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    Using a continuum model to decipher the mechanics of embryonic tissue spreading from time-lapse image sequences: An approximate Bayesian computation approach

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    Author
    Stepien, Tracy L
    Lynch, Holley E
    Yancey, Shirley X
    Dempsey, Laura
    Davidson, Lance A
    Affiliation
    Univ Arizona, Dept Math
    Issue Date
    2019-06-27
    
    Metadata
    Show full item record
    Publisher
    PUBLIC LIBRARY SCIENCE
    Citation
    Stepien TL, Lynch HE, Yancey SX, Dempsey L, Davidson LA (2019 ) Using a continuum model to decipher the mechanics of embryonic tissue spreading from time-lapse image sequences: An approximate Bayesian computation approach. PLoS ONE 14(6): e0218021. https://doi.org/10.1371/journal.pone.0218021
    Journal
    PLOS ONE
    Rights
    Copyright © 2019 Stepien et al. This is an open access article distributed under the terms of the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium,provided the original author and source are credited.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Advanced imaging techniques generate large datasets capable of describing the structure and kinematics of tissue spreading in embryonic development, wound healing, and the progression of many diseases. These datasets can be integrated with mathematical models to infer biomechanical properties of the system, typically identifying an optimal set of parameters for an individual experiment. However, these methods offer little information on the robustness of the fit and are generally ill-suited for statistical tests of multiple experiments. To overcome this limitation and enable efficient use of large datasets in a rigorous experimental design, we use the approximate Bayesian computation rejection algorithm to construct probability density distributions that estimate model parameters for a defined theoretical model and set of experimental data. Here, we demonstrate this method with a 2D Eulerian continuum mechanical model of spreading embryonic tissue. The model is tightly integrated with quantitative image analysis of different sized embryonic tissue explants spreading on extracellular matrix (ECM) and is regulated by a small set of parameters including forces on the free edge, tissue stiffness, strength of cell-ECM adhesions, and active cell shape changes. We find statistically significant trends in key parameters that vary with initial size of the explant, e.g., for larger explants cell-ECM adhesion forces are weaker and free edge forces are stronger. Furthermore, we demonstrate that estimated parameters for one explant can be used to predict the behavior of other similarly sized explants. These predictive methods can be used to guide further experiments to better understand how collective cell migration is regulated during development.
    Note
    Open access journal
    ISSN
    1932-6203
    PubMed ID
    31246967
    DOI
    10.1371/journal.pone.0218021
    Version
    Final published version
    Sponsors
    National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01 HD044750, R21 ES019259]; National Science FoundationNational Science Foundation (NSF) [CAREER IOS-0845775, CMMI-1100515]
    ae974a485f413a2113503eed53cd6c53
    10.1371/journal.pone.0218021
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