Computer Simulation of DNA Computing-Based Boolean Matrix Multiplication

TL;DR

This paper presents a Python simulation of a DNA computing algorithm for Boolean matrix multiplication, demonstrating the logical operations and potential of DNA-based computation as a research and educational tool.

Contribution

It develops a computer simulation of a DNA computing algorithm for Boolean matrix multiplication, aiding understanding and future development of DNA computing methods.

Findings

Simulation replicates key DNA computing steps

Provides a prototype for educational and research use

Highlights potential advantages of DNA-based computing

Abstract

DNA computing is an unconventional approach to computing that harnesses the parallelism and information storage capabilities of DNA molecules. It has emerged as a promising field with potential applications in solving a variety of computationally complex problems. This paper explores a DNA computing algorithm for Boolean matrix multiplication proposed by Nobuyuki et al. (2006) using a computer simulation, inspired by similar work done in the past by Obront (2021) for the DNA computing algorithm developed by Adleman (1994) for solving the Hamiltonian path problem. We develop a Python program to simulate the logical operations involved in the DNA-based Boolean matrix multiplication algorithm. The simulation replicates the key steps of the algorithm, including DNA sequence generation and hybridization, without imitating the physical behaviour of the DNA molecules. It is intended to serve as a basic prototype for larger, more comprehensive DNA computing simulators that can be used as educational or research tools in the future. Through this work, we aim to contribute to the understanding of DNA-based computing paradigms and their potential advantages and trade-offs compared to conventional computing systems, paving the way for future research and advancements in this emerging field.