A parallel cellular automata algorithm for the deterministic simulation of 3-D multicellular tissue growth

2015 ◽  
Vol 18 (4) ◽  
pp. 1561-1579 ◽  
Author(s):  
Belgacem Ben Youssef
Keyword(s):  
Cellular Automata ◽  
Tissue Growth ◽  
Deterministic Simulation ◽  
Cellular Automata Algorithm

Simulation of Multiple Cell Population Dynamics Using a 3-D Cellular Automata Model for Tissue Growth

2012 ◽  
pp. 201-220
Author(s):  
Belgacem Ben Youssef ◽  
Lenny Tang
Keyword(s):  
Population Dynamics ◽  
Cellular Automata ◽  
Temporal Dynamics ◽  
Discrete Systems ◽  
Tissue Growth ◽  
State Transitions ◽  
Extended Model ◽  
Alternative Approach ◽  
Continuous Models ◽  
Multiple Cell

In this paper, the authors describe a computational model for the growth of multicellular tissues using a discrete approach based on cellular automata to simulate the tissue growth rates and population dynamics of multiple populations of proliferating and migrating cells. Each population of cells has its own division, motion, collision, and aggregation characteristics. These random dynamic processes can be modeled by appropriately choosing the governing rules of the state transitions of each computational site. This extended model contains a number of system parameters that allow their effects on the volume coverage, the overall tissue growth rate, and some other aspects of cell behavior like the average speed of locomotion to be explored. These discrete systems provide an alternative approach to continuous models for the purpose of describing the temporal dynamics of complex systems.

Static Recrystallization Modeling With a Cellular Automata Algorithm

2005 ◽  
Author(s):  
Yujie Wu ◽  
Qiang Yu ◽  
Sven K. Esche
Keyword(s):  
Cellular Automata ◽  
Single Phase ◽  
Static Recrystallization ◽  
Volume Fraction ◽  
Mechanical Processing ◽  
Kinetics Analysis ◽  
Multi Scale ◽  
Microstructure Prediction ◽  
Phase Systems ◽  
Cellular Automata Algorithm

This paper reports on one part of a research project supported by NSF, which aims at developing a multi-scale methodology for systematic microstructure prediction in thermo-mechanical processing of metals. Based on combining mesoscopic microstructure models with macroscopic process formulations, the methodology is expected to provide universally applicable and accurate microstructure prediction capabilities. Cellular Automata (CA) models have been widely used in scientific studies of various microstructural phenomena. This paper discusses the modeling of the static recrystallization phenomenon by employing a regular CA algorithm. The recrystallization processes of single-phase systems under different nucleation conditions are simulated followed by the recrystallization kinetics analysis for 200 × 200 two-dimensional lattice. The performed simulations of static recrystallization confirm that the recrystallized volume fractions are time dependent. Furthermore, the simulated microstructures validate the following Johnson-Mehl-Avrami-Kolmogorov (JMAK) model according to which the recrystallized volume fraction is a sigmoidal function of time, and their evolution matches the JMAK equation with the expected exponents.

Simulation of Multiple Cell Population Dynamics Using a 3-D Cellular Automata Model for Tissue Growth

2010 ◽  
Vol 1 (3) ◽  
pp. 1-18 ◽  
Author(s):  
Belgacem Ben Youssef ◽  
Lenny Tang
Keyword(s):  
Population Dynamics ◽  
Cellular Automata ◽  
Temporal Dynamics ◽  
Discrete Systems ◽  
Tissue Growth ◽  
State Transitions ◽  
Extended Model ◽  
Alternative Approach ◽  
Continuous Models ◽  
Multiple Cell

In this paper, the authors describe a computational model for the growth of multicellular tissues using a discrete approach based on cellular automata to simulate the tissue growth rates and population dynamics of multiple populations of proliferating and migrating cells. Each population of cells has its own division, motion, collision, and aggregation characteristics. These random dynamic processes can be modeled by appropriately choosing the governing rules of the state transitions of each computational site. This extended model contains a number of system parameters that allow their effects on the volume coverage, the overall tissue growth rate, and some other aspects of cell behavior like the average speed of locomotion to be explored. These discrete systems provide an alternative approach to continuous models for the purpose of describing the temporal dynamics of complex systems.

Sign in / Sign up

Export Citation Format

Share Document