2022, Vol. 3, Issue 1, Part A
Cellular automata models and their applications in biology and biomedicine
Author(s): Nandita Mahesh
Abstract: Besides being the basis for modern computers, Alan Turing’s conceptual automaton, also known as the “Turing machine”, provides a useful way of viewing organisms. For instance, an organism (automaton) is affected by the outside world (inputs) and it behaves (outputs) according to its habits or condition (state) and nature (program). The “cellular automaton” extends this analogy to provide a way of viewing whole populations of interacting “cells”. By creating appropriate rules into a cellular automaton, one can simulate many kinds of complex behavior. A cellular automaton is an array of identically programmed automata that can interact with one another. With the invention of parallel computing, the “cellular automaton” has become central to computation. The crucial difference between cellular automata and sets of independent automata is that the cells interact with each other. These interactions can completely change the overall behavior of a set of objects. Hence, we need to consider cellular automata as whole systems, rather than as many individuals. Despite their simplicity, cellular automata are capable of an astonishing variety of Behaviors. An important property is that they tend to be “self-organizing”. Starting from complex and random cell configurations, to simple and conceptual automata behavior. Many natural phenomena can be modelled as cellular automata. For instance, linear automata provide good models of pattern formation on mollusk shells and the growth of crystals, such as snowflakes, are modelled as hexagonal automata growing from a seed. The state of each cell depends on the number of living neighbors it has: with Less than 2 living neighbor cells they die from isolation, but with more than 3 they die from overcrowding, and with exactly 3 living neighbors, a birth occurs in a dead cell. As with linear automata, these rules tend to produce order from arbitrary initial configurations. Ultimately, most configurations either disappear or break up into isolated pieces of pattern. Countless more applications of cellular automata will be discussed in detail in the research paper. Cellular automata provide global representations for processes that require context and involve discrete state changes in interacting populations of separate automata. The usual arrangement of cells is a rectangular grid, but different or flexible structures are appropriate for some processes, such as growth. Therefore, in understanding biological processes and in developing modern day medicine, the study of cellular automata can prove to be extremely useful.
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How to cite this article:
Nandita Mahesh. Cellular automata models and their applications in biology and biomedicine. Int J Res Circuits Devices Syst 2022;3(1):50-54.