Colorado State University is launching a new program to train graduate students in variety of scientific fields to sift through and make sense of complex biological data.
CSU recently received a five-year, $2.97 million grant from the National Science Foundation to establish the interdisciplinary research and
education program, which is known as Generating, Analyzing, and Understanding Sensory and Sequencing Information – or GAUSSI.
The program, which gets underway this summer, aims to cross-train students earning advanced degrees in biology, statistics, computer science, biomedical engineering, biomedical sciences, mathematics or cellular and molecular biology to tackle “big data.”
Recent advances in scientific instrumentation, technology and sensors have enabled researchers to generate reams of data cheaper and faster than ever before. But this steady stream of “big data” also comes with its own challenge – how to extract useful, meaningful information out of it.
“It’s much easier these days to generate and collect data. The real question is how do we make sense of it and understand what it means,” said Tom Chen, a professor of electrical and computer engineering and biomedical engineering, and director of GAUSSI. “Everyone is facing the same issue.”
Bridging a gap
To address the issue, GAUSSI is taking a different approach to training graduate students through innovative curricula and integration of research in a multidisciplinary learning environment. Graduate students enrolled in GAUSSI will learn to handle the new computational, statistical, mathematical, and engineering challenges that biologists, computer scientists, and engineers are unable to overcome alone. Interdisciplinary teams will tackle research involving biological sensors, detection of microbes, regulation of gene expression, and evolutionary genomics and genome assembly.
Chen and other CSU faculty involved have opened the program to students earning master’s degrees or PhDs in several different fields related to biosensing and next generation sequencing rather than just limiting it to those in say biology or computer science. Because of that, GAUSSI can be tailored to a student’s background and is designed to help fill his or her knowledge gaps.
For example, a biology student will spend more time taking math and computer science classes to learn about crunching the data. A mathematics student, however, would be required to take more biology courses.
Having a background in biology, computer science and math will better prepare the students to work with complex biological data, said Asa Ben-Hur, a professor of computer science and a GAUSSI co-director.
“Biologists are not trained in computer science and computer scientists are not trained in biology but they need to work together to make sense of these data sets,” he said. “This program will help bridge the gap between biology, computational/mathematical sciences, and engineering. ”
