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Vasan R, Rowan MP, Lee CT, Johnson GR, Rangamani P, Holst M. Applications and Challenges of Machine Learning to Enable Realistic Cellular Simulations. FRONTIERS IN PHYSICS 2020; 7:247. [PMID: 36188416 PMCID: PMC9521042 DOI: 10.3389/fphy.2019.00247] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
In this perspective, we examine three key aspects of an end-to-end pipeline for realistic cellular simulations: reconstruction and segmentation of cellular structures; generation of cellular structures; and mesh generation, simulation, and data analysis. We highlight some of the relevant prior work in these distinct but overlapping areas, with a particular emphasis on current use of machine learning technologies, as well as on future opportunities.
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Affiliation(s)
- Ritvik Vasan
- Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, CA, United States
| | - Meagan P. Rowan
- Department of Bioengineering, University of California San Diego, La Jolla, CA, United States
| | - Christopher T. Lee
- Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, CA, United States
| | | | - Padmini Rangamani
- Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, CA, United States
| | - Michael Holst
- Department of Mathematics, University of California San Diego, La Jolla, CA, United States
- Department of Physics, University of California San Diego, La Jolla, CA, United States
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Murphy RF. Building cell models and simulations from microscope images. Methods 2016; 96:33-39. [PMID: 26484733 PMCID: PMC4766043 DOI: 10.1016/j.ymeth.2015.10.011] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2015] [Revised: 10/15/2015] [Accepted: 10/16/2015] [Indexed: 01/13/2023] Open
Abstract
The use of fluorescence microscopy has undergone a major revolution over the past twenty years, both with the development of dramatic new technologies and with the widespread adoption of image analysis and machine learning methods. Many open source software tools provide the ability to use these methods in a wide range of studies, and many molecular and cellular phenotypes can now be automatically distinguished. This article presents the next major challenge in microscopy automation, the creation of accurate models of cell organization directly from images, and reviews the progress that has been made towards this challenge.
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Affiliation(s)
- Robert F Murphy
- Computational Biology Department, Center for Bioimage Informatics, and Departments of Biological Sciences, Biomedical Engineering and Machine Learning, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, USA; Freiburg Institute for Advanced Studies and Faculty of Biology, Albert Ludwig University of Freiburg, Germany.
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Li J, Shariff A, Wiking M, Lundberg E, Rohde GK, Murphy RF. Estimating microtubule distributions from 2D immunofluorescence microscopy images reveals differences among human cultured cell lines. PLoS One 2012; 7:e50292. [PMID: 23209697 PMCID: PMC3508979 DOI: 10.1371/journal.pone.0050292] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2012] [Accepted: 10/22/2012] [Indexed: 11/19/2022] Open
Abstract
Microtubules are filamentous structures that are involved in several important cellular processes, including cell division, cellular structure and mechanics, and intracellular transportation. Little is known about potential differences in microtubule distributions within and across cell lines. Here we describe a method to estimate information pertaining to 3D microtubule distributions from 2D fluorescence images. Our method allows for quantitative comparisons of microtubule distribution parameters (number of microtubules, mean length) between different cell lines. Among eleven cell lines compared, some showed differences that could be accounted for by differences in the total amount of tubulin per cell while others showed statistically significant differences in the balance between number and length of microtubules. We also observed that some cell lines that visually appear different in their microtubule distributions are quite similar when the model parameters are considered. The method is expected to be generally useful for comparing microtubule distributions between cell lines and for a given cell line after various perturbations. The results are also expected to enable analysis of the differences in gene expression underlying the observed differences in microtubule distributions among cell types.
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Affiliation(s)
- Jieyue Li
- Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Aabid Shariff
- Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Mikaela Wiking
- Science for Life Laboratory, Department of Biotechnology, Royal Institute of Technology, Solna, Sweden
| | - Emma Lundberg
- Science for Life Laboratory, Department of Biotechnology, Royal Institute of Technology, Solna, Sweden
| | - Gustavo K. Rohde
- Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- * E-mail: (GKR); (RM)
| | - Robert F. Murphy
- Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Department of Biological Sciences and Department of Machine Learning, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Faculty of Biology and Freiburg Institute for Advanced Studies, Albert Ludwig University of Freiburg, Freiburg, Germany
- * E-mail: (GKR); (RM)
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Buck TE, Li J, Rohde GK, Murphy RF. Toward the virtual cell: automated approaches to building models of subcellular organization "learned" from microscopy images. Bioessays 2012; 34:791-9. [PMID: 22777818 DOI: 10.1002/bies.201200032] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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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Affiliation(s)
- Taráz E Buck
- Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA
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Murphy RF. CellOrganizer: Image-derived models of subcellular organization and protein distribution. Methods Cell Biol 2012; 110:179-93. [PMID: 22482949 DOI: 10.1016/b978-0-12-388403-9.00007-2] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
This chapter describes approaches for learning models of subcellular organization from images. The primary utility of these models is expected to be from incorporation into complex simulations of cell behaviors. Most current cell simulations do not consider spatial organization of proteins at all, or treat each organelle type as a single, idealized compartment. The ability to build generative models for all proteins in a proteome and use them for spatially accurate simulations is expected to improve the accuracy of models of cell behaviors. A second use, of potentially equal importance, is expected to be in testing and comparing software for analyzing cell images. The complexity and sophistication of algorithms used in cell-image-based screens and assays (variously referred to as high-content screening, high-content analysis, or high-throughput microscopy) is continuously increasing, and generative models can be used to produce images for testing these algorithms in which the expected answer is known.
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Affiliation(s)
- Robert F Murphy
- Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
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