  Fluorescence microscopy has led to impressive quantitative models and new
insights gained from richer sets of biomedical imagery. However, there is a
dearth of rigorous and established bioimaging strategies for modeling
spatiotemporal behavior of diffuse, subcellular components such as mitochondria
or actin. In many cases, these structures are assessed by hand or with other
semi-quantitative measures. We propose to build descriptive and dynamic models
of diffuse subcellular morphologies, using the mitochondrial protein patterns
of cervical epithelial (HeLa) cells. We develop a parametric representation of
the patterns as a mixture of probability masses. This mixture is iteratively
perturbed over time to fit the evolving spatiotemporal behavior of the
subcellular structures. We convert the resulting trajectory into a series of
graph Laplacians to formally define a dynamic network. Finally, we demonstrate
how graph theoretic analyses of the trajectories yield biologically-meaningful
quantifications of the structures.
