Abstract
In this chapter, we describe protocols for using the CellOrganizer software on the Jupyter Notebook platform to analyze and model cell and organelle shape and spatial arrangement. CellOrganizer is an open-source system for using microscope images to learn statistical models of the structure of cell components and how those components are organized relative to each other. Such models capture the statistical variation in the organization of cellular components by jointly modeling the distributions of their number, shape, and spatial distributions. These models can be created for different cell types or conditions and compared to reflect differences in their spatial organizations. The models are also generative, in that they can be used to synthesize new cell instances reflecting what a model learned and to provide well-structured cell geometries that can be used for biochemical simulations.
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Acknowledgments
The original research upon which these protocols are based was supported by National Institutes of Health grants R01 GM090033 and P41 GM103712.
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© 2024 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature
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Sun, H., Murphy, R.F. (2024). Learning Morphological, Spatial, and Dynamic Models of Cellular Components. In: Wuelfing, C., Murphy, R.F. (eds) Imaging Cell Signaling. Methods in Molecular Biology, vol 2800. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3834-7_16
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DOI: https://doi.org/10.1007/978-1-0716-3834-7_16
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