PolyHoop: Soft particle and tissue dynamics with topological transitions

Vetter, Roman; Runser, Steve V.M.; Iber, Dagmar

doi:10.1016/j.cpc.2024.109128

Cited by 4 publications

(3 citation statements)

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“…Only one day of computation time is required to grow the tissue to 125,000 cells on an Intel Xeon W-2245 CPU (eight cores, 3.9 GHz) using 16 threads, for cells that possess 121 nodes and 238 triangular faces on average. The total time complexity of such a simulation is , where N c is the number of cells in the tissue, which is equivalent to the scaling observed in two-dimensional simulations 43 . Under similar settings, we tested the performance of CellSim3D 50 and Interacting Active Surfaces (IAS) 47 , two other cell-based 3D models offering low and high spatial resolution, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…(For possible ways to include frictional effects, see for example refs. 43 , 45 .) The first model connects adjacent pairs of faces { f a , f b } with elastic springs, while the second tightly couples pairs of adjacent nodes { n a , n b }.…”

Section: Methodsmentioning

confidence: 99%

“…To address the limitations of vertex models, a family of cell-based models sometimes referred to as deformable cell models (DCMs) has been developed. These models provide a more geometrically realistic representation of tissues by discretizing each cell membrane separately into a closed loop of connected points in two dimensions 32 – 43 or a closed triangulated surface in three dimensions 44 – 53 . The complex shapes that cells can adopt in DCMs make these models particularly suited to simulate phenomena such as the development of early embryos 54 or the cellular movements during wound healing 45 .…”

Section: Mainmentioning

confidence: 99%

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SimuCell3D: three-dimensional simulation of tissue mechanics with cell polarization

Runser,

Vetter,

Iber

2024

Nat Comput Sci

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The three-dimensional (3D) organization of cells determines tissue function and integrity, and changes markedly in development and disease. Cell-based simulations have long been used to define the underlying mechanical principles. However, high computational costs have so far limited simulations to either simplified cell geometries or small tissue patches. Here, we present SimuCell3D, an efficient open-source program to simulate large tissues in three dimensions with subcellular resolution, growth, proliferation, extracellular matrix, fluid cavities, nuclei and non-uniform mechanical properties, as found in polarized epithelia. Spheroids, vesicles, sheets, tubes and other tissue geometries can readily be imported from microscopy images and simulated to infer biomechanical parameters. Doing so, we show that 3D cell shapes in layered and pseudostratified epithelia are largely governed by a competition between surface tension and intercellular adhesion. SimuCell3D enables the large-scale in silico study of 3D tissue organization in development and disease at a great level of detail.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Mainmentioning

confidence: 99%

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SimuCell3D: three-dimensional simulation of tissue mechanics with cell polarization

Runser,

Vetter,

Iber

2024

Nat Comput Sci

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Computational tools for cellular scale biophysics

Stein,

Shelley

2024

Current Opinion in Cell Biology

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An adaptive numerical method for multi–cellular simulations of tissue development

Osborne

2024

Preprint

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In recent years, multi-cellular models, where cells are represented as individual interacting entities, are becoming ever popular. This has led to a proliferation of novel methods and simulation tools. The first aim of this paper is to review the numerical methods utilised by multi-cellular modelling tools and to demonstrate which numerical methods are appropriate for simulations of tissue and organ development and disease. The second aim is to introduce an adaptive time-stepping algorithm and to demonstrate its efficiency and accuracy. We focus on off-lattice, mechanics based, models where cell movement is defined by a series of differential equations, derived by considering a force balance. We see that many numerical methods have been used, ranging from simple Forward Euler approaches through to higher order single-step methods like Runge-Kutta 4 and multi-step methods like Adams-Bashforth 2. Through a series of exemplar multi-cellular simulations, we see that if: care is taken to have events (births deaths and re-meshing/re-arrangements) occur on common time-steps; and boundaries are imposed on all sub-steps of numerical methods or implemented using forces, then all numerical methods can converge with the correct order. We introduce an adaptive time-stepping method and demonstrate that the best compromise between L∞ error and run-time is to use Runge-Kutta 4 with an increased time-step and moderate adaptivity. We see that a judicious choice of numerical method can speed the simulation up by a factor of 10 and a further speed up by a factor of 4 can be achieved by using an adaptive time-step.

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PolyHoop: Soft particle and tissue dynamics with topological transitions

Cited by 4 publications

References 75 publications

SimuCell3D: three-dimensional simulation of tissue mechanics with cell polarization

SimuCell3D: three-dimensional simulation of tissue mechanics with cell polarization

Computational tools for cellular scale biophysics

An adaptive numerical method for multi–cellular simulations of tissue development

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