“Lord of the Rings” meets Smarter Healthcare | A Smarter Planet Blog.

in silico modeling creates images of liver
inflammation and cancer that are similar to what might be seen under the
microscope.
At the University of Pittsburgh’s McGowan Institute for
Regenerative Medicine, researchers are using IBM technology to open
up new dimensions in biological modeling. With the help of an IBM Shared
University Research (SUR) Award and an IBM supercomputer, Pitt is using
leading-edge in silico biological research, which uses computer
simulations to explore biological pathways and test therapeutic
interventions, tissue engineering, cell therapies and artificial organs
and biodevices. The results of such research could significantly reduce
the cost of new drug development and shorten the treatment evaluation
process – getting treatments to the market faster and cheaper. These
modeling techniques are similar to those used to generate the fantasy
creatures of other worlds in movies, such as “Lord of the Rings” and the
“Star Trek” series. So instead of creating an imaginary character to
fill out a battle scene, Pitt scientists are applying computational
techniques to simulate, for example, inflamed liver cells morphing into
cancer. That allows them to see not only how tumors develop, but how
drugs or other interventions could affect disease progression.
For example, the Pitt research team has simulated liver tissue to
study how a chronic hepatitis infection can lead to liver cancer, lung
tissues to study viral infection and chronic obstructive pulmonary
disease and skin to study how patients with spinal cord injuries develop
pressure ulcers. This type of advanced modeling can help researchers
better understand basic biological processes and allows them to screen
drugs and determine their impact on the body to uncover the best
interventions for a broad range of diseases.
For more info, read an interview with Dr. Yoram Vodovotz in the Pittsburgh Post-Gazette.
Photo credit: University of Pittsburgh Center for Inflammation
and Regenerative Modeling
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