Raymond Cheong, Johns Hopkins University, “Information Transduction Capacity and Noise Sources of Biochemical Signaling Networks”

When:
March 11, 2013 @ 12:00 pm – 1:00 pm
2013-03-11T12:00:00-04:00
2013-03-11T13:00:00-04:00

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“Information transduction capacity and noise sources of biochemical signaling networks”

Raymond Cheong is a native of the Baltimore area, having grown up in Columbia, MD. His interest in research was sparked in high school when he studied the tumor suppressive properties of pp32 under Dr. Gary Pasternack at Hopkins, earning honors in the Westinghouse and Intel science competitions. He was a Banneker/Key Scholar at the University of Maryland, College Park where he earned a BS degree in chemical engineering. Raymond then returned to Hopkins, joining the MD/PhD program and completed his PhD in biomedical engineering under Dr. Andre Levchenko. His thesis work focused on understanding information processing by the tumor necrosis factor signaling network through systems biological approaches, and he was recognized as a 2010 Siebel Scholar and received the 2012 Michael A. Shanoff Young Investigator Award.

For more information, visit Raymond Cheong’s website here.

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Abstract

“Information transduction capacity and noise sources of biochemical signaling networks”

In order to make appropriate decisions, a cell must obtain and interpret information about its external environment, and signaling networks are the biochemical systems which perform this essential task. Signaling systems that appear to be highly accurate input-output machines on the basis of population-based measurements often show marked variability when viewed at the single cell level due to biochemical noise, raising questions about how precisely a cell can process and transmit information. In this talk, I will describe three analyses tightly integrating experiment and theory to understand the impact of noise on signaling fidelity. First, we quantified the information transduction capacity of tumor necrosis factor (TNF) signaling to NF-kappaB, finding that the pathway had a capacity of just ~1 bit, which is sufficient for binary decisions but not more nuanced input discrimination. Second, further information theoretic analysis revealed several factors that restrict information flow, such as a receptor-level bottleneck that limits the capacity of the entire TNF signaling network. Third, we developed a generalization of Elowitz’s extrinsic/intrinsic noise decomposition which enables us to identify and measure the sources of noise within a signaling network. Together, these results enable us to gain new insights into the relationship between system structure and information processing accuracy.

Note: Light lunch will be served starting at 11:30am.

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JHU - Institute for Computational Medicine