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

Area of Science:

• Biophysics
• Developmental Biology
• Computational Biology

Background:

• Advanced imaging generates large datasets for studying tissue dynamics.
• Current methods for inferring biomechanical properties lack robustness and statistical power.
• Integrating imaging data with mathematical models is crucial but challenging.

Purpose of the Study:

• To develop a robust computational framework for analyzing large biological datasets.
• To estimate biomechanical parameters of embryonic tissue spreading using a novel Bayesian approach.
• To enable rigorous statistical testing and prediction in developmental studies.

Main Methods:

• Utilized approximate Bayesian computation (ABC) rejection algorithm for parameter estimation.
• Developed a 2D Eulerian continuum mechanical model of embryonic tissue spreading.
• Integrated the model with quantitative image analysis of embryonic tissue explants on extracellular matrix (ECM).

Main Results:

• Constructed probability density distributions for model parameters.
• Identified statistically significant trends in parameters (e.g., cell-ECM adhesion, free edge forces) varying with explant size.
• Demonstrated predictive power of estimated parameters for similar-sized explants.

Conclusions:

• The ABC method provides robust parameter estimation and statistical analysis for large biological datasets.
• Explant size significantly influences biomechanical properties regulating collective cell migration.
• This predictive framework can guide future experiments in developmental biology.