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BioSight encodes mathematical equations as 30-base hachimoji DNA sequences for Adleman/Lipton-style DNA computing. 4-layer Φ mapping: Layer 1: F(E) — byte-class histogram on Δ₇ Layer 3: τ(E) + δ(E) — parse tree structure Layer 4: 6 consistency rules → allele-specific PCR pass/fail Independent phi/ modules: charclass, ast_parse, consistency, embed, output Build: python3 -m py_compile — all modules clean
72 lines
2.8 KiB
Markdown
72 lines
2.8 KiB
Markdown
# BioSight — Equation-to-DNA Φ Encoding Pipeline
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**Status:** Active, v0.1.0
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BioSight encodes mathematical equations as hachimoji DNA sequences
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for Adleman/Lipton-style DNA computing. The Φ mapping has 4 layers:
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1. **F(E)** — byte-class histogram over Δ₇ (8 character classes)
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2. *Phase alphabet — implicit in the mapping from Δ₇ to bases*
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3. **τ(E) + δ(E)** — parse tree structure (node types + child ordering)
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4. **Consistency** — 6 rules → allele-specific PCR G/T pass/fail
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## Ground Rules
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- **Decision logic is in Lean** — BioSight shims are Python I/O only.
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If any logic here starts making admissibility or routing decisions,
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port it to SilverSight's `Semantics.RRC.*` modules.
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- **No floats in compute paths** — `phi.charclass` and `phi.embed`
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use integer arithmetic for all core encoding. Only `phi.output`
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uses math.acos (for Fisher distance in Adleman graph building).
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- **Pure functions** — every phi module is a pure function of
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its equation string input. No state, no I/O, no side effects.
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- **Reproducibility** — two invocations on the same equation must
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produce identical DNA sequences (deterministic byte-class and
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AST order).
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## Core Modules
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| Module | Layer | Responsibility |
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|--------|-------|----------------|
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| `phi.charclass` | 1 | 8-class byte histogram → F(E) ∈ Δ₇ |
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| `phi.ast_parse` | 3 | Python AST → τ(E) + δ(E) distributions |
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| `phi.consistency` | 4 | 6 rules → ADMIT/QUARANTINE |
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| `phi.embed` | Core | (F, τ, δ) → 30-base hachimoji DNA |
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| `phi.output` | Formats | FASTQ, Adleman graph, PCR protocol |
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## DNA Layout (30 bases)
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```
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bases 0-7: F(E) — byte-class frequencies on Δ₇
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bases 8-15: τ(E) — parse tree node-type frequencies
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bases 16-23: δ(E) — child-ordering frequencies
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bases 24-29: Layer 4 consistency (G=pass, T=fail)
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```
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## External Integration
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- **BioComputing** (`https://github.com/Abesuden/BioComputing`, MIT):
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Our `phi.output.to_adleman_graph()` builds vertices+edges that
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feed directly into `hampath.connectNodes()` and `sattv.createNodes()`.
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Clone it alongside BioSight for wet-lab DNA sequence generation.
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## Key References
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- Adleman (1994) *Science* 266:1021–1024 — 7-vertex Hamiltonian path
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- Lipton (1995) *Science* 268:542–545 — SAT generalization
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- Hoshika et al. (2019) *Science* 363:884–887 — 8-base hachimoji alphabet
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- Newton et al. (1989) *Nucleic Acids Res* 17:2503 — allele-specific PCR
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## Build & Test
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```bash
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cd python
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python3 -m py_compile phi/*.py equation_dna_encoder.py
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python3 -c "import phi; print(phi.encode_phi('x + 1 = 2'))"
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```
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## SilverSight Relationship
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BioSight is the DNA-computing branch of the Research Stack ecosystem.
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SilverSight (`/home/allaun/SilverSight`) owns all formal Lean logic.
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If BioSight needs a new theorem or receipt, it requests it from SilverSight.
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