In-DNA computing turns the archive itself into an associative memory. Computation is performed directly on stored molecules, so large collections can be searched and filtered in place before any bulk sequencing. The result is massive parallelism with very low energy cost and dramatic reductions in read volume.Documentation Index
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Data Model
The archive is written as identifiers DNA molecules assembled by choosing one component from each of several layers and ligating them in order.Combinatorial address space
The Cartesian product of layers creates an address space with a well-defined
rank for every identifier. Data are mapped to codewords with uniform weight.
Trie organization
A subset of layers is reserved as a key so identifiers can be addressed
chemically by their components. This induces a trie over the identifier
space, enabling dictionary-like operations in the wet lab.
Instruction Set
Two primitive operations form the core of in-DNA compute:Select
Takes a key defined by a sequence of components across a contiguous set of
layers. Returns the subset of identifiers that contain that key. Implemented
via recursive selective PCR after m steps only identifiers containing the
full key remain enriched.
Quotient
Takes a layer index q. Truncates all enriched molecules at layer q so
that identifiers differing only in deeper layers collapse to the same
sub-identifier. Signals from many descendants in the trie are summed into
their common ancestor.
Exact Search Workflow
Derive bit-position keys
Obtain the codeword for the query object. With weight w, it defines w
distinct bit-position keys.
Apply quotient
Quotient at the layer adjacent to the bit-position key. Each true occurrence
contributes one strong sub-identifier; non-targets contribute weak residual
fragments.
Similarity Search Workflow
Real-valued embeddings are mapped to uniform-weight codewords so that similar vectors share more one-bit positions.Encode embeddings
Choose a small set of reference points in embedding space. Assign a one to
the k nearest references for each item. Two unrelated items share w²/L
bits on average; related items share significantly more.
Apply quotient and measure signal
Signal after quotient is proportional to shared one-bit positions directly
correlating with semantic similarity.
Parallelism & Complexity
Select and quotient run in single-instruction, multiple-data fashion
across the entire library. The number of wet-lab steps for a select equals the
number of layers in the key it does not depend on total stored items.
Total reads can drop by two to three orders of magnitude relative to a
full scan, while maintaining strong signal separation between targets and
non-targets.
Programmability Beyond Search
The same library supports additional molecular instructions:| Operation | Mechanism | Use |
|---|---|---|
| AND / OR logic | Controlled hybridization of single-stranded identifiers | Set intersection and union |
| Counting | Concentration-based representation | Majority voting |
| Bit rewrite | Selective enrichment + controlled ligation or degradation | In-place update |
Design Parameters
| Parameter | Effect |
|---|---|
| Codeword length L | Higher L improves selectivity and similarity resolution; increases identifiers to assemble |
| Codeword weight w | Lower w reduces collision rate; lower quotient signal |
| Layer allocation | Determines which attributes are searchable keys and traversal depth |
| Primer & overhang design | Controls enrichment efficiency and signal-to-noise ratio |