Systems biology (un)certainties

Modelers must restore confidence in Systems and Computational Biology by avoiding over-interpretation of mathematical models and providing adequate assessments of uncertainty. Systems Biology, some have claimed (1), is attempting the impossible and is doomed to fail. Possible definitions abound, but Systems Biology is widely understood (including here) to be an approach for studying the behavior of systems of interacting biological components, that relies on combining experiments with computational and mathematical reasoning. Mod-eling complex systems occurs throughout the sciences, so it is perhaps not immediately clear why it should attract greater controversy in molecular and cell biology than elsewhere. We contend that the way in which models are often presented and (over) interpreted in the litera-ture is at least partly to blame. As with experimental results, the key to successfully reporting a mathematical model is an honest appraisal and representation of uncertainty: in the models predictions, parameters, and (where appropriate) in the structure of the model itself. Deriving 1 biological models is rarely straightforward. Although biology is, of course, subject to the sam