As mathematical systems theory praises, the scale and complexity of the solution should match the scale and complexity of the problem, and biology is no exception. In the recent years it has become apparent that many common disorders such as cancer or cardiovascular and mental diseases are much more complex than initially anticipated since they are often caused by multiple molecular abnormalities, rather than the result of a single defect. Recent outstanding works in the field of cancer genetics have shown that there are many diverse genetic routes that might end up perturbing certain cellular pathways leading to the origination of, for instance, pancreatic cancer. These studies have also highlighted that, despite the great genetic variation observed among each type of cancer in different patients, they clearly share common features at the protein pathway level, which agrees with the view of cancer as a “disease of pathways”. Thus, it seems clear that modulating a single target, even with a very efficient drug, is unlikely to yield the desired outcome, and the growing perception is that we should increase the level of complexity of our proposed therapies by changing the way we think about complex diseases from a gene-centric to a network-centric view.

High-throughput interaction discovery initiatives are providing thousands of novel protein interactions which are unveiling many unexpected links between apparently unrelated biological processes. Indeed, recent exciting studies have exploited the information contained within protein networks to disclose some of the molecular mechanisms underlying complex pathological processes. These findings suggest that both protein-protein interactions and the networks themselves will certainly emerge as a new class of targetable entities, boosting the quest for novel therapeutic strategies.