Abstract
Stochastic fluctuations can cause identical cells or individual molecules to exhibit wildly different behaviours. Often labelled “noise,” these fluctuations are frequently considered a nuisance that compromises cellular responses, complicates modelling, makes predictive understanding and control all but impossible. However, if we examine fluctuations more closely and match them to discrete stochastic analyses, we discover virtually untapped, yet powerful sources of information and opportunities. In this talk, I will present our collaborative endeavours to integrate single-cell and single-molecule experiments with precise stochastic analyses to gain new insight and quantitatively predictive understanding for signal-activated gene regulation.
I will explain how we experimentally quantify transcription dynamics at high temporal and spatial resolutions; how we use precise computational analyses to model this data and efficiently infer biological mechanisms and parameters; and how we predict and evaluate the extent to which model constraints (i.e., data) and uncertainty (i.e., model complexity) contribute to our understanding.
I will finish with the discussion of new opportunities in which noise analysis not only helps us to better understand gene regulation phenomena, but where it actually introduces new opportunities to precisely control these phenomena.
Biography
Dr. Brian Munsky joined the Department of Chemical and Biological Engineering as an assistant professor in January of 2014. He received B.S. and M.S. degrees in Aerospace Engineering from the Pennsylvania State University in 2000 and 2002, respectively, and his Ph.D. in Mechanical Engineering from the University of California at Santa Barbara in 2008. Following his graduate studies, Dr. Munsky worked at the Los Alamos National Laboratory — as a Director’s Postdoctoral Fellow (2008-2010), as a Richard P. Feynman Distinguished Postdoctoral Fellow in Theory and Computing (2010-2013), and as a Staff Scientist (2013). Dr. Munsky is best known for his discovery of Finite State Projection algorithm, which has enabled the efficient study of probability distribution dynamics for stochastic gene regulatory networks. Dr. Munsky’s research interests at CSU are in the integration of stochastic models with single-cell experiments to identify predictive models of gene regulatory systems. He was the recipient of the 2008 UCSB Department of Mechanical Engineering best Ph.D. Dissertation award, the 2010 Leon Heller Postdoctoral Publication Prize and the 2012 LANL Postdoc Distinguished Performance Award for his work in this topic. Dr. Munsky is the contact organizer of the internationally recognized, NIH-funded q-bio summer school, where he runs a 3-week graduate level summer course on single-cell gene regulation (q-bio.org).