From energy to cellular forces in the Cellular Potts Model: An algorithmic approach

Area of Science:

• Computational biology
• Biophysics
• Cellular dynamics

Background:

• The Cellular Potts Model (CPM) is a computational tool for simulating cell behavior.
• Deriving forces from energy landscapes is a common approach in physics.

Purpose of the Study:

• To develop a simple algorithm for associating effective forces with cell shapes in the CPM.
• To predict and validate cellular traction forces using computational methods.

Main Methods:

• Developed an algorithm to derive force fields from the CPM Hamiltonian.
• Approximated and smoothed internal cell forces using interpolation.
• Applied the algorithm to single cells, multicellular systems, and cell sorting simulations.

Main Results:

• Successfully predicted traction forces for single cells of various shapes and sizes.
• Demonstrated correlation between CPM internal signaling and cell retraction-protrusion forces.
• Validated the algorithm's efficacy in modeling forces within interacting multicellular systems.

Conclusions:

• The developed algorithm provides a robust method for calculating cell-associated forces within the CPM framework.
• This approach enhances the predictive power of CPM for cell migration, shape dynamics, and multicellular interactions.