Extracting cellular automaton rules from physical Langevin equation models for single and collective cell migration.

Cellular automata (CA) are discrete time, space, and state models which are extensively used for modeling biological phenomena. CA are "on-lattice" models with low computational demands. In particular, lattice-gas cellular automata (LGCA) have been introduced as models of single and collective cell migration. The interaction rule dictates the behavior of a cellular automaton model and is critical to the model's biological relevance. The LGCA model's interaction rule has been typically chosen phenomenologically. In this paper, we introduce a method to obtain lattice-gas cellular automaton interaction rules from physically-motivated "off-lattice" Langevin equation models for migrating cells. In particular, we consider Langevin equations related to single cell movement (movement of cells independent of each other) and collective cell migration (movement influenced by cell-cell interactions). As examples of collective cell migration, two different alignment mechanisms are studied: polar and nematic alignment. Both kinds of alignment have been observed in biological systems such as swarms of amoebae and myxobacteria. Polar alignment causes cells to align their velocities parallel to each other, whereas nematic alignment drives cells to align either parallel or antiparallel to each other. Under appropriate assumptions, we have derived the LGCA transition probability rule from the steady-state distribution of the off-lattice Fokker-Planck equation. Comparing alignment order parameters between the original Langevin model and the derived LGCA for both mechanisms, we found different areas of agreement in the parameter space. Finally, we discuss potential reasons for model disagreement and propose extensions to the CA rule derivation methodology.

Dielectric breakdown model for conductor-loaded and insulator-loaded composite materials.

In the present work we generalize the dielectric breakdown model to describe dielectric breakdown patterns in both conductor-loaded and insulator-loaded composites. The present model is an extension of a previous one [F. Peruani et al., Phys. Rev. E 67, 066121 (2003)] presented by the authors to describe dielectric breakdown patterns in conductor-loaded composites. Particles are distributed at random in a matrix with a variable concentration p. The generalized model assigns different probabilities P(i,k-->i('),k(')) to breakdown channel formation according to particle characteristics. Dielectric breakdown patterns are characterized by their fractal dimension D and the parameters of the Weibull distribution. Studies are carried out as a function of the fraction of inhomogeneities, p.

Dielectric breakdown model for composite materials.

This paper addresses the problem of dielectric breakdown in composite materials. The dielectric breakdown model was generalized to describe dielectric breakdown patterns in conductor-loaded composites. Conducting particles are distributed at random in the insulating matrix, and the dielectric breakdown propagates according to new rules to take into account electrical properties and particle size. Dielectric breakdown patterns are characterized by their fractal dimension D and the parameters of the Weibull distribution. Studies are carried out as a function of the fraction of conducting inhomogeneities, p. The fractal dimension D of electrical trees approaches the fractal dimension of a percolation cluster when the fraction of conducting particles approximates the percolation limit.