Robert F. Murphy: "CellOrganizer: Image-derived Models of Subcellular Organization over Time and Space"

CellOrganizer: Image-derived Models of Subcellular Organization over Time and Space

The CellOrganizer project provides tools for learning generative models of cell organization directly from images, storing and retrieving those models in XML files, synthesizing cell images (or other representations) from one or more models. Model learning captures variation among cells in a collection of images. Images used for model learning and instances synthesized from models can be two- or three-dimensional static images or movies. Current components of CellOrganizer can learn models of cell shape, nuclear shape, chromatin texture, vesicular organelle size, shape and position, and microtubule distribution. These models can be conditional upon each other. For example, for a given synthesized cell instance, organelle position is dependent upon the cell and nuclear shape of that instance. Cell types that CellOrganizer has been applied to include human HeLa cells, mouse NIH 3T3 cells, and Arabidopsis protoplasts. Support for various aspects of this project has been provided by grants GM075205 and GM090033 from the National Institute of General Medical Sciences, by a ForschungPreis from the Alexander von Humboldt Foundation, and by the School of Life Sciences of the Freiburg Institute for Advanced Studies. Major collaborators include Professors Gustavo Rohde and Klaus Palme, with contributions from Aabid Sharif, Tao Peng, Ivan Cao-Berg, Taraz Buck, Jieyue Li, Wei Wang, Ting Zhao, Joshua Kangas, Alexander Dovzhenko, and Rüdiger Trojok.