A quantitative high-resolution computational mechanics cell model for growing and regenerating tissues

Area of Science:

• Biophysics
• Computational Biology
• Regenerative Medicine

Background:

• Mathematical models are crucial for understanding collective cell behavior in biology and medicine.
• Quantitative models are needed to accurately represent biological systems, such as liver regeneration.
• Cellular mechanics can significantly impact tissue repair processes like liver regeneration.

Purpose of the Study:

• To develop a high-resolution, 3D cell-based model integrating experimental data to study the biomechanical effects on liver regeneration.
• To quantitatively assess the impact of cell deformation and mechanical stress on the closure of drug-induced liver lesions.
• To compare the predictive power of this cell-based model with traditional center-based models.

Main Methods:

• A discrete, physically scalable network of viscoelastic elements representing individual cells in 3D.
• Incorporation of cell migration, growth, and division capabilities.
• Inference of mechanical parameters through comparison with optical stretcher experiments.
• Modeling cell-environment interactions, including with complex structures like blood vessels, using surface triangulation.

Main Results:

• The cell-based model accurately mimics cell deformation and provides subcellular-scale information.
• Simulations showed that the forces required for tissue lesion closure are significantly lower than predicted by center-based models.
• The model's generality was demonstrated through simulations of monolayer and multicellular spheroid growth.

Conclusions:

• The developed model offers quantitative insights into tissue organization and regeneration processes.
• It enables in silico hypothesis testing for biological systems where cell mechanics are important.
• The model can guide experimental design in fields like regenerative medicine and biotechnology.