August 21, 2020
Dramatic increases in the scale and availability of data are profoundly reshaping the life sciences. As a result, data acquisition and availability — from DNA sequencers to environmental sensors to parallel global studies — are outpacing the capacity for analysis, including the development of models that represent knowledge of biological processes.
Thanks to a $6 million National Science Foundation (NSF) grant, researchers from the University of Wyoming, the University of Montana and the University of Nevada-Reno will be able to address pressing needs in the life sciences through research and education by this consortium. The title of the project is “Highly Predictive, Explanatory Models to Harness the Life Science Data Revolution.”
“This grant will fund a network of scientists to build and test computational statistical models for the life sciences,” says Alex Buerkle, a UW professor of botany and principal investigator (PI) of the grant. “It will grow the Data Science Center at the University of Wyoming.”
The center, established in September 2018, is designed to help educate and provide tools for analysis for undergraduate students up through faculty, and create unprecedented opportunities for researchers to engage in the cutting edge of data science.
The four-year NSF grant will start Sept. 1 and run through Aug. 31, 2024. The grant builds on previous NSF, private donor and state investments in data science at UW, Buerkle says.
In addition to Buerkle, UW participants and co-PIs are Sarah Collins, an assistant professor of zoology and physiology; Daniel Laughlin, an associate professor of botany; and Lauren Shoemaker and Christopher Weiss-Lehman, both assistant professors of botany.
Other researchers include Joanna Blaszczak, an assistant professor of natural resources and environmental science, and Matt Forister, a professor of biology, both at the University of Nevada-Reno; and Robert Hall, a professor of biological sciences at the University of Montana’s Flathead Lake Biological Station.
“Some types of models can fit observed data very well, but lack generality and the ability to extrapolate to novel settings or future time points,” Buerkle explains. “Conversely, other types of models can be more general but provide a poorer fit to individual data sets. In our research, we will develop knowledge of these trade-offs and methods that combine advantageous features of different types of models.”
The research consortium will further develop computational, statistical and machine learning methods for multidimensional data to develop highly predictive and explanatory models for the life sciences. Additionally, the group will test and refine methods, and develop critical tools for harnessing the data revolution.
Each year, the grant will fund the salaries of 12 postdoctoral researchers who will participate in the consortium, with most of them working at UW.
“In each year, our consortium will train a diverse cohort of 12 postdoctoral researchers in cutting-edge modeling techniques and prepare them for the workforce,” Buerkle says.
The project investigators and postdoctoral researchers at the three institutions will create an integrated, highly collaborative and interdisciplinary consortium of data scientists, he adds.
“We will develop educational tools to aid the dissemination of the methodologies we develop, promoting the efficient use of high dimensional data in the life sciences,” Buerkle says.