Evolutionary approach to construct robust codes for DNA-based data storage - Rasool, Abdur; Jiang, Qingshan; Wang, Yang; Huang, Xiaoluo; Qu, Qiang; Dai, Junbiao (Frontiers in Genetics, 2023)
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 bio-constraints 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 utilized 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 (MFOS) to optimize these problems by constructing reverse-complement constraints. MFOS aims to attain global optimal 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 employ 19 state-of-the-art functions. Compared with existing studies, the proposed approach with three different bio-constraints substantially improves the lower bounds of the DNA codes by 12–28% and significantly reduces errors.