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CellOrganizer: Learning and Using Cell Geometries for Spatial Cell Simulations

  • Timothy D. Majarian
  • Ivan Cao-Berg
  • Xiongtao Ruan
  • Robert F. MurphyEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1945)

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.

Key words

Generative model Spatial organization Biochemical simulation 

Notes

Acknowledgments

The original research upon which these protocols are based was supported in part by National Institutes of Health grants R01 GM090033 and P41 GM103712.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Timothy D. Majarian
    • 1
    • 2
    • 3
  • Ivan Cao-Berg
    • 1
  • Xiongtao Ruan
    • 1
  • Robert F. Murphy
    • 1
    • 2
    • 4
    • 5
    Email author
  1. 1.Computational Biology DepartmentCarnegie Mellon UniversityPittsburghUSA
  2. 2.Department of Biological SciencesCarnegie Mellon UniversityPittsburghUSA
  3. 3.Broad Institute of MIT and HarvardCambridgeUSA
  4. 4.Department of Biomedical EngineeringCarnegie Mellon UniversityPittsburghUSA
  5. 5.Machine Learning DepartmentCarnegie Mellon UniversityPittsburghUSA

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