CellOrganizer: Learning and Using Cell Geometries for Spatial Cell Simulations

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


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 



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


  1. 1.
    Resasco DC et al (2012) Virtual cell: computational tools for modeling in cell biology. Wiley Interdiscip Rev Syst Biol Med 4(2):129–140CrossRefGoogle Scholar
  2. 2.
    Robinson M, Andrews SS, Erban R (2015) Multiscale reaction-diffusion simulations with Smoldyn. Bioinformatics 31(14):2406–2408CrossRefGoogle Scholar
  3. 3.
    Kerr RA et al (2008) Fast Monte Carlo simulation methods for biological reaction-diffusion systems in solution and on surfaces. SIAM J Sci Comput 30(6):3126CrossRefGoogle Scholar
  4. 4.
    Mochly-Rosen D (1995) Localization of protein kinases by anchoring proteins: a theme in signal transduction. Science 268(5208):247–251CrossRefGoogle Scholar
  5. 5.
    Huh W-K et al (2003) Global analysis of protein localization in budding yeast. Nature 425(6959):686–691CrossRefGoogle Scholar
  6. 6.
    Hung MC, Link W (2011) Protein localization in disease and therapy. J Cell Sci 124(Pt 20):3381–3392CrossRefGoogle Scholar
  7. 7.
    Zhao T, Murphy RF (2007) Automated learning of generative models for subcellular location: building blocks for systems biology. Cytometry A 71(12):978–990CrossRefGoogle Scholar
  8. 8.
    Johnson GR et al (2015) Joint modeling of cell and nuclear shape variation. Mol Biol Cell 26(22):4046–4056CrossRefGoogle Scholar
  9. 9.
    Peng T, Murphy RF (2011) Image-derived, three-dimensional generative models of cellular organization. Cytometry A 79(5):383–391CrossRefGoogle Scholar
  10. 10.
    Li J et al (2012) Estimating microtubule distributions from 2D immunofluorescence microscopy images reveals differences among human cultured cell lines. PLoS One 7(11):e50292CrossRefGoogle Scholar
  11. 11.
    Shariff A, Murphy RF (2011) Automated estimation of microtubule model parameters from 3-D live cell microscopy images. IEEE 11:1330–1333Google Scholar
  12. 12.
    Shariff A, Murphy RF, Rohde GK (2010) A generative model of microtubule distributions, and indirect estimation of its parameters from fluorescence microscopy images. Cytometry A 77(5):457–466PubMedPubMedCentralGoogle Scholar
  13. 13.
    Afgan E et al (2016) The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update. Nucleic Acids Res 44(W1):W3–W10CrossRefGoogle Scholar
  14. 14.
    Schneider CA, Rasband WS, Eliceiri KW (2012) NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9(7):671–675CrossRefGoogle Scholar
  15. 15.
    Legland D, Arganda-Carreras I, Andrey P (2016) MorphoLibJ: integrated library and plugins for mathematical morphology with ImageJ. Bioinformatics 32(22):3532–3534PubMedGoogle Scholar
  16. 16.
    Faeder JR, Blinov ML, Hlavacek WS (2009) Rule-based modeling of biochemical systems with BioNetGen. In: Maly VI (ed) Systems Biology. Humana Press, Totowa, NJ, pp 113–167CrossRefGoogle Scholar
  17. 17.
    Smith AM et al (2012) RuleBlender: integrated modeling, simulation and visualization for rule-based intracellular biochemistry. BMC Bioinformatics 13(8):S3Google Scholar
  18. 18.
    Waltemath D et al (2016) Toward community standards and software for whole-cell modeling. IEEE Trans Biomed Eng 63(10):2007–2014CrossRefGoogle Scholar

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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