Two Trinity College faculty members from different academic disciplines are teaming up to work on a new research project studying cell signaling, thanks to a three-year grant of $293,514 awarded by the National Science Foundation.
Associate Professor of Chemistry Michelle Kovarik is the principal investigator (PI) for the project, titled “RUI: Microelectrophoretic and Statistical Tools for Studies of Kinase- and ROS-Based Signaling in Dictyostelium discoideum.” The grant began in September 2021 and includes funding to support three undergraduate student researchers from Trinity each summer of the grant’s duration. Kovarik invited Associate Professor of Mathematics Per Sebastian Skardal to collaborate on innovative data analysis for the project.
“Living organisms use a diversity of molecules to communicate information within and between cells, and scientists are still investigating the interactions between these molecules,” Kovarik said. “The grant project looks at the ways in which cells communicate in response to different stresses using enzymes and reactive oxygen species—not in separate measurements, but together, looking at how they might be interacting.” Kovarik’s previous NSF grant studied enzyme signaling, and a past grant she received from a private foundation examined reactive oxygen species. “This proposal takes the two branches of my research until now and looks at how they are interrelated in a single project,” she said.
For one part of the project, Skardal and his students will develop new mathematical tools to identify subpopulations of cells that may respond differently to stresses than the rest of the population. “It would be useful to have mathematical tools to analyze the chemical data we get and draw out some of those relationships that become evident in the mathematics,” Kovarik said of the goals for Skardal’s work. “He has proposed some ideas, and we’ll be testing those methods using data generated by my students in the lab.”
Skardal, who usually studies dynamical systems and nonlinear dynamics, said, “This project relates to my field because I’m interested in systems where there are a lot of different objects that interact in complicated ways and patterns. Being able to address a problem that’s classically statistics from a totally different viewpoint using a networks approach is quite new.”