Cellular proteomic characterization using Active Shape and Non-Gaussinan stochastic texture models

This paper presents a method for the systematical extraction cellular parameters from imaging proteomic datasets in a way suitable for subsequent biological modeling and simulation. This was achieved by capturing the spatial boundaries of cell structures as well as the distribution of its constituents. The model uses the Active Shape Models to parameterize the shape of cellular structures and the Non-Gaussian Texture Model to parameterize spatial distribution of sub-cellular material. Results show the model can extract then generate faithful representations of cellular shapes and textures for a variety of cell types and protein expressions and hence could offer a natural spatial framework for current research on simulating and predicting sub-cellular processes.