Drawing a new map

Albert Einstein once said, “You can’t use an old map to explore a new world.”

Michael Brent, the Henry Edwin Sever Professor of Engineering in the McKelvey School of Engineering at Washington University in St. Louis, plans to make a new map and model of the information processing machinery in cells with a five-year, nearly $2 million grant from the National Institutes of Health. The grant, a Maximizing Investigators' Research Award (MIRA), is designed to increase the efficiency of National Institute of General Medical Sciences funding by providing investigators with the flexibility to pursue broad research goals, rather than more narrowly defined projects.

Brent’s research group focuses on the control circuits, or transcriptional regulatory networks, by which cells sense their situation and respond to it by changing how much protein is made from each gene. Each gene contains the instructions for making a specific protein machine that does a specialized job within the cell, such as physical infrastructure, information processing, metabolism or nutrient sensing. The key components of these regulatory networks are transcription factors, proteins that turn genes on and off by binding to nearby DNA. They allow cells to obtain information from their environments, to process that information and to respond to it adjusting the production rate from each gene. Learning more about the circuits underlying these responses  would help to create a new model that ultimately could predict the response to combinations of signals from within or outside the cell.

In the newly funded work, Brent and his lab will use yeast, a single-cell organism that shares many functional components with human cells, to develop a quantitative model that can predict transcriptional responses to signals.

For instance, with yeast cells, Brent and his team can manipulate they types of food they feed the yeast, such as different types of sugars, and see how the cells detect the various types of sugars and respond by making the machinery needed for metabolizing them.

“We want to know which molecules are talking to which other molecules, leading to the cellular response,” he said. “The way cells respond is by turning up production of some genes and turning down others. We want to know which molecules are in charge of controlling the production rate of each gene.”

To develop the model, he plans to improve methods to determine which genes are regulated by each transcription factor. With that information, he plans to produce an updated, highly accurate and detailed map of the transcription factor network that will be a resource to the scientific community 

“Experimental methods that generate this data are continually improving,” Brent said. “As they improve and change, we have to improve and change the computational methods that use that data.”

In addition, he plans to develop methods to understand the activity levels of all transcription factors in any sample of cells by analyzing their transcriptomes – the collection of all messenger RNAs in the cells. Cells change their transcriptional programs by changing the activity of transcription factors. Once the new methods are validated, he plans to design software that will allow other scientists to identify changes in transcription factor activity in any set of yeast transcriptional profiles.  

Finally, he plans to develop methods to identify proteins that regulate the activity of each transcription factor. These methods will help to explain the changes in transcription factor activity they see when nutrients or drugs are provided to cells and to design experiments to test those explanations.

“Ultimately, we hope to use this to engineer cells to have different behaviors that are useful for people,” Brent said. “This work also could have industrial applications.” 

For this work, Brent is collaborating with Robi Mitra, the Alvin Goldfarb Distinguished Professor of Computational Biology in the Department of Genetics at the School of Medicine, as well as Richard Bonneau, professor of biology and computer science, and David Gresham, both at New York University.

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