Probabilistic GC-Constraints for Composite DNA

2026-10-07

AI Summary

This study addresses the significant rate penalty imposed by deterministic GC-content constraints in composite DNA storage by proposing an ε-probabilistic constraint framework. Methodologically, the authors integrate combinatorial type analysis with finite-state Markov chain modeling to derive capacity bounds for the composite DNA channel, and further design a multi-type enumerative encoder capable of achieving this capacity. By overcoming the limitations of conventional deterministic models, this work establishes capacity achievability under both global and local sliding-window constraints, thereby substantially enhancing the efficiency of DNA data storage systems.

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.

Problem

• composite DNA storage
• GC-content constraints
• probabilistic constraints
• capacity bounds

Innovation

• composite DNA storage
• probabilistic GC-constraints
• capacity bounds
• Markov chains
• enumerative encoder