February 18, 2022, zoom

Modern fluorescence labeling and optical microscopy approaches make it possible to observe every stage of basic gene regulatory processes, even at the level of individual DNA, RNA, and protein molecules, in living cells, and within fluctuating environments. To complement these observations, the mechanisms and parameters of discrete stochastic models can be rigorously inferred to reproduce and quantitatively predict every step of the central dogma of molecular biology. As single-cell experiments and stochastic models become increasingly more complex and more powerful, the number of possibilities for their integrated application increases combinatorically, requiring efficient approaches for optimized experiment design. In this presentation, we will introduce two model-driven experimental design approaches: one based on detailed mechanistic simulations of optical experiments, and the other on a new formulation of Fisher Information for discrete stochastic process models. Using combinations of experimental data and realistic simulations for single-gene transcription and single-RNA translation, we will demonstrate how experiment design strategies can be reformulated to account for non-gaussian noise within individual cells as well as for arbitrary measurement noise effects due to optical distortions and image processing errors

Dr. Munsky 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). He joined the Department of Chemical and Biological Engineering and the School of Biomedical Engineering as an Assistant Professor in January of 2014 and was promoted to Associate professor in 2020. Dr. Munsky is known for his discovery of Finite State Projection algorithm (when he was a student at UCSB), which has enabled the efficient study of probability distribution dynamics for stochastic gene regulatory networks. Dr. Munsky’s research at CSU explores the integration of stochastic models with single-cell experiments to identify predictive models of gene regulatory systems and more complex biological systems, and his research is actively funded by the W M Keck Foundation, the NIGMS (MIRA), and the NSF (CAREER). Dr. Munsky is also enthusiastic about research education in the interdisciplinary fields of Quantitative Biology, and he is the contact organizer of the 2022 UQ-Bio Summer School