Reversible network reconnection model for simulating large deformation in dynamic tissue morphogenesis.

Area of Science:

• Biophysics
• Computational Biology
• Developmental Biology

Background:

• Tissue morphogenesis involves complex 3D cell deformations regulated by mechanical interactions.
• Existing 3D vertex models struggle with large deformations due to irreversibility and inconsistencies.

Purpose of the Study:

• To develop a novel computational model, the reversible network reconnection (RNR) model, for simulating large tissue deformations during morphogenesis.
• To address limitations of existing 3D vertex models, including geometrical, energetic, and topological irreversibility.

Main Methods:

• Developed the RNR model based on the 3D vertex model framework.
• Introduced new definitions for polygonal face shapes and improved conditions for network reconnections and potential energy functions.
• Incorporated a new constraint for cell-cell boundary shapes and performed mathematical/computational analyses.

Main Results:

• The RNR model successfully resolved geometrical, energetic, and topological irreversibility issues inherent in previous models.
• Simulations demonstrated the model's ability to accurately recapitulate large tissue deformations, including changes in local curvature and layer formation.
• The model effectively suppresses geometrical gaps and avoids irreversible network patterns during reconnections.

Conclusions:

• The RNR model provides a robust computational tool for studying tissue morphogenesis and large-scale deformations.
• This advancement enables more accurate in silico recapitulation of complex developmental processes.
• The RNR model overcomes critical limitations, paving the way for advanced simulations in developmental biology.