On Cellular Automaton Approaches to Modeling Biological Cells

Abstract

Abstract. We discuss two different types of Cellular Automata (CA): lattice-gasbased cellular automata (LGCA) and the cellular Potts model (CPM), and describe their applications in biological modeling.LGCA were originally developed for modeling ideal gases and fluids. We describe several extensions of the classical LGCA model to self-driven biological cells. In particular, we review recent models for rippling in myxobacteria, cell aggregation, swarming, and limb bud formation. These LGCA-based models show the versatility of CA in modeling and their utility in addressing basic biological questions.The CPM is a more sophisticated CA, which describes individual cells as extended objects of variable shape. We review various extensions to the original Potts model and describe their application to morphogenesis; the development of a complex spatial structure by a collection of cells. We focus on three phenomena: cell sorting in aggregates of embryonic chicken cells, morphological development of the slime mold Dictyostelium discoideum and avascular tumor growth. These models include intercellular and extracellular interactions, as well as cell growth and death.

Tutorial on agent-based modelling and simulation

“…Generally, authors contend that agent-based modelling offers benefits beyond traditional modelling approaches for the problems studied and use the models as electronic laboratories as an adjunct to traditional laboratories. Cellular automata are a natural application for modelling cellular systems (Alber et al, 2003). One approach uses the cellular automata grid to model structures of stationary cells comprising a tissue matrix.…”

Modeling Three-Dimensional Spatial Regulation of Bacterial Cell Division (Dissertation)

“…The cellular automata (CA) method employs a lattice of uniform voxels with a finite number of states that evolves in discrete-time (Weimar and Boon, 1994;Alber et al, 2003). Stundzia and Lumsden (1996) extended the Gillespie's stochastic approach (Gillespie, 1976(Gillespie, , 1977 to be used in subvolumes for spatial modeling.…”

Towards whole-organ modelling of tumour growth

“…Our aim in this section is to give a flavour of the different approaches used by researchers in this field; an exhaustive and complete review of existing models is beyond the scope of this paper. For more extensive reviews see, for example, Alber et al (2002), Moreira and Deutsch (2002) and Preziosi (2003).…”

“…For more extensive reviews of CA modelling of biological systems in general and tumour growth in particular see Alber et al (2002) and Moreira and Deutsch (2002), respectively.…”