New nanotechnology for detecting cancer and patient-specific computer modeling for studying blood flow are some of the advances that might result from research funded this year by the Collaborative Advances in Biomedical Computing (CABC) program by the Gulf Coast Center for Computational Cancer Research (GC4R).
The CABC is supported by the John and Ann Doerr Fund for Computational Biomedicine. The seed funding furthers the Vision for the Second Century goal of aggressively fostering collaborative relationships with other institutions to leverage resources.
”Being able to stimulate new research collaboration is critical to our mission,” said Jan Odegard, executive director of Rice’s Computer and Information Technology Institute and acting executive director of GC4R. “The seed fund provided by the Doerrs enables computational researchers at Rice to forge new collaborations with colleagues in the Texas Medical Center focused on solving challenging problems that will improve our ability to treat some of the most challenging diseases.”
Below are the projects that received 2007 funding:
A Computational Approach to Optimization of Nanotechnology-Enabled Optical Molecular Imaging of Cancer. Principal investigators Rebekah Drezek, associate professor of bioengineering, and Kuan Yu, assistant professor of radiation oncology at The University of Texas M.D. Anderson Cancer Center, will work to develop simulation tools for the design of new light-activated molecular imaging agents for noninvasive cancer detection.
They will develop computational strategies to address the gap between nanoscale modeling of individual nanoparticle optical properties and microscale modeling of bulk optical signals measured from tissue. They hope to provide a more customized approach to optimize imaging agent performance given a patient’s tissue properties and the optical instrument’s design.
Patient-Specific Computer Modeling of Arterial Dynamics and Blood Flow. Tayfun Tezduyar, the James F. Barbour Professor of Mechanical Engineering, Sunil Sathe, research scientist in mechanical engineering, and Brian Conklin, assistant professor of pediatrics at Baylor College of Medicine, will demonstrate that patient-specific computer modeling of arterial dynamics and blood flow can be a life-saving medical diagnostic and decision-making tool.
They believe that the advanced, parallel computer modeling techniques they have developed, Rice’s parallel computing resources, and the collaboration between Rice and Baylor College of Medicine will make that happen.
The team plans to improve the accuracy, detail and comprehensiveness in representing patient-specific artery segments. They also will design effective ways to process, display and interpret the results obtained from the computer models. Their goal is to find the best method to process and present the data so it is most helpful to doctors trying to make decisions about patient care.
Improving the Accuracy and Speed of Treatment Planning Calculations for Cancer Patients. Pablo Yepes, senior faculty fellow in physics and astronomy, and Wayne Newhauser, assistant professor of radiation physics at UT’s M.D. Anderson Cancer Center, hope to make proton radiation treatment a more viable medical option since it causes less damage to healthy tissue than the more common photon radiation treatment.
They propose to use techniques borrowed from particle physics, computer science, statistics and applied mathematics to optimize proton radiation and to provide a computational tool to maximize its benefits.
Using Signaling Networks and Proteomic Data for Computational Prediction of Therapeutic Targets. Luay Nakhleh, assistant professor of computer science, and Prahlad T. Ram, assistant professor of molecular therapeutics at UT’s M.D. Anderson Cancer Center, will study biological signaling networks, which regulate many activities critical to the health of cells. Their long-term goal is to identify therapeutic targets and explain changes in protein activity levels in response to targeted agents.
They are particularly interested in identifying molecules that may be used as targets for combinations of therapeutic agents and in explaining currently unpredictable protein activity levels in response to drugs, using signaling network topology, as well as genomic and proteomic data.
Funding for three other collaborative projects involving Rice researchers was renewed:
Computational Discovery of Selectivity Filters for the Cancer Pharmacokinome
Learning Models of Signaling Networks in Cancer: A Mixed Computational and Experimental Approach
T4DT: Processing 4D CT Scans of the Lungs