Patrick Flaherty, professor of mathematics and statistics, was recently awarded a three-year, $582,883 grant from the National Institutes of Health’s (NIH) National Institute of General Medical Sciences to better understand cellular protein homeostasis, the balance between protein creation and destruction. The dysregulation of protein homeostasis is one of the primary paths that allows diseases such as Alzheimer's, Huntington’s or Parkinson’s to develop.
Flaherty is an expert on statistical tools used to analyze large genomic data sets. He is collaborating on this award with Peter Chien, professor of biochemistry and molecular biology, who is an expert on the highly regulated cellular cleanup system in which specialized proteins called proteases degrade damaged or no-longer-needed proteins – a system critical for protein homeostasis. They plan to develop new statistical and computational tools to analyze large-scale genetic experiments to catalog the essential components of this system, which Flaherty and Chien hope will lead to better understanding of pathways important for many human diseases.
As Flaherty explains, recent technological advances in such areas as deep DNA sequencing now allow researchers to piece together the genetic networks in complex cellular systems such as protein homeostasis that are essential to cellular function, and under what conditions – temperature, pH – those genetic networks are essential. Until now, identifying “conditionally essential networks” has been challenging for computational and statistical reasons, they point out.
Flaherty continues, “Understanding what genes in the biological cell are essential is akin to understanding what parts of a car are essential. Wetake out each piece of the car one-by-one and try to drive the car. If it runs just fine, then we say the part is inessential, butif the car won’t run without the part then we say it is essential.”
“What’s new here,” he adds, “is that we’re trying to drive the car in lots of different weather conditions. For example, we might find that the wind shield wipers are in essential on sunnydays, but turn out to be essential when it rains. In this analogy, we expect to be able to collect all of the fuel components into a group called the fuel system and all of the steering components into the steering system.”
Once characterized, conditionally essential networks can be used to identify biomarker combinations for diagnosis and treatment in humans or to identify regulatory networks in model organisms, the researchers say. Further, the tools Flaherty will develop will be generally useful for analyzing deep DNA sequencing data and experiments on other cellular networks and in other organisms.
Chien notes, “This project takes advantage of the amazing breadth of expertise here at UMass, the statistics expertise from Professor Flaherty is exactly the type of tools that are needed to understand the large amount of biological data coming from our systems-level experiments.”