Autômatos celulares probabilísticos com aplicações a sistemas biológicos

Abstract

Biological systems are complex and their comprehension requires understanding the interactions between the individual components as well as emergent properties. Such systems are highly adaptive, dynamical, and evolve in time by processing information. The goal of the present work is to identify models that can be applied to biological systems. In particular, we have considered binary one-dimensional cellular automata under elementary rules, and have introduced a probability parameter aimed to alter an automaton's evolution in time in such a way as to allow each cell to occasionally disobey the rule in use. The results obtained suggest that, while trying to reduce the uncertainty that emerges from the interactions between cells, the system's components generate information, often giving rise to the appearance of surplus information resulting from the manner of their interactions. They also suggest the metaphorical use of cellular automata in the representation of complex biological processes, such as the immune response (by the immune system) and the rise of conscious states (in the neuronal system).