Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

From Energy to Cellular Force in the Cellular Potts Model

View through CrossRef
Abstract Single and collective cell dynamics, cell shape changes, and cell migration can be conveniently represented by the Cellular Potts Model, a computational platform based on minimization of a Hamiltonian while permitting stochastic fluctuations. Using the fact that a force field is easily derived from a scalar energy ( F = −∇ H ), we develop a simple algorithm to associate effective forces with cell shapes in the CPM. We display the predicted forces for single cells of various shapes and sizes (relative to cell rest-area and cell rest-perimeter). While CPM forces are specified directly from the Hamiltonian on the cell perimeter, we infer internal forces using interpolation, and refine the results with smoothing. Predicted forces compare favorably with experimentally measured cellular traction forces. We show that a CPM model with internal signaling (such as Rho-GTPase-related contractility) can be associated with retraction-protrusion forces that accompany cell shape changes and migration. We adapt the computations to multicellular systems, showing, for example, the forces that a pair of swirling cells exert on one another, demonstrating that our algorithm works equally well for interacting cells. Finally, we show forces associated with the dynamics of classic cell-sorting experiments in larger clusters of model cells. Author summary Cells exert forces on their surroundings and on one another. In simulations of cell shape using the Cellular Potts Model (CPM), the dynamics of deforming cell shapes is traditionally represented by an energy-minimization method. We use this CPM energy, the Hamiltonian, to derive and visualize the corresponding forces exerted by the cells. We use the fact that force is the negative gradient of energy to assign forces to the CPM cell edges, and then extend the results to interior forces by interpolation. We show that this method works for single as well as multiple interacting model cells, both static and motile. Finally, we show favorable comparison between predicted forces and real forces measured experimentally.
Title: From Energy to Cellular Force in the Cellular Potts Model
Description:
Abstract Single and collective cell dynamics, cell shape changes, and cell migration can be conveniently represented by the Cellular Potts Model, a computational platform based on minimization of a Hamiltonian while permitting stochastic fluctuations.
Using the fact that a force field is easily derived from a scalar energy ( F = −∇ H ), we develop a simple algorithm to associate effective forces with cell shapes in the CPM.
We display the predicted forces for single cells of various shapes and sizes (relative to cell rest-area and cell rest-perimeter).
While CPM forces are specified directly from the Hamiltonian on the cell perimeter, we infer internal forces using interpolation, and refine the results with smoothing.
Predicted forces compare favorably with experimentally measured cellular traction forces.
We show that a CPM model with internal signaling (such as Rho-GTPase-related contractility) can be associated with retraction-protrusion forces that accompany cell shape changes and migration.
We adapt the computations to multicellular systems, showing, for example, the forces that a pair of swirling cells exert on one another, demonstrating that our algorithm works equally well for interacting cells.
Finally, we show forces associated with the dynamics of classic cell-sorting experiments in larger clusters of model cells.
Author summary Cells exert forces on their surroundings and on one another.
In simulations of cell shape using the Cellular Potts Model (CPM), the dynamics of deforming cell shapes is traditionally represented by an energy-minimization method.
We use this CPM energy, the Hamiltonian, to derive and visualize the corresponding forces exerted by the cells.
We use the fact that force is the negative gradient of energy to assign forces to the CPM cell edges, and then extend the results to interior forces by interpolation.
We show that this method works for single as well as multiple interacting model cells, both static and motile.
Finally, we show favorable comparison between predicted forces and real forces measured experimentally.

Related Results

Remote homology search with hidden Potts models
Remote homology search with hidden Potts models
AbstractMost methods for biological sequence homology search and alignment work with primary sequence alone, neglecting higher-order correlations. Recently, statistical physics mod...
Selection of sequence motifs and generative Hopfield-Potts models for protein families
Selection of sequence motifs and generative Hopfield-Potts models for protein families
Statistical models for families of evolutionary related proteins have recently gained interest: in particular pairwise Potts models, as those inferred by the Direct-Coupling Analys...
Role of vitamin D in patients with Potts spine
Role of vitamin D in patients with Potts spine
Potts spine is caused by Mycobacterium tuberculosis, a slow growing gram-positive, acid-fast bacillus which becomes lodged in the bone via Batson’s venous plexus and lymphatic from...
Phase transitions in the diluted 2D three-state Potts model on a square lattice
Phase transitions in the diluted 2D three-state Potts model on a square lattice
The computer simulation method was used to study phase transitions in a two-dimensional site-diluted 3-state Potts model. Systems with linear dimensions Lx L=N, L=10/160 at a spin ...
Robert Schumann and Mary Potts
Robert Schumann and Mary Potts
Robert Schumann's Bunte Blätter , published in December 1851 as Opus 99, is dedicated to "Miss Mary Potts." This American woman—unidentified in the Schumann literature—is here reve...
Mechanisms of E-cadherin force transmission
Mechanisms of E-cadherin force transmission
<p>Cells are subject to a wide variety of forces throughout their lifetimes. During epithelial morphogenesis, epithelial cells form sheets of cells that line the cavities and...
Introducing Optimal Energy Hub Approach in Smart Green Ports based on Machine Learning Methodology
Introducing Optimal Energy Hub Approach in Smart Green Ports based on Machine Learning Methodology
Abstract The integration of renewable energy systems in port facilities is essential for achieving sustainable and environmentally friendly operations. This paper presents ...

Back to Top