Speaker: Smita Krishnaswamy

Assistant Professor of Genetics and Computer Science at Yale University

“Understanding Cellular Computation and Information Processing Networks through Multidimensional Single-Cell Data”

Abstract: Cells are computational entities that process external signals through networks of interacting proteins and reconfigure their state via biochemical modifications of proteins and changes in gene expression. Despite progress in the understanding of signaling biology, graph diagrams typically used as depictions of signaling relationships only offer qualitative abstractions. New single-cell measurement technologies provide quantitatively precise measurements of dozens of cellular components representing important biochemical functions. However, a major challenge in deciphering single-cell signaling data is developing computational methods that can handle the complexity, noise and bias in the measurements. I will describe algorithms that quantify the flow of information through signaling interactions and mathematically characterize relationships between signaling molecules, using statistical techniques to detect dependencies while mitigating the effect of noise. I will show how these algorithms can be utilized to characterize signaling relationships in immune cells, detect subtle differences between cell types, and predict differential responses to perturbation. Next, I will analyze T cells from non-obese diabetic (NOD) mice and show that previously recognized defects in extracellular-signal-regulated kinase (ERK) signaling can be traced back to a small receptor-proximal defect that is amplified through reconvergence in the network. Then, I will show how multidimensional extensions of these techniques can be used to track dynamic changes in the relatively unknown network driving the epithelial-to-mesenchymal (EMT) transition that occurs during cancer metastasis, with the goal of predicting drugs to halt the process. Finally, I will discuss future directions involving integration of gene expression and other data types in order to gain a more complete picture of cellular computation. 

Bio: Smita Krishnaswamy is currently an assistant professor of Genetics and Computer Science at Yale University. Smita’s research aims to learn predictive computational models of cellular signaling and gene expression from high-throughput single cell data. Prior to joining Yale, Smita was a postdoctoral scientist at Columbia University’s systems biology department. She received her Ph.D. in 2008 in Computer Science and Engineering from the University of Michigan, specializing in algorithms for the design and analysis of logic circuits that are subject to probabilistic errors.

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