameter estimates in a model of a signalling pathway and upon gene network inference.