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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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"Analyzing 3-D Stochastic Dynamics in Live Cells" - Christopher Calderon, Life University/Numerica Corporation
Host: Jennifer Curtis
Advances in microscopy have enabled measurements in living cells, but there is a wealth of biologically relevant dynamical information contained in experimental data that has not been utilized. Existing analysis methods either coarse grain too much or cannot overcome some technical challenges inherent to in vivo measurements. The importance of more fully utilizing information “hidden” in noisy 3D in vivo measurements will be emphasized in several problems. In this talk, I demonstrate how recent advances in time series analysis can be used to estimate stochastic differential equations (SDEs) and construct hypothesis tests checking the consistency of a fitted model with a single experimental trajectory. The inferred SDE parameters change in a statistically significant fashion over the lifetime of a single trajectory, so methods capable of rigorous statistical inference checking all SDE model (and measurement noise) assumptions using only one time series are valuable. Analyzing a single trajectory is important for quantitatively identifying heterogeneity in noisy complex systems. The methods discussed offer new tools for quantitatively probing molecular traffic in the cytoplasm and also enable new discoveries. Although the results presented are centered around the analysis of experimental mRNA in live yeast cells (Saccharomyces Cerevisiae), the work is also relevant to tracking groups of particles in crowded, noisy, complex environments.
