"This is one of the most complicated projects in computational science. But you can do anything with a supercomputer," Oden said. "There's a cascading list of models at different scales that talk to each other. Ultimately, we're going to need to learn to calibrate each and compute their interactions with each other."

FROM COMPUTER TO CLINIC


The research team at UT Austin -- which comprises 30 faculty, students, and postdocs -- doesn't only develop mathematical and computer models. Some researchers work with cell samples in vitro; some do pre-clinical work in mice and rats. And recently, the group has begun a clinical study to predict, after one treatment, how an individual's cancer will progress, and use that prediction to plan the future course of treatment.

At Vanderbilt University, Yankeelov's previous institution, his group was able to predict with 87 percent accuracy whether a breast cancer patient would respond positively to treatment after just one cycle of therapy. They are trying to reproduce those results in a community setting and extend their models by adding new factors that describe how the tumor evolves.

The combination of mathematical modeling and high-performance computing may be the only way to overcome the complexity of cancer, which is not one disease but more than a hundred, each with numerous sub-types.

"There are not enough resources or patients to sort this problem out because there are too many variables. It would take until the end of time," Yankeelov said. "But if you have a model that can recapitulate how tumors grow and respond to therapy, then it becomes a classic engineering optimization problem. 'I have this much drug and this much time. What's the best way to give it to minimize the number of tumor cells for the longest amount of time?'"

Computing at TACC has helped Yankeelov accelerate his research. "We can solve problems in a few minutes that would take us 3 weeks to do using the resources at our old institution," he said. "It's phenomenal."

According to Oden and Yankeelov, there are very few research groups trying to sync clinical and experimental work with computational modeling and state-of-the-art resources like the UT Austin group.

"There's a new horizon here, a more challenging future ahead where you go back to basic science and make concrete predictions about health and well-being from first principles," Oden said.

Said Yankeelov: "The idea of taking each patient as an individual to populate these models to make a specific prediction for them and someday be able to take their model and then try on a computer a whole bunch of therapies on them to optimize their individual therapy -- that's the ultimate goal and I don't know how you can do that without mathematizing the problem."

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