Inferring Temporal Signaling Pathways and Regulatory Networks, and Classifying Cell Types from High-Throughput Data

Speaker: Siddhartha Jain, PhD , Carnegie Mellon - School of Computer Science

Date: Wednesday, March 01, 2017

Time: 10:30 AM to 11:30 AM Note: all times are in the Eastern Time Zone

Public: Yes

Location: Seminar Room G575

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Host: David Gifford

Contact: Linda Lynch, 617 715 2459, lindalynch@csail.mit.edu

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Reminder Subject: TALK: Inferring Temporal Signaling Pathways and Regulatory Networks, and Classifying Cell Types from High-Throughput Data

Abstract:
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.

In this talk 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 that 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 that attempts to do so and significantly improves on the state of the art.

Finally, we will present a neural network-based method to cluster single cells, based on their expression, as well as retrieve the closest cell types to a cell given its expression.

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Created by Linda Lynch at Tuesday, February 21, 2017 at 12:47 PM.