Actin automata with memory

TL;DR

This paper models actin filaments as automata with memory, showing that incorporating past states affects dynamics by slowing perturbation spread and altering pattern formations.

Contribution

It introduces a novel automaton model of actin filaments that includes memory of past states, extending previous momentary state transition models.

Findings

Memory slows down propagation of perturbations

Memory decreases entropy of patterns

Memory transforms traveling localizations into stationary oscillators

Abstract

Actin is a globular protein which forms long polar filaments in eukaryotic. The actin filaments play roles of cytoskeleton, motility units , information processing and learning. We model actin filament as a double chain of finite state machines, nodes, which take states `0' and `1'. The states are abstractions of absence and presence of a sub-threshold charge on an actin units corresponding to the nodes. All nodes update their state in parallel in discrete time. A node updates its current state depending on states of two closest neighbours in the node chain and two closest neighbours in the complementary chain. Previous models of actin automata considered momentary state transitions of nodes. We enrich the actin automata model by assuming that states of nodes depends not only on the current states of neighbouring node but also on their past states. Thus, we assess the effect of memory of past states on the dynamics of acting automata. We demonstrate in computational experiments that memory slows down propagation of perturbations, decrease entropy of space-time patterns generated, transforms travelling localisations to stationary oscillators, and stationary oscillations to still patterns.