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PreprintCC BY 4.0arXiv

Probabilistic GC-Constraints for Composite DNA

  • Humeyra Bodur
  • Frederik Walter
  • Antonia Wachter-Zeh
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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.

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Cite

BibTeXRIS
BibTeX
@misc{bodur2026probabilistic,
  title = {{Probabilistic GC-Constraints for Composite DNA}},
  author = {Humeyra Bodur and Frederik Walter and Antonia Wachter-Zeh},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.09998},
  eprint = {2610.09998},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.09998},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Probabilistic GC-Constraints for Composite DNA
AU  - Humeyra Bodur
AU  - Frederik Walter
AU  - Antonia Wachter-Zeh
PY  - 2026
DA  - 2026/10/07
PB  - arXiv
DO  - 10.48550/arxiv.2610.09998
UR  - https://arxiv.org/abs/2610.09998
AB  - 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.
ER  -