Quantitative models of signal transduction networks: How detailed should they be?

Receptor-mediated signal transduction networks, comprised of multiple biochemical pathways, control cell responses and are therefore central to normal and aberrant physiological processes. An appreciation for the inherent complexities of these networks has matured in recent years, to the point where it is now apparent that experimental measurements will need to be combined with computational modeling and analysis to best interpret and predict how individual mechanisms (proteinprotein interactions, enzymatic reactions, etc.,) are integrated at the network level. To progress along these lines, there is a major barrier to overcome: although a deep mechanistic understanding of signal transduction has been achieved, data sets of a suitably quantitative nature are still lacking. Based on our efforts to systematically acquire and analyze such measurements, we contend that the level of detail in models of signaling networks ought to be limited by the availability of quantitative data, rather than by the much greater availability of qualitative information about signaling interactions. Although this approach is sensible from a data-driven modeling perspective, it is controversial because it gives the false impression that molecular-level details are being ignored.