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Abstract Details

Activity Number: 548
Type: Invited
Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
Sponsor: JASA, Applications and Case Studies
Abstract - #303509
Title: Bayesian Spatio-Dynamic Modeling in Cell Motility Studies: Learning Nonlinear Taxic Fields Guiding Immune Response
Author(s): Ioanna Manolopoulou*+ and Melanie B Matheu and Michael D Cahalan and Thomas B Kepler and Mike West
Companies: Duke University and University of California at Irvine and University of California at Irvine and Duke University Medical Center and Duke University
Address: box 90251, durham, NC, 27708, United States
Keywords: Nonlinear stochastic dynamics ; Radial basis regression ; State-space models ; Single-cell motion ; Chemotaxis
Abstract:

We present dynamic spatial modeling and computational methods for the analysis of collections of objects moving in an inhomogeneous force field. Core motivating examples come from single-cell systems biology, where multiple cells move in 3-D environments driven by underlying chemical force fields. Models are based on underlying discrete-time, dynamic, state-space models for locations and directional velocities of each of a set of objects, combined with a latent force-field over the spatial domain that drives changes in velocities. Locations are measured with noise over time, while velocities are latent. We develop novel models of the force fields using Bayesian radial basis function regression to define a potential surface, with the force field given by the gradient of the potential in 3-D. This allows us to flexibly represent heterogeneity in the spatial structure of the force field and define effective MCMC methods for model fitting. We exemplify the work with analysis of single cell data using a movie from stained immune cells. Interest lies in characterizing the dynamic behavior of cell migration, which is key to understanding the mechanisms of the adaptive immune response.


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