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Toward the virtual cell: Automated approaches to building models of subcellular organization “learned” from microscopy images

Authors

  • Taráz E. Buck,

    1. Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA
    2. Joint Carnegie Mellon University–University of Pittsburgh Ph.D. Program in Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA
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  • Jieyue Li,

    1. Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA, USA
    2. Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
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  • Gustavo K. Rohde,

    1. Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA
    2. Joint Carnegie Mellon University–University of Pittsburgh Ph.D. Program in Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA
    3. Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA, USA
    4. Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
    5. Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
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  • Robert F. Murphy

    Corresponding author
    1. Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA
    2. Joint Carnegie Mellon University–University of Pittsburgh Ph.D. Program in Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA
    3. Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA, USA
    4. Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
    5. Departments of Biological Sciences and Machine Learning, Carnegie Mellon University, Pittsburgh, PA, USA
    6. Freiburg Institute for Advanced Studies, Albert Ludwig University of Freiburg, Freiburg, Germany
    • Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA
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Abstract

We review state-of-the-art computational methods for constructing, from image data, generative statistical models of cellular and nuclear shapes and the arrangement of subcellular structures and proteins within them. These automated approaches allow consistent analysis of images of cells for the purposes of learning the range of possible phenotypes, discriminating between them, and informing further investigation. Such models can also provide realistic geometry and initial protein locations to simulations in order to better understand cellular and subcellular processes. To determine the structures of cellular components and how proteins and other molecules are distributed among them, the generative modeling approach described here can be coupled with high throughput imaging technology to infer and represent subcellular organization from data with few a priori assumptions. We also discuss potential improvements to these methods and future directions for research.

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