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Updated: Jun 10, 2025

Probing Cell Mechanics with Bead-Free Optical Tweezers in the Drosophila Embryo

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Published on: November 2, 2018

Bayesian parameter inference for epithelial mechanics.

Xin Yan¹, Goshi Ogita², Shuji Ishihara³

¹Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8561, Japan.

Journal of Theoretical Biology

|October 12, 2024

Summary

This summary is machine-generated.

We enhanced image-based parameter inference for cell-based mechanical models using Bayesian statistics. This method accurately estimates parameters and reveals key insights into tissue mechanics, like the dispensability of cortical elasticity.

Keywords:

Bayesian inference Mechanics Modeling Morphogenesis

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Area of Science:

Biophysics
Computational Biology
Developmental Biology

Background:

Cell-based mechanical models, like the Cell Vertex Model (CVM), are crucial for understanding epithelial tissue dynamics.
Image-based parameter inference was previously developed to formulate CVM functions and estimate parameters from tissue image data.

Purpose of the Study:

To improve the utility and flexibility of image-based parameter inference by employing Bayesian statistics.
To refine the quantitative dissection of mechanical control in tissue dynamics.

Main Methods:

Developed and applied hierarchical and non-hierarchical Bayesian models for image-based parameter inference.
Validated the method using synthetic data for accurate parameter estimation.
Applied the Bayesian framework to Drosophila wing image data to analyze mechanical anisotropies and force-shape correlations.

Main Results:

Bayesian models provided accurate parameter estimates for CVM functions.
Parameter estimation reliability is strongly correlated with mechanical anisotropies in epithelial tissues.
The cortical elasticity term was found to be dispensable for explaining in vivo force-shape correlations.

Conclusions:

The Bayesian statistical framework enhances image-based parameter inference for cell-based mechanical models.
This approach facilitates the integration of diverse data types for quantitative analysis of tissue mechanics.
Findings contribute to a deeper understanding of the mechanical control governing tissue dynamics.