Computational models are increasingly helping healthcare researchers to understand how to both identify and treat complex diseases, according to a recent article published by four Johns Hopkins professors in the journal Science Translational Medicine. The professors cite several examples of how computational models have improved research efforts for diseases such as Alzheimer's, as well as how they are being used for preventive care.
"The field has exploded. There is a whole new community of people being trained in mathematics, computer science and engineering, and they are being cross-trained in biology," Raimond Winslow, director of the school's Institute for Computational Medicine and co-author of the article, said in a statement.
"This allows them to bring a whole new perspective to medical diagnosis and treatment."
Winslow compared these new hybrid researchers to engineers, saying that they are "building computational models" for diseases, much like engineers build models of the systems they design.
"Computational medicine can help you see how the pieces of the puzzle fit together to give a more holistic picture," Winslow said.
In a study published in the Annals of Internal Medicine in August, researchers concluded that a computer risk-prediction model developed at the University of Liverpool's Cancer Research Center that assessed a patient's risk for developing lung cancer was more accurate than assessments based on smoking duration or family history.
An algorithm developed by scientists at New York-based Mount Sinai School of Medicine, meanwhile, is assisting in the building of networks from data found in medical records by helping researchers better understand interactions like gene-gene, protein-protein and drug side effects.
In July, researchers from Stanford's bioengineering department completed the world's first computer model of an organism. The model accounts for all molecular interaction in a bacterium, and allows members of the medical community to observe and experiment.