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Ivan Cao-Berg, Aabid Shariff, Jieyue Li, Devin Sullivan, Tao Peng, Armaghan Naik, Gustavo Rohde, Robert F. Murphy (Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA)There exists a need for computational models that accurately represent the number, size, shape, and positions of subcellular structures, the spatial relationships between different structures, and how proteins are distributed between them. The CellOrganizer project provides tools for learning generative models of cell organization directly from images, storing and retrieving those models in XML files and 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. 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. Together, these enable synthesis and exploration of cellular data sourced from wide sources and types into a single, cohesive knowledge platform.