Computer Science Thesis Proposal

Tuesday, April 5, 2016 - 12:00pm

Location:

Traffic 21 Classroom 6501 Gates & Hillman Centers

Speaker:

SIDDHARTHA JAIN, Ph.D. Student http://www.cs.cmu.edu/~sj1

For More Information, Contact:

deb@cs.cmu.edu

Cells need to be able to sustain themselves, divide, and adapt to new stimuli. Proteins are key agents in regulating these processes. In all cases, the cell behavior is regulated by signaling pathways and proteins called transcription factors which regulate what and how much of a protein should be manufactured. Anytime a new stimulus arises, it can activate multiple signaling pathways by interacting with proteins on the cell surface (if it is an external stimulus) or proteins within the cell (if it is a virus for example).  Disruption in signaling pathways can lead to a myriad of diseases including cancer. Knowledge of which signaling pathways play a role in which condition, is thus key to comprehending how cells develop, react to environmental stimulus, and are able to carry out their normal functions. Recently, there has also been considerable excitement over the role epigenetics -- modification of the DNA structure that doesn't involve changing the sequence may play. This has been buoyed by the tremendous amount of epigenetic data that is starting to be generated. Epigenetics has been heavily implicated in transcriptional regulation. How epigenetic changes are regulated and how they affect transcriptional regulation are still open questions however. In this thesis we present a suite of computational techniques and tool and deal with various aspects of the problem of inferring signaling and regulatory networks given gene expression and other data on a condition. In many cases, the amount of biological data available for a condition can be very small compared to the number of variables. We will present an algorithm which uses multi-task learning to learn signaling networks from many related conditions. There are also very few tools that attempt to take temporal dynamics into account when inferring signaling networks. We will present a new algorithm which attempts to do so and significantly improves on the state of the art. Finally, we propose to work on integrating epigenetic data into the inference of signaling and regulatory networks. Thesis Committee: Ziv Bar-Joseph (Chair) Jaime Carbonell Eric Xing Naftali Kaminski (Yale University) Copy of Thesis Summary

Keywords:

Thesis Proposal