Analyzing 3-D stochastic dynamics in live cells

Analyzing 3-D stochastic dynamics in live cells

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...

Date

December 7, 2011 - 8:00am

Location

5th floor Centergy Building, Room 5186, 75 Fifth Street NW

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.