Mathematical Models and Supercomputers Helping to Advance Tailored Cancer Therapies

Mathematical Models and Supercomputers Helping to Advance Tailored Cancer Therapies
Researchers at the University of Texas at Austin are advancing cancer research using supercomputers and big data to model how cancer behaves — a technique that could be used to improve therapies for prostate cancer. Thomas Yankeelov, director of the Center for Computational Oncology at UT Austin, believes cancer research is rich in data but needs laws and models. The solution, he said, is to mine large quantities of data to bring mathematics to cancer and find formulas that explain the multiplicity of behaviors of different cancers, which might be useful in improving personalized therapies for patients. "We're trying to build models that describe how tumors grow and respond to therapy," Yankeelov, who is leading the project, said in a press release. "The models have parameters in them that are agnostic, and we try to make them very specific by populating them with measurements from individual patients." His research is based on studies completed in 2017. Results showed the research team was able to predict how brain tumors would grow and respond to X-ray radiation therapy much more accurately than other models. They achieved these results by including factors like the mechanical forces that act on cells and tumors. "We're at the phase now where we're trying to recapitulate experimental data so we have confidence that our model is capturing the key factors," Yankeelov said. The team developed the mathematically complex models using supercomputers at the Texas Advanced Computi
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