By Mike Krapfl, News Service
The third round of funding from a presidential initiative will build four research teams that will use big data to benefit human and animal health, improve cities and build new tools for researchers.
The flu virus, as seen by an electron microscope. One of the teams supported by the Presidential Initiative for Interdisciplinary Research will use big data tools for real-time tracking of flu in swine. Submitted image.
President Steven Leath launched the Presidential Initiative for Interdisciplinary Research in 2012. The program provides seed funding to establish research teams from across campus to tackle emerging societal challenges. The goal is to help the teams grow into well-funded, cross-disciplinary research groups.
The last two rounds of the initiative focused on building teams that are developing big-data tools and techniques to tackle major research problems in agriculture, health, communities, access to research and other areas.
"We launched this initiative four years ago with the intent of creating a new culture of collaborative research at Iowa State, a culture of thinking big," Leath said. "These latest projects in big-data science are great examples of that. We'll have teams of researchers from across campus taking on brain disease and swine flu, while others develop cyber infrastructures and sustainable cities. Thinking big like this is how we'll live up to our mission of creating, sharing and applying knowledge to improve our state and world."
The latest project by a BCB Faculty member to win the initiative's support is:
Characterizing, monitoring and rapidly recognizing emerging swine influenza through data-driven science: a three-year, $375,000 project led by Phillip Gauger, associate professor of veterinary diagnostic and production animal medicine. A multidisciplinary team will develop new bioinformatics tools for real-time tracking of flu in swine. The goal is to improve animal health and welfare, protect human health and secure the food supply. Researchers will develop techniques to integrate and analyze large collections of diagnostic and genetic data on the influenza A virus, which is known to move frequently between swine and humans.