Static Virus Spread Algorithm for DNA Sequence Design

TL;DR

This paper introduces a novel static virus spread algorithm (SVS) for designing high-quality DNA sequences, optimizing for non-complementarity, melting temperature, and base proportion, validated through simulations and experiments.

Contribution

The paper presents a new meta-heuristic evolutionary algorithm specifically for DNA sequence design, addressing key biological constraints effectively.

Findings

Designed DNA sequences meet non-complementarity constraints

Sequences have balanced base proportions and stable melting temperatures

Experimental validation confirms effectiveness of the sequences

Abstract

DNA is not only the genetic material of life, but also a favorable material for a new computing model. Various research works based on DNA computing have been carried out in recent years. DNA sequence design is the foundation of such research. The sequence quality directly affects the universality, robustness, and stability of DNA computing. How to design DNA sequences depends on the biological properties and target requirements, which is a typical combinatorial optimization problem. In this paper, in order to design DNA sequences with high-quality, we propose a novel meta-heuristic evolutionary algorithm, termed the static virus spread algorithm (SVS). Through this algorithm, we focus on the constraints of universal DNA sequence design and produce a large number of DNA sequences with non-complementarity and small difference in melting temperature as the objectives, and fully considering the balanced proportion of the four bases. The computer simulation and polyacrylamide gel electrophoresis experiments show that the high-quality DNA sequences designed by this algorithm are effective, which is expected to provide a convenient tool for sequence preparation before DNA biochemical operations.