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DNA Molecular Storage System: Transferring Digitally Encoded Information through Bacterial Nanonetworks
Mathematics, University of Padua, Padua, Italy.
Örebro universitet, Institutionen för naturvetenskap och teknik. (Machine Perception & Interaction)ORCID-id: 0000-0001-9293-7711
Department of Science, Waterford Institute of Technology, Waterford, Ireland.
Mathematics, University of Padua, Padua, Italy.
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2019 (Engelska)Ingår i: IEEE Transactions on Emerging Topics in Computing, ISSN 2168-6750Artikel i tidskrift (Refereegranskat) Epub ahead of print
Abstract [en]

Since the birth of computer and networks, fuelled by pervasive computing, Internet of Things and ubiquitous connectivity, the amount of data stored and transmitted has exponentially grown through the years. Due to this demand, new storage solutions are needed. One promising media is the DNA as it provides numerous advantages, which includes the ability to store dense information while achieving long-term reliability. However, the question as to how the data can be retrieved from a DNA-based archive, still remains. In this paper, we aim to address this question by proposing a new storage solution that relies on bacterial nanonetworks properties. Our solution allows digitally-encoded DNA to be stored into motility-restricted bacteria, which compose an archival architecture of clusters, and to be later retrieved by engineered motile bacteria, whenever reading operations are needed. We conducted extensive simulations, in order to determine the reliability of data retrieval from motility-restricted storage clusters, placed spatially at different locations. Aiming to assess the feasibility of our solution, we have also conducted wet lab experiments that show how bacteria nanonetworks can effectively retrieve a simple message, such as "Hello World", by conjugation with motility-restricted bacteria, and finally mobilize towards a target point for delivery.

Ort, förlag, år, upplaga, sidor
IEEE, 2019.
Nyckelord [en]
DNA Encoding, Data Storage, Bacterial Nanonetworks, Molecular Communications
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:oru:diva-78931DOI: 10.1109/TETC.2019.2932685OAI: oai:DiVA.org:oru-78931DiVA, id: diva2:1384089
Tillgänglig från: 2020-01-09 Skapad: 2020-01-09 Senast uppdaterad: 2020-01-09Bibliografiskt granskad

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Giaretta, Alberto

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