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CellOrganizer: Learning and Using Cell Geometries for Spatial Cell Simulations.

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This chapter describes the procedures necessary to create generative models of the spatial organization of cells directly from microscope images and use them to automatically provide geometries for spatial simulations… Click to show full abstract

This chapter describes the procedures necessary to create generative models of the spatial organization of cells directly from microscope images and use them to automatically provide geometries for spatial simulations of cell processes and behaviors. Such models capture the statistical variation in the overall cell architecture as well as the number, shape, size, and spatial distribution of organelles and other structures. The different steps described include preparing images, learning models, evaluating model quality, creating sampled cell geometries by various methods, and combining those geometries with biochemical model specifications to enable simulations.

Keywords: cellorganizer learning; geometries spatial; learning using; using cell; cell; cell geometries

Journal Title: Methods in molecular biology
Year Published: 2019

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