ArticleJournal of nanobiotechnology2025
Enzyme-driven triplex structure-based DNA logic circuits.
Article in Journal of nanobiotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- DNA-Based Boolean Logic Gates for Molecular Computation and Biosensing: A Critical Review.Molecular biotechnology · 2026Review
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Authors and funding
15 authors.
Funding
Abstract
DNA logic circuits have emerged as crucial media for biological computing due to their molecular-level parallelism and high biocompatibility. In the two mainstream systems currently prevalent, the strand-displacement system generally suffers from issues such as high strand complexity, signal leakage, and difficulties in cascading. In comparison, the enzyme-driven system has offered a further improvement in high rates and low leakage. However, it continues to encounter challenges pertaining to signal leakage and signal attenuation, which limit the development of intricate computational systems.This study introduces enzyme-powered triplex DNA logic circuits that simplify strand design through input-gate concatenation forming triplex structures, utilizing Bst 3.0 polymerase for efficient operation. System validation includes single-gate analysis, multi-level cascades (secondary/tertiary), complex logic circuits, and large-scale square root operations. Single-gate circuits achieve < 2-min half-completion times with minimal leakage. The two-level cascaded circuits exhibit minimal leakage and the longest half-completion time of less than 8 min, while 10-gate square root circuits maintain < 25-min operation with 24.3% reduced strand complexity and 25% faster reaction rates versus existing systems. The low-leakage architecture enables efficient DNA computing for biosensing, data storage, and synthetic biology applications, while its modular design supports scalable biological computing networks.
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