Speaker: Brian Munsky
Center for NonLinear Studies, the Los Alamos National Laboratory
Title: "Listening to the Noise: Random Fluctuations Reveal Gene
Network Parameters"
April 5, 11a-12:30p
Santa Fe Complex, Commons
Lunch will be available for purchase $7
Abstract: The cellular environment is abuzz with noise originating
from the inherent random motion of reacting molecules in the living
cell. In this noisy environment, clonal cell populations exhibit cell-
to-cell variability that can manifest significant phenotypic
differences. Noise induced stochastic fluctuations in cellular
constituents can be measured and their statistics quantified using
flow cytometry, fluorescence in situ hybridization, time lapse
fluorescence microscopy and other single cell and single molecule
measurement techniques. We show that these random fluctuations carry
within them valuable information about the underlying genetic network.
Far from being a nuisance, the ever-present cellular noise acts as a
rich source of excitation that, when processed through a gene network,
carries its distinctive fingerprint that encodes a wealth of
information about that network. We demonstrate that in some cases the
analysis of these random fluctuations enables the full identification
of network parameters, including those that may otherwise be difficult
to measure. We use theoretical investigations to establish
experimental guidelines for the identification of gene regulatory
networks, and we apply these guideline to experimentally identify
predictive models for different regulatory mechanisms in bacteria and
yeast.
Biosketch: Brian Munsky is currently a Director Funded Postdoctoral
Fellow studying computational systems biology in the Center for
NonLinear Studies at the Los Alamos National Laboratory. In a past
life, he studied helicopter noise and earned his B.S. and M.S. degrees
in Aerospace Engineering from the Pennsylvania State University in
2000 and 2002, respectively. He then earned his Ph.D. in mechanical
engineering at the University of California at Santa Barbara, where he
studied a very different type of noise that affects gene regulatory
networks. At UCSB, Brian developed the Finite State projection
approach for solving the chemical master equation and applied this
approach to model the dynamics of a stochastic epigenetic switch in E.
coli. His dissertation was awarded the 2007-2008 best Ph.D thesis
award for the UCSB department of Mechanical Engineering. Brian is
currently interested in developing approaches and software to automate
the modeling, identification and analysis of gene regulatory systems.
He and his collaborators utilize various single-cell measurement
techniques such as flow cytometry, time-lapse microscopy, and
fluorescence in situ hybridization.
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