UQ-bio Summer School

4.3 – Identifying Models for Single-Cell Gene Regulation (Dr. Brian Munsky)

Lecture 4.3

  • Title: Invited Lecture — Designing Optimal Microscopy Experiments to Harvest Single-Cell Fluctuation

Information while Rejecting Imaging Distortion Effects

Lecturer: Prof. Brian Munsky

Lecturer Website: https://www.engr.colostate.edu/~munsky/

Lecturer Email: brian.munsky@colostate.edu

Learning Objectives:

      • Learn how to integrate single-cell data with discrete stochastic models
      • Understand the importance of uncertainty quantification and model selection
      • Learn about new opportunities for model-driven experiment design

Dr. Brian Munsky joined the Department of Chemical and Biological Engineering and the School of Biomedical 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, and his research is actively funded by the W M Keck Foundation, the NIGMS (MIRA), and the NSF (CAREER). Dr. Munsky is very excited about the future of quantitative biology, and he would love to talk about this with you!

Title: Designing Optimal Microscopy Experiments to Harvest Single-Cell Fluctuation

Abstract: abc abc abc

Suggested Reading or Key Publications:

Links to Relevant Software: 

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Referenced Material that are briefly highlighted during the talk:

  • L. ForeroW. RaymondT. HandaM. SaxtonT. MorisakiH. KimuraE. Bertrand, B. Munsky*T. Stasevich*, “Live-cell imaging reveals the spatiotemporal organization of endogenous RNA polymerase II phosphorylation at a single gene”, Nature Communications, 2021, https://doi.org/10.1101/2020.04.03.024414
  • L. AguileraW. RaymondZ. R. FoxM. P. MayE. DjokicT. Morisaki, T. J. StasevichB. Munsky, Computational design and interpretation of live-cell, single-RNA translation experiments, PLoS Computational Biology15:10, e1007425, 2019, https://doi.org/10.1371/journal.pcbi.1007425
  • A L KochL AguileraT MorisakiB E Munsky*, T J Stasevich*, “Quantifying the spatiotemporal dynamics of IRES versus Cap translation with single-molecule resolution in living cells,” 
  • K.R. Lyon Jr.L.U. AguileraT. MorisakiB. Munsky*T.J. Stasevich*, Live-cell single RNA imaging reveals bursts of translational frameshifting, Molecular Cell178:2, 2019, https://doi.org/10.1016/j.molcel.2019.05.002
  • Z. Fox, B. Munsky, The finite state projection based Fisher information matrix approach to estimate and maximize the information in single-cell experiments, PLoS Computational Biology15:1, e1006365, 2019, online here
  • G Neuert, B Munsky, R-Z Tan, L Teytelman, M Khammash, A van Oudenaarden, Systematic Identification of Signal-Activated Stochastic Gene Regulation, Science339:6119, 584-587 (2013). reprint (.pdf)
  • Munsky, B., Khammash, M., The Finite State Projection Algorithm for the Solution of the Chemical Master Equation, Journal of Chemical Physics124 :044104 (2006). reprint (.pdf)
  • Munsky, B., Neuert, G., van Oudenaarden, A., Using Gene Expression Noise to Understand Gene Regulation, Science, 336:6078, 183-187, (2012). reprint (.pdf)