Dr. Brian Munsky, Colorado State University
October 14, 2021
Modern fluorescence labeling and optical microscopy approaches have made it possible to experimentally 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, Dr. Munsky 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 real biological experiments and realistic simulated data for single-gene transcription and single-RNA translation, Dr. Munsky will demonstrate how experiment design approaches can be reformulated to account for non-gaussian noise within individual cells, as well as for non-trivial measurement noise effects due to optical distortions and image processing errors.
To view Dr. Munsky’s webinar, please click on the link below:
Designing Optimal Microscopy Experiments to Harvest Single-Cell Fluctuation Information