Unraveling the Promise of Computing DNA Data Storage: An Investigative Analysis of Advancements, Challenges, Future Directions

MPhil Research Postgraduate Student, Master of Philosophy (MPhil) in Machine Learning and Machine Intelligence, Department of Engineering, University of Cambridge, United Kingdom
* Corresponding author. Email: zarifbinakhtarg@gmail.com; zarifbinakhtar@ieee.org
Manuscript submitted April 10, 2024; revised April 30, 2024; accepted May 3, 2024.

Abstract—This research is dedicated to a comprehensive examination of DNA-based data storage systems, emphasizing the fundamental principles of DNA computing and its applications for long-term data archival. The primary objective is to critically assess the capabilities and limitations of DNA in meeting the exponential demand for robust and sustainable data storage solutions. Employing an in-depth iterative exploration, the research delves into recent breakthroughs in synthetic biology tools and unconventional computing functional methodologies in line with artificial intelligence (AI) technicality features, particularly emphasizing the transformative machine intelligence impact of the interdisciplinary collaborations within biomedical engineering and AI application domains. The findings illuminate the promise of DNA data storage as a viable solution for addressing the escalating data storage demands of the digital era, shedding light on its efficiency and integration of AI, scalability, and transformative potential. Ultimately, this research contributes to the development of efficient and scalable DNA data storage technologies, the role of AI and machine intelligence integration highlighting their immense significance in the ever-evolving landscape of data management towards the future and beyond..

keywords—Artificial Intelligence (AI), Biomedical Engineering (BME), Deep Learning (DL), DNA Data Storage, Machine Learning (ML), synthetic biology tools, unconventional computing.

Cite: Zarif Bin Akhtar"Unraveling the Promise of Computing DNA Data Storage: An Investigative Analysis of Advancements, Challenges, Future Directions," Journal of Advances in Artificial Intelligence vol. 2, no. 1, pp. 122-137, 2024.

Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).