Emergent cell and tissue dynamics from subcellular modeling of active biomechanical processes

Area of Science:

• Biophysics
• Computational Biology
• Cell Biology

Background:

• Cells are dynamic entities, altering behavior based on biochemical and biomechanical signals.
• Multicellular processes like morphogenesis, wound healing, and cancer involve significant cell deformation, proliferation, and migration.
• Accurate cell-based modeling necessitates algorithms capturing active cell behaviors and emergent tissue phenotypes.

Purpose of the Study:

• To extend the subcellular element model for simulating active biomechanical subcellular processes.
• To investigate emergent cell and tissue-level phenotypes from these active processes.
• To link simulation results to experimental observations in multicellular systems.

Main Methods:

• Extension of the subcellular element model to incorporate active biomechanical processes at the subcellular level.
• Development of algorithms to capture cell behavioral changes in response to external cues.
• Simulation of adaptive cell shape deformations, viscous tissue flow, and chemotactic cell streaming.

Main Results:

• Demonstrated adaptive cell shape deformations under slow stretching conditions.
• Modeled viscous flow dynamics in embryonic tissues.
• Simulated streaming patterns of chemotactic cells in epithelial-like sheets.
• Connected simulation outcomes to recent experimental findings.

Conclusions:

• The extended subcellular element model effectively captures active cell behaviors and predicts emergent tissue-level phenotypes.
• The model provides a framework for understanding how subcellular active processes influence larger-scale multicellular phenomena.
• This approach facilitates the integration of computational modeling with experimental data in cell and tissue dynamics.