Mathematics at the university differ from engineering mathematics and physics in one fundamental way: the focus is not in how to calculate, but in showing that a solution exists. As a result, it would be rather easy for me to proof that a generic numerical integration method converges, but if I were to actually integrate the problem numerically, I would find it quite hard task. Later I’ve been wondering whether some biologists reading modelling paper might feel the same: would they see a model that, in principle, would be what they need, but the jump to actually start to use it themselves seems hopelessly difficult as the implementation might be too complicated.
This paper partly addresses such problematics:
Kuparinen A, O’Hara RB, Merilä J (2008) Probabilistic reaction norms for continuous ontogenetic transition processes. PLoS ONE 3:e3677
Probabilistic reaction norms were first developed to describe ontogenetic transitions in discrete time intervals, but later then a continuous time analogy of the model was constructed. Unfortunately, this method appeared being very demandig in terms of data required and also very compicated to implement. Our paper addresses this problem, by pointing out that with a few simplifying assumptions essentially similar models can be used with very little data requirements and prior assumptions, and methods for such models are readily implemented to several statistical packages. PLoS ONE seems interesting outlet for this kind of a study, as their philosophy is that the readers of the papers can evaluate, whether they find the study useful and interesting. Go & evaluate!