Dynamic Pattern Generation Research Articles

Metastable equilibrium with random local fluctuations: simulations of dynamic receptor pattern generation in a fluid mosaic membrane.

The generation of receptors in the animal cell's membrane was simulated by a model consisting of units in four possible states within a hexagonal area (playboard) of n units of a triangular network. The state of each unit was determined by the previous state of itself and of its six nearest neighbours, as regulated by a set of transition rules, which kept the mean relative frequency (m.r.f.) of each state constant. The transition rules were applied to the system exactly n times, regardless whether this involved selection of a unit on 0, 1, 2 or more occasions (programme "random selection with repeat"; RS-R). Comparison to previous results obtained by other ways of application of the rules has shown that the RS-R programme accounted for the highest m.r.f. of quiet (Q) units and Q clusters (sub-patterns), and also for the longest survival of Q configurations through several generations. Functioning of the model under the RS-R programme simulates an integrated system in metastable equilibrium with random local fluctuations, such as the cytoplasmic membrane is imagined to be in standardized environmental conditions. The formation-persistence-disintegration cycle of the sub-patterns is believed to simulate the dynamic generation of transitory receptor configurations in the cell membrane.

An advanced version of the dynamic receptor pattern generation model: the flux model.

In the recently described simple model of dynamic receptor pattern generation we used a two dimensional hexagonal area of a regular triangular network, formed by a statistically constant distribution of unit electostatic changes in a dynamic equilibrium. A set of 16 trnasition rules was applied to all units simultaneously; the next state of each unit depended only on the previous state of its six nearest neighbours, and the transition of the total pattern into the new one occurred in a single jump. Hence we designated the initial simple model as "jump model". In this paper we described an advanced version of the model, in which simplified rules are applied to one unit after the other in a sequential order, from left to right, starting with the top row of units. In the advanced version the state of a unit depends not only on that of its six nearest neighbours, but also on the state of all units preceding in sequence the one actually considered. This results in flux-like transitions. We therefore designated the advanced version as the "flux model". It is shown that the flux model represents a closer approximation of physical and biological realities than the original jump model.