FB2026_01 , released March 12, 2026
FB2026_01 , released March 12, 2026
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Citation
Ogita, G., Kondo, T., Ikawa, K., Uemura, T., Ishihara, S., Sugimura, K. (2022). Image-based parameter inference for epithelial mechanics.  PLoS Comput. Biol. 18(6): e1010209.
FlyBase ID
FBrf0253821
Publication Type
Research paper
Abstract
Measuring mechanical parameters in tissues, such as the elastic modulus of cell-cell junctions, is essential to decipher the mechanical control of morphogenesis. However, their in vivo measurement is technically challenging. Here, we formulated an image-based statistical approach to estimate the mechanical parameters of epithelial cells. Candidate mechanical models are constructed based on force-cell shape correlations obtained from image data. Substitution of the model functions into force-balance equations at the cell vertex leads to an equation with respect to the parameters of the model, by which one can estimate the parameter values using a least-squares method. A test using synthetic data confirmed the accuracy of parameter estimation and model selection. By applying this method to Drosophila epithelial tissues, we found that the magnitude and orientation of feedback between the junction tension and shrinkage, which are determined by the spring constant of the junction, were correlated with the elevation of tension and myosin-II on shrinking junctions during cell rearrangement. Further, this method clarified how alterations in tissue polarity and stretching affect the anisotropy in tension parameters. Thus, our method provides a novel approach to uncovering the mechanisms governing epithelial morphogenesis.
PubMed ID
PubMed Central ID
PMC9223404 (PMC) (EuropePMC)
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Secondary IDs
    Language of Publication
    English
    Additional Languages of Abstract
    Parent Publication
    Publication Type
    Journal
    Abbreviation
    PLoS Comput. Biol.
    Title
    PLoS Computational Biology
    Publication Year
    2005-
    ISBN/ISSN
    1553-7358 1553-734X
    Data From Reference
    Genes (2)