| dc.description.abstract | DNA has emerged as a promising medium for long-term archival data storage due to its
exceptional information density, durability, and passive energy requirements. However, practical
deployment remains constrained by three fundamental challenges: stochastic synthesis bias,
chemical degradation over time, and limited scalability of random access. This thesis proposes a
Hybrid Error-Resilient DNA Storage Framework that integrates computational, biochemical,
and architectural solutions to address these limitations holistically.
First, a bias-aware adaptive coding scheme is introduced, which models synthesis and PCR
dropout as probabilistic processes and dynamically assigns logical redundancy based on predicted
sequence fragility. Unlike conventional uniform redundancy approaches, the proposed method
reduces sequencing coverage requirements while preserving decoding reliability. Second, a
Markov-chain-based enzymatic repair model is developed to simulate long-term molecular
decay and restoration using a multi-enzyme repair cocktail consisting of APE1, Bst polymerase,
and Taq ligase. The model demonstrates a significant extension of data recoverability horizons
under accelerated aging conditions. Third, the thesis evaluates thermoresponsive microcapsule
based random access, enabling repeated, low-bias retrieval through thermoconfined PCR while
preventing destructive consumption of the archival pool.
Comprehensive simulations indicate a reduction of sequencing coverage by over 70% and an
effective extension of archive longevity by nearly threefold compared to conventional DNA
storage pipelines. Additionally, a sustainability analysis highlights substantial reductions in energy
consumption, carbon emissions, and electronic waste relative to magnetic tape and hard disk-based
archival systems. The results establish DNA storage as a viable candidate for future ultra-long
term, sustainable digital preservation. | en_US |
