2019
DOI: 10.1103/physreve.100.063307
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Unbiased on-lattice domain growth

Abstract: Domain growth is a key process in many areas of biology, including embryonic development, the growth of tissue, and limb regeneration. As a result, mechanisms for incorporating it into traditional models for cell movement, interaction, and proliferation are of great importance. A previously wellused method in order to incorporate domain growth into on-lattice reaction-diffusion models causes a build up of particles on the boundaries of the domain, which is particularly evident when diffusion is low in comparis… Show more

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Cited by 1 publication
(7 citation statements)
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“…When a jump occurs from the first compartment into the ghost cell, we reduce n 1 (t) by 1 and add a new particle to the ghost cell by sampling a uniform position within the ghost cell (that is, add a new particle at a position y* ∼ Unif(I(t) − h c (t), I(t))) and subsequently increase n GC (t) by 1. Domain growth is implemented using the stretching method [24] for the mesoscale, and via the SDE for the microscale. The algorithm for the gGCM can be found in algorithm 2, and a schematic for the static case is given in figure 2.…”
Section: The Ghost Cell Methodsmentioning
confidence: 99%
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“…When a jump occurs from the first compartment into the ghost cell, we reduce n 1 (t) by 1 and add a new particle to the ghost cell by sampling a uniform position within the ghost cell (that is, add a new particle at a position y* ∼ Unif(I(t) − h c (t), I(t))) and subsequently increase n GC (t) by 1. Domain growth is implemented using the stretching method [24] for the mesoscale, and via the SDE for the microscale. The algorithm for the gGCM can be found in algorithm 2, and a schematic for the static case is given in figure 2.…”
Section: The Ghost Cell Methodsmentioning
confidence: 99%
“…To extend the domain, we use the stretching method of Smith et al [24]. As the domain grows through a series of discrete fixed size extensions, the compartments grow in length uniformly.…”
Section: Mesoscopic Modellingmentioning
confidence: 99%
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