News Release

NSF funds research predicting gene mutations via computational algorithms

Grant and Award Announcement

Virginia Tech

Biotech Researchers

image: This is T.M. Murali, John Tyson, and Jean Peccoud (left to right). view more 

Credit: Virginia Tech

Think of Facebook as a metaphor for the complex networks of interacting molecules in a living cell.

Genes and proteins regulate the flow of information within a cell similar to people posting, liking, and disliking updates. But, how is it possible to understand and predict the function of these complex networks of genes and gene mutations within a living cell?

Virginia Tech and Colorado State University researchers have been awarded a $1.52 million grant from the National Science Foundation (NSF) to develop new algorithms and mathematical models with the goal of predicting the effects of novel combinations of gene mutations in living cells. They will apply this computational framework to models of cell growth and division in budding yeast.

T.M. Murali, a professor of computer science in the College of Engineering and associate director of the computational tissue engineering interdisciplinary graduate education program, is the lead investigator of this grant. His research group focuses on problems in computational systems biology. Murali is collaborating with John Tyson, University Distinguished Professor of biological sciences in the College of Science at Virginia Tech, and Jean Peccoud, the Abell Chair in Synthetic Biology in the Department of Chemical and Biological Engineering at Colorado State University on this grant.

Up to this point, computational cell biologists have constructed predictive mathematical models of many of the processes in a cell, but the potential of these models to predict the phenotypes of novel combinations of gene mutations has not been fully realized.

Murali and Tyson's research groups will create a unique, integrated framework that will systematically generate informative predictions from mathematical models. They will devise techniques that prioritize these predictions. Subsequently, they will develop new algorithms that will streamline the design of experiments to test thousands of model predictions.

"We will be using computational strategies to understand the complex rules by which proteins control each other and quickly construct, test, and validate a cell growth model with high-throughput experiments. This transformative approach is general purpose and can help to streamline and accelerate the mathematical modeling cycle for any process in the cell," said Murali, who is also a core faculty member in the Virginia Tech-Wake Forest School of Biomedical Engineering and Sciences.

Peccoud's lab will test the predictions of the mathematical models in cost-effective high-throughput genetic cross experiments in yeast. Their results will provide information to Murali and Tyson's team to rapidly reconcile inconsistencies between the model and the experiments.

"My group has been collaborating with Drs. Murali and Tyson for more than 10 years. Initially, we were limited to testing a handful of genetic mutations. With this transformational award from the NSF, we will be able to design large-scale experiments to collect big datasets to validate and refine their models," said Peccoud.

The modeling and high-throughput experiments developed by the researchers will be applicable in the future for drug development in such systems as animal and human cells.

The grant will also infuse computational thinking into biology education at both universities on the undergraduate level and encourage students with backgrounds in life science, engineering, or computation to consider systems biology as a career choice. Each year, the researchers will offer a 10-week summer research institute on Computationally-Driven Experimental Biology to six undergraduate students, which will consist of lectures on project-related topics and a single collaborative research project.


Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.