Network-Based DNA Data Storage
Time: Thu 2026-09-17 09.00
Location: Hall Air & Fire, SciLifeLab, Tomtebodavägen 23, 171 65 Solna
Language: English
Subject area: Biotechnology
Doctoral student: Shuai Lang , Genteknologi
Opponent: Professor Robert Grass, ETH Swiss Federal Institute of Technology
Supervisor: Docent Ian T. Hoffecker, Genteknologi, Science for Life Laboratory, SciLifeLab; Professor Afshin Ahmadian, Genteknologi, Science for Life Laboratory, SciLifeLab
QC 2026-08-21
Abstract
DNA data storage has attracted growing interest as a potential solution to the challenges posed by rapidly increasing global data generation. It offers distinct advantages over magnetic storage media, including high information density, low energy requirements, and long-term stability. Established DNA writing strategies typically rely on content-dependent biochemical reactions, such as de novo synthesis, molecular modification, or biological recording. Despite their demonstrated utility, these approaches can be limited by reaction kinetics, cost, and operational complexity.
This thesis introduces write-by-partitioning, a network-based DNA data storage strategy in which information is written through physical partitioning rather than content-dependent biochemical reactions. The method employs a pre-formed DNA barcode network in which spatial relationships among barcodes are preserved as proximity-dependent associations. Data are encoded by dividing the network into segments and assigning a symbol to each segment based on the information being stored. For readout, the DNA barcode associations are sequenced to reconstruct the network and infer the original symbol sequence.
This thesis establishes a workflow for data encoding, network reconstruction, and decoding. The relationship between network properties and data storage performance is investigated. Because decoding depends on network reconstruction, we introduce a method for evaluating the spatial coherence of the reconstructed network. This work demonstrates the feasibility of network-based data storage and provides a fundamental framework for the development, evaluation, and optimization of future systems.
By replacing content-dependent biochemical writing with the physical partitioning of a pre-formed network, write-by-partitioning could provide a faster, more scalable, and more accessible approach to DNA data storage. Beyond data storage, the concepts and analytical methods developed could support broader applications of DNA barcode network.