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October 5, 2014 Seeking investigators for Collaborative or Service projects through the National Center for Multiscale Modeling of Biological Systems

The image analysis and modeling team at the NIH-supported National Center for Multiscale Modeling of Biological Systems would like to bring to your attention an opportunity to engage in a collaborative or service project with researchers at the Center. We are seeking investigators who wish to use CellOrganizer for learning and using generative models of cell size, shape and subcellular organization (or to help with further development). We can provide extensive training to external personnel, consultation on appropriate methods and design of studies, help with local installation of any desired software, and access to computational resources at the Center for image analysis, modeling and simulation. CellOrganizer learns modular models of things such as cell shape, nuclear shape, vesicular organelle distribution and microtubule distribution directly from 2D or 3D images and can produce specific instances of cell geometries without the need to create them by hand or to segment microscope images (see Buck et al, 2012 for an overview). Through Center funding, pipelines have been created whereby these geometries can be combined with biochemical models to perform spatially realistic cell simulations with a minimum of effort (Center resources can be provided to run these using the cell simulation engine MCell. The biochemical models can be encoded in SBML (i.e., investigator created or downloaded from models databases) or can be generated by BioNetGen (a powerful rule-based modeling package). This combination of CellOrganizer and MCell allows investigators to explore the effect of different cell geometries on their models (e.g., to independently explore different modes of variation in the generative models, such as variation in organelle number vs. shape). Existing generative models of 3T3 cells, HeLa cells, and C2C12 cells can be used so that making extensive image collections can be avoided.

If interested, please contact murphy@cmu.edu or fill out the form at the MMBioS web site. We would be happy to further explain the capabilities of the current system and discuss development of new capabilities.

April 29, 2014 New Release! Version 2.1

New Release Highlights!

  1. Cell image synthesis
    • Improved vesicular organelles model
      • Eliminate object/object and object/boundary overlap during generation
    • Ability to combine models learned from images of different resolution, and synthesize images at desired resolution
    • Ability to synthesis random walks in shape space from diffeomorphic models of cell and nuclear shape
      • Including directed random walks using Willmore energy and shape space density
  2. Model training
    • New capabilities for cell and nuclear shape model learning
      • Build nuclear models from images without nuclear marker
      • Build joint diffeomorphic models of cell and nuclear shape
    • Per-cell representations for easy model building and comparison
  3. Other
    • Export to SBML-spatial and mesh formats for interfacing with tools such as CellBlender and VCell.
    • Parallelization of model learning pipeline
    • Reporter tools
      • assess learned models
      • compare models
      • compare per cell parameters within or across models

The CellOrganizer project provides tools for

  • learning generative models of cell organization directly from images
  • storing and retrieving those models in XML files
  • 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.

CellOrganizer can learn models of

  • cell shape
  • nuclear shape
  • chromatin texture
  • vesicular organelle size, shape and position
  • microtubule distribution.
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.

Cell types for which generative models for at least some organelles have been built include human HeLa cells, mouse NIH 3T3 cells, and Arabidopsis protoplasts. Planned projects include mouse T lymphocytes and rat PC12 cells.


Synthesized Cell Images
(click to view)

2D HeLa (endosomes)


3D HeLa (mitochondria)


3D protoplast (chloroplasts)

3D HeLa (microtubules)


3D HeLa movie

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