Digital Data
Any binary file — documents, images, archives, scientific datasets, or arbitrary payloads. The engine treats input as opaque bytes with cryptographic integrity tracking.
Computational architecture
From Bits to Bases — and Back
DNA Vault models the full information path from arbitrary binary data through molecular symbolic representation, simulated sequencing noise, and bit-identical recovery. Every layer is implemented computationally — not in a wet lab.
Computational platform. Wet-lab validation not currently claimed.
Architecture overview
The DNA Vault Engine implements a ten-layer pipeline. Each stage is configurable, instrumented, and reproducible through seeded experiments.
DIGITAL DATA
↓
PREPROCESSING (hash · compress · encrypt)
↓
INFORMATION PACKAGING (chunk · address · metadata)
↓
DNA ENCODING (binary → A C G T)
↓
ERROR RESILIENCE (Reed-Solomon · fountain · redundancy)
↓
BIOLOGICAL CONSTRAINTS (GC balance · homopolymer limits)
↓
SEQUENCING SIMULATION (substitution · indel · dropout · coverage)
↓
RECONSTRUCTION (simulated reads → recovered sequences)
↓
VERIFICATION (SHA-256 comparison)
↓
ORIGINAL DATA (bit-identical recovery)
Ten computational layers
Preprocessing
- SHA-256 hashing for integrity verification
- Optional compression to reduce payload size
- Optional encryption with user-supplied passphrase
Information Packaging
- Configurable chunking for parallel recovery
- Addressing and block metadata
- Manifest generation for package inspection
DNA Encoding
Binary information is mapped to symbolic A/C/G/T sequences using configurable codecs. The output is a computational representation of molecular information — not synthesized DNA.
Error Resilience
- Reed-Solomon error correction
- Fountain coding (LT assembly)
- Configurable redundancy and overhead
- Recovery from partial or corrupted simulated reads
Biological Constraints
- GC-content balancing within configurable bounds
- Homopolymer run constraints
- Sequence validity checks for synthesis-ready design
Sequencing Simulation
- Substitution errors at configurable rates
- Insertion and deletion simulation
- Read dropout modeling
- Coverage depth variation
- Seeded randomness for reproducible experiments
Reconstruction
Simulated sequencing reads are assembled back into recovered DNA information using ECC, fountain decoding, and sync framing. This stage models what a physical sequencing pipeline would need to accomplish.
Verification
Recovered data is compared against the original SHA-256 hash. Verification status is reported as VERIFIED or FAILED — providing a clear pass/fail signal for every experiment.
Original Data
When recovery succeeds under the configured error model, the output file is bit-identical to the input. This is the computational proof that the encoding and recovery stack works for a given configuration.
Try the pipeline
Run the full encode → simulate → recover → verify workflow in DNA Vault Studio.