The research, he says, "has truly taken off over the last 10 years or so." Biological knowledge is expanding. Experimental tools are improving. "There's a lot of data to feed quantitative models."

And, of course, "The ever-expanding power of computing is making it possible to simulate very large scale systems."

Winslow has described the intricate interplay of genetic material, proteins, cells and bodily organs as something like hugely complex jigsaw puzzle. Advances in computational models have equipped biologists with powerful ways to make sense of the microscopic mechanisms of disease – and offered new opportunities to test out treatments based on that knowledge.

Some examples of recent research include models that are helping scientists understand how networks of molecules are implicated in cancer – helping them predict which people might be most at risk – and a field called computational physiological medicine, which uses computational modeling to show how biological systems shift from a healthy to an unhealthy state.

Computational anatomy uses imaging technology to look for changes in the shape of certain structures in the brain – changes that could indicate Alzheimer’s disease or schizophrenia.

That is "one area of computational medicine that I think is closest to truly meaningful, large-scale, clinical application," says Winslow.

Already, he says, it's at the point where, by looking at the structure of the hippocampus, researchers could say a patient has Alzheimer's, or is at the early stages, or has indications that portend a significant progression of the disease.

The research – which could similarly be a boon for treating Parkinson's disease, or Tourette's syndrome – is "very close to being delivered and used in the clinic, precisely because it's proving to have such refined diagnostic capabilities," says Winslow.

Despite these near-term advances, however, computational medicine is "definitely not being used to its full potential," he adds.

Still, Winslow is hopeful. "We should all understand that this is a slow process," he adds. "We're at the very beginnings of computational medicine. Constructing these models is difficult. It relies on data that is difficult to get as well."

And the advances are coming faster every day. "As we develop better technologies for measuring what's going on in the body," says Winslow, "with the emerging power of genomics, it's likely that these new kinds of data that we can measure in every patient are going to be really valuable in helping to constrain models for that patient's illness."