"Disease is too complex to just think your way through it," says Raimond Winslow, director of The Institute for Computational Medicine at Johns Hopkins. "We can no longer work with what I call purely mental models of how biological systems function in either health or disease."
Thankfully, we have technology to lend a hand.
The burgeoning and highly complex field of computational medicine is showing promise for the treatment of illnesses such as Alzheimer's, heart disease, cancer and more, as technology and troves of data are harnessed to investigate the underpinnings and map the progression of diseases.
Technological advancements have precipitated a significant leap forward for the discipline over the past decade or so, but it still has a long way to go before realizing its true potential.
"Computational medicine is a discipline where we try to develop experimentally based computer models of disease, so we can very quantitatively understand what disease is, what affects disease, and then try to model therapeutic interventions," Winslow explains.
He likens it to "the way a flight simulator has a computer model of how the airplane behaves. Intentionally in this case, something goes wrong with the flight simulator and the pilots, aka the physicians, make an intervention, and the simulator responds the way a simulator would respond.
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"The analogy is: we have a model of the plane, a/k/a the disease, and we have the people who are making interventions when something goes wrong – the physicians – and learning how to correct for whatever goes wrong," he says.
The Institute for Computational Medicine was founded in 2005 as a partnership between Johns Hopkins' Whiting School of Engineering and its School of Medicine. But the techniques central to computational medicine have been around for more than 50 years.
"Probably the oldest discipline in which this kind of approach has been used is cardiovascular science," says Winslow. It began in 1960, with work by Oxford University biologist Denis Noble. "He published first electrical model of how the cardiac myocyte generates its electrical activity, which leads to the contraction of the heart. He did it for a single cell. But that was the beginning of modeling cardiac muscle cells and trying to understand how they function in health and in disease."
Nowadays, says Winslow, "computational models of heart disease are being developed, from the molecular level to the cellular level to the whole-heart level."
Those models, of course, are central to the burgeoning field of personalized medicine.
Cardiac care is just one of the fields that stands to gain from computational learning.
"Models have began to appear in other disciplines, over the past 10 years or so," says Winslow. "Modeling has been done on lung disease, cancer, certain types of brain diseases. Increasingly, these models are being tailored to the individual and used to guide selection of therapy to treat the disease."