Synthetic biology is predicated upon the paradigm of rationally engineering biological systems to enrich them with new functions. Departing from the path outlined by other engineering disciplines, synthetic biology has made limited use of mathematical models so far. Indeed, their laborious inference leverages on noisy data that often provide partial insights into the system behaviour. If the quality of data generated via a perturbation is a road-block to inference, how should we analyse the informativeness of a stimulus? Which are the factors that contribute to it? Here we combine ideas from System Identification and Bayesian inference to quantify the information content of in vivo experiments for the calibration of a deterministic model of the genetic toggle switch. Beyond establishing a link between Bayes-and Fisher Information-based inference approaches, we find a ∼ 40% gain can be ascribed to the dynamical properties of the perturbation scheme. Our results hint at the importance of including Bayesian experimental design in the characterisation of synthetic circuits.