The more we learn about cancer, heart disease and Alzheimer's, the more vexingly complex they seem—and the more elusive their cures. Even with cutting-edge imaging technology, biomarker tests and genetic data, we are still far from understanding the multifaceted causes and varied developmental stages of these illnesses.
With the advent of powerful computing, better modeling programs and a flood of raw biomedical data, researchers have been anticipating a leap forward in their abilities to decipher the intricate dynamics involved human disease. Now, these computational capabilities are starting to arrive, according to a new analysis published online this week in Science Translational Medicine. In fact, "the field has exploded," Raymond Winslow, director of the Johns Hopkins Institute for Computational Medicine, and co-author of the review, said in a prepared statement.
Medicine and medical research largely have been focused on small specialties and narrow studies. But the body is a whole system—not isolated organ groups—and it is in constant interaction with the wider environment, including pollutants, toxins and other stressors. The resulting interactions do not only work in a single direction; instead, we have learned that there are feed-forward and feedback loops and crosstalk on cellular, molecular and genetic levels. This nexus is where advances in computational medicine are poised to make a large contribution. "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 will wind up with a much clearer view of what causes disease and how to treat it."
Models comparing gene expression in different patients have already successfully helped to determine different grades of prostate cancer, predict how different patients will respond to breast cancer treatment and find different types of stomach cancer.
Scientists are also taking advantage of more advanced anatomical data to model whole organs and their function—and dysfunction. Using, for example, diffusion tensor magnetic resonance imaging, researchers can collect detailed information about heart anatomy, fiber and structure. This macro structure can be combined with more cellular-based models for "unprecedented structural and biophysical detail, including cardiac electromechanics," the researchers noted in their paper. With this information, scientists are learning more about blood-flow dynamics, arrhythmia and heart attacks. These new models are now starting to be translated back to individual patients, to help find better treatments.
Computational-medicine algorithms from detailed brain maps have already been used to develop an iPad app that is being used clinically to help doctors decide on deep brain stimulation locations and strengths.
These models, however, also need to be checked frequently against real-world data and adjusted accordingly. But researchers who are armed to deal with this once unusual cross-discipline endeavor are growing more common. "There is a whole new community of people being trained in mathematics, computer science and engineering, and they are being cross-trained in biology," Winslow said. "This allows them to bring a whole new perspective to medical diagnosis and treatment."
The myriad applications for computational medicine approaches are only beginning to be explored, the researchers noted. "As we gain confidence in the ability of computational models to predict human biological processes, they will help guide us through the complex landscape of disease, ultimately leading to more effective and reliable methods for disease diagnosis, risk stratification and therapy," the researchers wrote. "We are poised at an exciting moment in medicine."
Video of electromechanical heart model courtesy of N. Trayanova