Understanding the basis for intracellular motion is critical as the field moves toward a deeper understanding of the relation between Brownian forces, molecular crowding, and anisotropic (or isotropic) energetic forcing. However this task is complicated because effective forces and other parameters used to summarize molecular motion change over time in live cells due to latent state changes. This talk discusses limitations in currently popular analysis methods (e.g., MSD-based analyses) and how new techniques can be used to systematically analyze SPT data experiencing time dependent state changes. New techniques for reliably and more carefully extracting the rich dynamic information content from high resolution (in space and time) optical microscopy measurements has the potential to address many open questions in cell biology and biophysics. Our approach [1] is demonstrated by tracking GFP tagged chromatids in metaphase in live yeast cells and subsequently inferring the effective forces resulting from dynamic interactions that reflect the sum of a number of physical phenomena. State changes are induced by various sources including: microtubule dynamics exerting force through the centromere and DNA-based molecular machines including polymerases and protein exchange complexes. Refined force estimates are obtained by adopting and modifying a nonparametric Bayesian modeling technique, the Hierarchical Dirichlet Process Switching Linear Dynamical System (HDP-SLDS) [2], for SPT applications. The HDP-SLDS method shows promise in systematically identifying dynamical regime changes induced by unobserved state changes when the number of underlying states is unknown in advance (a common problem in SPT applications). We expand on the relevance of the HDP-SLDS approach, show how systematically accounting for measurement noise can assist in SPT analysis, and discuss limitations of the approach. In addition, we demonstrate new computational techniques for tuning hyperparameters and for checking the statistical consistency of model assumptions [3]; the techniques circumvent the need for ``ground-truth'' and/or subjective information. [1] Calderon, C.P. and Bloom, K. PLOS Comp. Bio. (under review). [2] Fox E., &lt;!--StartFragment--&gt;Sudderth E.B., Jordan, &lt;!--StartFragment--&gt;M.I., &lt;!--StartFragment--&gt;and Willsky&lt;!--EndFragment--&gt; A.S. &lt;!--StartFragment--&gt;IEEE Transactions on Signal Processing 59 (4), 2011.&lt;!--EndFragment--&gt; [3] Calderon, C.P. , &lt;!--StartFragment--&gt;Thompson, M.A., &lt;!--StartFragment--&gt;Casolari, J.M., &lt;!--StartFragment--&gt;Paffenroth, R.C., and Moerner, W.E. J Phys Chem B 117 (49), 2013.&lt;!--EndFragment--&gt;

Inferring latent states and force estimates via hierarchical Dirichlet process modeling in single particle tracking experiments

249th National Meeting (2015)

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