Adam Kuzdraliński, Marek Miśkiewicz, Hubert Szczerba, Wojciech Mazurczyk, Tomasz Ociepa, Michał Lechowski, Bogdan Księżopolski
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Abstract
DNA-based technologies for object authentication and data storage are becoming an interesting alternative to classic identification systems, yet their practical implementation faces fundamental technical and commercial barriers that limit widespread adoption. This review presents an analysis of DNA tagging and storage technologies, assessing their technical features, cost-effectiveness, and real-world applicability through comparison of competing approaches. We demonstrate that DNA tagging and data storage applications exhibit fundamentally different requirements, necessitating divergent technological strategies rather than unified solutions. DNA tagging faces severe cost disadvantages ($1–$100 per authentication versus $0.01–$0.10 for established technologies) and extended verification times (30 min to 6+ hours versus instant readout), limiting viability to high-security, low-volume markets such as pharmaceuticals and luxury goods. Current commercial implementations frequently lack peer-reviewed validation, creating an evidence deficit that undermines enterprise confidence. Among current approaches, isothermal amplification methods (LAMP, RPA) combined with colorimetric detection represent the most promising pathway for field-deployable authentication, while Illumina sequencing platforms provide optimal performance for data storage applications. The absence of standardization frameworks fundamentally constrains commercial adoption across both domains, preventing interoperability and enabling unsubstantiated performance claims. We conclude that successful commercialization requires strategic reorientation toward application-specific optimization and integrative approaches where DNA serves as secondary authentication combined with established identifiers, rather than competing directly on speed and cost metrics.
This article is categorized under:
Structure and Mechanism > Molecular Structures
Data Science > Databases and Expert Systems
Molecular and Statistical Mechanics > Molecular Mechanics
期刊介绍：
Computational molecular sciences harness the power of rigorous chemical and physical theories, employing computer-based modeling, specialized hardware, software development, algorithm design, and database management to explore and illuminate every facet of molecular sciences. These interdisciplinary approaches form a bridge between chemistry, biology, and materials sciences, establishing connections with adjacent application-driven fields in both chemistry and biology. WIREs Computational Molecular Science stands as a platform to comprehensively review and spotlight research from these dynamic and interconnected fields.
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