Dr. Smita Krishnaswamy’s (CSE PhD 2008) first-author research paper was recently published in Science Magazine entitled, “Conditional density-based analysis of T cell signaling in single-cell data”.
The paper focuses on computational methods to analyze single cell data in order to obtain a better understanding of how cells process signals. She states, “Cellular circuits sense the environment, process signals, and compute decisions using networks of interacting proteins. To model such a system, the abundance of each activated protein species can be described as a stochastic function of the abundance of other proteins. High-dimensional single-cell technologies, like mass cytometry, offer an opportunity to characterize signaling circuit-wide. However, the challenge of developing and applying computational approaches to interpret such complex data remains.”
The researchers developed computational methods to characterize signaling network relationships by quantifying the strengths of network edges and deriving signaling response functions. Next, they compared signaling between naïve and antigen-exposed CD4+ T-lymphocytes and found naïve cells transmitted more information along a key-signaling cascade. They then validated this on mice lacking the extracellular-regulated MAP kinase (ERK2), which showed stronger influence of pERK on pS6 (phosphorylated-ribosomal protein S6), in naïve cells compared to antigen-exposed cells.
Krishnaswamy graduated with a BS in computer engineering in 2002 and received her PhD in 2008 in computer science and engineering at the University of Michigan. She is currently a postdoctoral research scientist at Columbia University where she is working on computational systems biology.
Posted: November 6, 2014