Probabilistic gc content constraints improve dna data storage efficiency
Probabilistic GC-Constraints for Composite DNA
Information Theory
Summary
Storing data in DNA molecules requires controlling the amount of G and C bases to ensure stability and accuracy. Previous methods strictly limited this GC content, which reduced data storage efficiency. The authors introduce a new way to allow a small chance that GC content falls outside the target range, which better matches the natural variability of composite DNA strands. They also provide mathematical limits on how much data can be stored under these relaxed rules and design an encoding method that achieves these limits.
What this means in practice
- •For dna storage engineers: Implement encoding schemes that allow probabilistic GC content variations to increase data density in DNA-based storage.
- •For biotech developers: Design DNA synthesis processes that tolerate controlled GC-content fluctuations improving overall strand reliability.
Authors
Humeyra Bodur, Frederik Walter, Antonia Wachter-Zeh
Abstract
This paper addresses the challenge of encoding biochemical GC-content constraints in composite DNA-based data storage. Previous deterministic models impose high rate penalties by avoiding any possibility for a strand to fall outside the allowed range. To account for the stochastic nature of composite DNA, we introduce an $ε$-probabilistic constraint framework, and derive capacity bounds for global constraints using composition types and for local sliding-window constraints via finite-state Markov chains. Furthermore, we propose a capacity-achieving multi-type enumerative encoder.