Mathematical models of signalling and gene regulatory systems are abstractions of much more complicated processes. Even as more and larger data sets are becoming available we are not be able to dispense entirely with mechanistic models of real-world processes; nor should we. However, trying to develop informative and realistic models of such systems typically involves suitable statistical inference methods, domain expertise and a modicum of luck. Except for cases where physical principles provide sufficient guidance it will also be generally possible to come up with a large number of potential models that are compatible with a given biological system and any finite amount of data generated from experiments on that system. Here I will discuss how we can systematically evaluate potentially vast sets of mechanistic candidate models in light of experimental and prior knowledge about biological systems. I will illustrate the problems and possible solutions in the context of differential gene regulation in healthy versus patients, embryonic stem cells in different growth media, and a comparative analysis of the regulation of the standard and immuno proteasomes.