Stable DNA Sequence Over Close-Ending and Pairing Sequences Constraint

Abstract

DNA computing is a new method based on molecular biotechnology to solve complex problems. The design of DNA sequences is a multi-objective optimization problem in DNA computing, whose objective is to obtain optimized sequences that satisfy multiple constraints to improve the quality of the sequences. However, the previous optimized DNA sequences reacted with each other, which reduced the number of DNA sequences that could be used for molecular hybridization in the solution and thus reduced the accuracy of DNA computing. In addition, a DNA sequence and its complement follow the principle of complementary pairing, and the sequence of base GC at both ends is more stable. To optimize the above problems, the constraints of Pairing Sequences Constraint (PSC) and Close-ending along with the Improved Chaos Whale (ICW) optimization algorithm were proposed to construct a DNA sequence set that satisfies the combination of constraints. The ICW optimization algorithm is added to a new predator–prey strategy and sine and cosine functions under the action of chaos. Compared with other algorithms, among the 23 benchmark functions, the new algorithm obtained the minimum value for one-third of the functions and two-thirds of the current minimum value. The DNA sequences satisfying the constraint combination obtained the minimum of fitness values and had stable and usable structures.

Evolutionary approach to construct robust codes for DNA-based data storage

“…One of the computational methods is evolutionary computation, which comprises a set of biologically inspired algorithms. For example, the author showed the chaos whale optimization with sine and cosine functions to construct the DNA sequence set ( Li et al, 2021b ). Similarly, Xiaoru and Ling (2021) delivered an evolutionary-based equilibrium optimization with a random search model to overcome the error rate in DNA-coding sets.…”

“…Nevertheless, these evolutionary studies have performed well by enhancing the efficiency of optimization algorithms by the mutation strategies, but they focus on particular biological coding constraints. The satisfaction of these constraints controls the oligonucleotide errors in the DNA sequences ( Sager and Stefanovic, 2006 ; Song et al, 2018 ; Li et al, 2021b ; Cao et al, 2022 ). For instance, the GC-content and Hamming distance constraints were considered for the oligonucleotide libraries construction by reducing the hybridization errors ( Aboluion et al, 2012 ; Organick et al, 2018 ; Yang et al, 2020 ; Rasool et al, 2022b ).…”

Levy Equilibrium Optimizer algorithm for the DNA storage code set

“…They are widely used in engineering to optimize traditional complex engineering problems such as distribution system [ 35 ], power flow [ 36 ], and power grid [ 37 ]. It is also often applied to solve optimization problems in the biological field [ 38 , 39 ].…”