Computational medicine, a fast-growing method of using computer models and sophisticated software to figure out how disease develops — and how to thwart it — has begun to leap off the drawing board and land in the hands of doctors who treat patients for heart ailments, cancer and other illnesses. Using digital tools, researchers have begun to use experimental and clinical data to build models that can unravel complex medical mysteries.
These are some of the conclusions of a new review of the field published in the Oct. 31 issue of the journal Science Translational Medicine. The article, “Computational Medicine: Translating Models to Clinical Care,” was written by four Johns Hopkins professors affiliated with the university’s Institute for Computational Medicine…
In recent years, “the field has exploded,” institute director Raimond Winslow said. “There is a whole new community of people being trained in mathematics, computer science and engineering, and they are being cross-trained in biology. This allows them to bring a whole new perspective to medical diagnosis and treatment. Engineers traditionally construct models of the systems they are designing. In our case, we’re building computational models of what we are trying to study, which is disease.”
Looking at disease through the lens of traditional biology is like trying to assemble a very complex jigsaw puzzle with a huge number of pieces, he said. The result can be a very incomplete picture.
“Computational medicine can help you see how the pieces of the puzzle fit together to give a more holistic picture,” Winslow said. “We may never have all of the missing pieces, but we’ll wind up with a much clearer view of what causes disease and how to treat it…”
Computational models, Winslow said, help us to understand these complex interactions, the nature of which is often highly complex and non-intuitive. Models like these allow researchers to understand disease mechanisms, aid in diagnosis, and test the effectiveness of different therapies. By using computer models, he said, potential therapies can be tested “in silico” at high speed. The results can then be used to guide further experiments to gather new data to refine the models until they are highly predictive.
RTFA to get more of an idea where this new field is going.