Modelling cell shape in 3D structured environments: A quantitative comparison with experiments.

Area of Science:

• Biophysics
• Cell Biology
• Computational Biology

Background:

• Cell shape is critical for biological processes like migration and division.
• Predicting three-dimensional (3D) cell shape in structured environments remains a challenge.
• Existing models often fail to capture experimental cell morphology accurately.

Purpose of the Study:

• To compare different computational models for predicting 3D cell shape.
• To identify the most effective modeling approach for mesenchymal cells in structured environments.
• To advance the understanding of cell morphology in complex biological contexts.

Main Methods:

• Experimental observation of single mesenchymal cells in custom 3D scaffolds.
• Comparison of Fourier methods and area-minimizing surfaces with experimental data.
• Application and evaluation of various Cellular Potts Models (CPMs) with different energy formulations.

Main Results:

• Area-minimizing surface models showed significant discrepancies with experimental cell shapes.
• The Cellular Potts Model with a linear area energy Hamiltonian demonstrated superior accuracy compared to elastic area constraints.
• Explicitly modeling the cell nucleus did not enhance the accuracy of simulated cell shapes.

Conclusions:

• The Cellular Potts Model, particularly with linear area energy, is effective for modeling 3D cell shapes in structured environments.
• Advanced modeling approaches are necessary to accurately represent cell morphology beyond simple geometric constraints.
• This research provides a validated computational framework for studying cell shape dynamics.