A Strategy-based Optimization Algorithm to Design Codes for DNA Data Storage System

Rasool, Abdur; Qu, Qiang; Jiang, Qingshan; Wang, Yang

doi:10.1007/978-3-030-95388-1_19

Cited by 6 publications

(13 citation statements)

References 19 publications

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“…In retrospective studies ( Cao et al, 2020b ; Rasool et al, 2022a ), some sequences were similar among the set of DNA sequences, including reverse or reverse-complement, and then non-specific hybridization occurred, which caused errors by forming the secondary structure (SS). A sequence structure in which a set of pairs of nucleotides support a secondary structure is known as the stem, while the number of repeated nucleotides is known as the stem length.…”

Section: Dna Coding Constraintsmentioning

confidence: 99%

“…A multi-verse optimizer (MVO) was made synergistic by damping the strategy to gain a stable state in global search, and it also reported 4–16% of improved DNA-coding sets ( Cao et al, 2020b ). Rasool et al (2022a) used the opposition-based learning (OBL) mutation strategy ( Dong et al, 2017 ) for three-dimensional search space with a meta-heuristic algorithm ( Mirjalili, 2015 ) to generate the DNA-coding sets for data storage. The codes generated by the improved algorithm (MFOL) enhanced the DNA-coding sets over the altruistic algorithm ( Limbachiya et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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DNA is a practical storage medium with high density, durability, and capacity to accommodate exponentially growing data volumes. A DNA sequence structure is a biocomputing problem that requires satisfying bioconstraints to design robust sequences. Existing evolutionary approaches to DNA sequences result in errors during the encoding process that reduces the lower bounds of DNA coding sets used for molecular hybridization. Additionally, the disordered DNA strand forms a secondary structure, which is susceptible to errors during decoding. This paper proposes a computational evolutionary approach based on a synergistic moth-flame optimizer by Levy flight and opposition-based learning mutation strategies to optimize these problems by constructing reverse-complement constraints. The MFOS aims to attain optimal global solutions with robust convergence and balanced search capabilities to improve DNA code lower bounds and coding rates for DNA storage. The ability of the MFOS to construct DNA coding sets is demonstrated through various experiments that use 19 state-of-the-art functions. Compared with the existing studies, the proposed approach with three different bioconstraints substantially improves the lower bounds of the DNA codes by 12–28% and significantly reduces errors.

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Section: Dna Coding Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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“…In our previous work, a novel algorithm was developed to generate the DNA codes. The obtained DNA codes' errors have been corrected to a limited extent with GC-content and no-run-length constraints [12].…”

Section: Dna Coding With Combinatorial Bio-constraintsmentioning

confidence: 99%

“…Their results revealed 4-16% more improved DNA coding than that of [10], which suggests that the increase in constraints can improve the codes for high-density DNA data storage. In 2021, our previous paper [12] extended the work [10] by proposing a novel algorithm to construct the DNA coding sets with improved lower bounds. The proposed algorithm was applied with GC-content and no-run-length constraints and achieved 30% better lower bounds.…”

Section: Introductionmentioning

confidence: 99%

“…Their studies furnish the basic idea of combinatorial constraints to generate DNA codes with minimum errors. Although the literature mentioned above [10][11][12]17,20] received high storage DNA code sets and coding rates, these studies do not provide a sufficient method to design higher DNA codes in the shorter sequences that must satisfy the biological constraints, which is enormously important for a stable density-based DNA storage system.…”

Section: Introductionmentioning

confidence: 99%

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Bio-Constrained Codes with Neural Network for Density-Based DNA Data Storage

Rasool

Wang

et al. 2022

Mathematics

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DNA has evolved as a cutting-edge medium for digital information storage due to its extremely high density and durable preservation to accommodate the data explosion. However, the strings of DNA are prone to errors during the hybridization process. In addition, DNA synthesis and sequences come with a cost that depends on the number of nucleotides present. An efficient model to store a large amount of data in a small number of nucleotides is essential, and it must control the hybridization errors among the base pairs. In this paper, a novel computational model is presented to design large DNA libraries of oligonucleotides. It is established by integrating a neural network (NN) with combinatorial biological constraints, including constant GC-content and satisfying Hamming distance and reverse-complement constraints. We develop a simple and efficient implementation of NNs to produce the optimal DNA codes, which opens the door to applying neural networks for DNA-based data storage. Further, the combinatorial bio-constraints are introduced to improve the lower bounds and to avoid the occurrence of errors in the DNA codes. Our goal is to compute large DNA codes in shorter sequences, which should avoid non-specific hybridization errors by satisfying the bio-constrained coding. The proposed model yields a significant improvement in the DNA library by explicitly constructing larger codes than the prior published codes.

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Large‐Scale Cardiac Muscle Cell‐Based Coupled Oscillator Network for Vertex Coloring Problem

Ren

Gomez

et al. 2023

Advanced Intelligent Systems

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Modern computers require an exponential increase in resources when solving computationally hard problems, motivating the need for an alternative computing platform to solve such problems in an energy‐efficient manner. Vertex coloring, a nondeterministic polynomial time (NP‐hard) combinatorial optimization problem, is one such problem. Herein, an experimental demonstration of using cardiac cell‐based bio‐oscillator network coupling dynamics to solve a vertex coloring problem in various scales of graphs using a simple cell patterning method to construct scalable and controlled cardiac cell networks is presented. Although there are limitations to using these cardiac cells as oscillators, such as their low frequency compared to complementary metal–oxide–semiconductor (CMOS) oscillators, that result in longer processing times, the accuracy in large graph instances, the significantly less amount of energy consumption, and the ease of fabrication and potential to extend this system to massively parallel 3D structures make the bio‐oscillators a promising new platform for collective computing applications.

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A Strategy-based Optimization Algorithm to Design Codes for DNA Data Storage System

Cited by 6 publications

References 19 publications

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

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

Bio-Constrained Codes with Neural Network for Density-Based DNA Data Storage

Large‐Scale Cardiac Muscle Cell‐Based Coupled Oscillator Network for Vertex Coloring Problem

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