Anticipators and Procrastinators: Cellular Decision Making in Multivariate Environments

Rice University
Center for Theoretical Biological Physics

PRESENTS
Seminar Speaker

Dr. Hana El-Samad
Associate Professor
University of California, San Francisco
School of Medicine

Tuesday, March 31, 2015
12:30 – 1:30 PM
BRC, 10th Floor, Room 1060 A/B
Feel free to bring your lunch – A light lunch is provided for attendees at 12:15 p.m.

Abstract: Understanding how cellular programs process multiple simultaneous environmental inputs is a century old problem. A classical example is that of the Monod Model, in which microorganisms that are presented with two carbon sources first consume the carbon substrate that supports the highest growth rate and then switch to the secondary carbon source. Sequential sugar utilization is attributed to transcriptional repression of the secondary metabolic pathway, followed by activation of this pathway upon depletion of the preferred carbon source. In this talk, we revisit this problem and present an interesting new twist to this classical dogma.

Bio: Hana El-Samad is a faculty member in the department of Biochemistry and Biophysics at the University of California, San Francisco and the California Institute for Quantitative Biosciences (QB3), where she holds the Grace Boyer Junior Endowed Chair in Biophysics and is the deputy director of the UCSF Systems and Synthetic Biology Center. She is a 2009 Packard Fellow, a recipient of the 2011 Donald P. Eckman Award and the 2012 CSB2 prize in Systems Biology. In 2013, Dr. El-Samad was also named a Paul G. Allen Distinguished Investigator. Dr. El-Samad joined UCSF after obtaining a doctorate degree in Mechanical Engineering from the University of California, Santa Barbara, preceded by a Masters Degree in Electrical Engineering from the Iowa State University. Dr. El-Samad’s research group emphasizes the role of control theory and dynamical systems in the study of biological networks. Her research interests include the investigation of stress responses and biological stochastic phenomena, in addition to the establishment of theoretical computational and technological infrastructures that allow for their quantitative probing in single cells.