Claire J. Tomlin, UC Berkeley “Data-Driven Identification of Genetic Regulatory Networks in Cancer”

When:
March 4, 2014 @ 12:00 pm – 1:00 pm
2014-03-04T12:00:00-05:00
2014-03-04T13:00:00-05:00

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Bio

“Data-driven identification of genetic regulatory networks in cancer”

Claire Tomlin is a Professor of Electrical Engineering and Computer Sciences at Berkeley, where she holds the Charles A. Desoer Chair in Engineering. She held the positions of Assistant, Associate, and Full Professor at Stanford from 1998-2007, and in 2005 joined Berkeley. She has been an Affiliate at LBL in the Life Sciences Division since January 2012. She received the Erlander Professorship of the Swedish Research Council in 2010, a MacArthur Fellowship in 2006, and the Eckman Award of the American Automatic Control Council in 2003. She works in hybrid systems and control, with applications to biology, robotics, and air traffic systems.

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Abstract

“Data-driven identification of genetic regulatory networks in cancer”

As the understanding of cellular regulatory networks grows, system dynamics and behaviors resulting from feedback effects have proven to be sufficiently complex so as to prevent intuitive understanding. Mathematical modeling in engineering has traditionally sought to extrapolate from existing information and underlying principles to create complex descriptions of various systems, which could be analyzed or simulated, and from which further abstractions could be made. However, in studying biological systems, often only incomplete abstracted hypotheses exist to explain observed complex patterning and functions. The challenge has become to show that enough of a network is understood to explain the behavior of the system. Mathematical modeling must simultaneously characterize the complex and non-intuitive behavior of a network, while revealing deficiencies in the model and suggesting new experimental directions.

In this talk, we describe the process of modeling treated regulatory networks in breast cancer. We present data-driven methods for identifying network structure, the first based on compressive sensing, the second on causal structure learning. We demonstrate the use of the resulting mathematical models in both understanding the system, and in suggesting new treatments. The talk will conclude with experimental results on HER2 positive cell lines. This is joint work with Young Hwan Chang, Soulaiman Itani, Karen Sachs, Jim Korkola, and Joe Gray.

 

JHU - Institute for Computational Medicine