Literature DB >> 16383422

Solving the advection-diffusion equations in biological contexts using the cellular Potts model.

Debasis Dan1, Chris Mueller, Kun Chen, James A Glazier.   

Abstract

The cellular Potts model (CPM) is a robust, cell-level methodology for simulation of biological tissues and morphogenesis. Both tissue physiology and morphogenesis depend on diffusion of chemical morphogens in the extra-cellular fluid or matrix (ECM). Standard diffusion solvers applied to the cellular potts model use finite difference methods on the underlying CPM lattice. However, these methods produce a diffusing field tied to the underlying lattice, which is inaccurate in many biological situations in which cell or ECM movement causes advection rapid compared to diffusion. Finite difference schemes suffer numerical instabilities solving the resulting advection-diffusion equations. To circumvent these problems we simulate advection diffusion within the framework of the CPM using off-lattice finite-difference methods. We define a set of generalized fluid particles which detach advection and diffusion from the lattice. Diffusion occurs between neighboring fluid particles by local averaging rules which approximate the Laplacian. Directed spin flips in the CPM handle the advective movement of the fluid particles. A constraint on relative velocities in the fluid explicitly accounts for fluid viscosity. We use the CPM to solve various diffusion examples including multiple instantaneous sources, continuous sources, moving sources, and different boundary geometries and conditions to validate our approximation against analytical and established numerical solutions. We also verify the CPM results for Poiseuille flow and Taylor-Aris dispersion.

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Year:  2005        PMID: 16383422     DOI: 10.1103/PhysRevE.72.041909

Source DB:  PubMed          Journal:  Phys Rev E Stat Nonlin Soft Matter Phys        ISSN: 1539-3755


  7 in total

1.  Adhesion between cells, diffusion of growth factors, and elasticity of the AER produce the paddle shape of the chick limb.

Authors:  Nikodem J Popławski; Maciej Swat; J Scott Gens; James A Glazier
Journal:  Physica A       Date:  2007-01-01       Impact factor: 3.263

2.  Simulation of single-species bacterial-biofilm growth using the Glazier-Graner-Hogeweg model and the CompuCell3D modeling environment.

Authors:  Nikodem J Popławski; Abbas Shirinifard; Maciej Swat; James A Glazier
Journal:  Math Biosci Eng       Date:  2008-04       Impact factor: 2.080

3.  A multiscale model of thrombus development.

Authors:  Zhiliang Xu; Nan Chen; Malgorzata M Kamocka; Elliot D Rosen; Mark Alber
Journal:  J R Soc Interface       Date:  2008-07-06       Impact factor: 4.118

4.  Spatio-temporal morphology changes in and quenching effects on the 2D spreading dynamics of cell colonies in both plain and methylcellulose-containing culture media.

Authors:  N E Muzzio; M A Pasquale; M A C Huergo; A E Bolzán; P H González; A J Arvia
Journal:  J Biol Phys       Date:  2016-06-07       Impact factor: 1.365

5.  Fluid dynamics in heart development: effects of hematocrit and trabeculation.

Authors:  Nicholas A Battista; Andrea N Lane; Jiandong Liu; Laura A Miller
Journal:  Math Med Biol       Date:  2018-12-05       Impact factor: 1.854

6.  Computer simulations of cell sorting due to differential adhesion.

Authors:  Ying Zhang; Gilberto L Thomas; Maciej Swat; Abbas Shirinifard; James A Glazier
Journal:  PLoS One       Date:  2011-10-18       Impact factor: 3.240

7.  A local uPAR-plasmin-TGFβ1 positive feedback loop in a qualitative computational model of angiogenic sprouting explains the in vitro effect of fibrinogen variants.

Authors:  Sonja E M Boas; Joao Carvalho; Marloes van den Broek; Ester M Weijers; Marie-José Goumans; Pieter Koolwijk; Roeland M H Merks
Journal:  PLoS Comput Biol       Date:  2018-07-06       Impact factor: 4.475

  7 in total

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