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allaun 41fcbc3daa feat(init): initial BioSight commit — equation-to-DNA Φ encoding

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

2026-06-23 18:27:35 -05:00

2.8 KiB

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BioSight — Equation-to-DNA Φ Encoding Pipeline

Status: Active, v0.1.0

BioSight encodes mathematical equations as hachimoji DNA sequences for Adleman/Lipton-style DNA computing. The Φ mapping has 4 layers:

F(E) — byte-class histogram over Δ₇ (8 character classes)
Phase alphabet — implicit in the mapping from Δ₇ to bases
τ(E) + δ(E) — parse tree structure (node types + child ordering)
Consistency — 6 rules → allele-specific PCR G/T pass/fail

Ground Rules

Decision logic is in Lean — BioSight shims are Python I/O only. If any logic here starts making admissibility or routing decisions, port it to SilverSight's Semantics.RRC.* modules.
No floats in compute paths — phi.charclass and phi.embed use integer arithmetic for all core encoding. Only phi.output uses math.acos (for Fisher distance in Adleman graph building).
Pure functions — every phi module is a pure function of its equation string input. No state, no I/O, no side effects.
Reproducibility — two invocations on the same equation must produce identical DNA sequences (deterministic byte-class and AST order).

Core Modules

Module	Layer	Responsibility
`phi.charclass`	1	8-class byte histogram → F(E) ∈ Δ₇
`phi.ast_parse`	3	Python AST → τ(E) + δ(E) distributions
`phi.consistency`	4	6 rules → ADMIT/QUARANTINE
`phi.embed`	Core	(F, τ, δ) → 30-base hachimoji DNA
`phi.output`	Formats	FASTQ, Adleman graph, PCR protocol

DNA Layout (30 bases)

bases  0-7:   F(E) — byte-class frequencies on Δ₇
bases  8-15:  τ(E) — parse tree node-type frequencies
bases 16-23:  δ(E) — child-ordering frequencies
bases 24-29:  Layer 4 consistency (G=pass, T=fail)

External Integration

BioComputing (https://github.com/Abesuden/BioComputing, MIT): Our phi.output.to_adleman_graph() builds vertices+edges that feed directly into hampath.connectNodes() and sattv.createNodes(). Clone it alongside BioSight for wet-lab DNA sequence generation.

Key References

Adleman (1994) Science 266:1021–1024 — 7-vertex Hamiltonian path
Lipton (1995) Science 268:542–545 — SAT generalization
Hoshika et al. (2019) Science 363:884–887 — 8-base hachimoji alphabet
Newton et al. (1989) Nucleic Acids Res 17:2503 — allele-specific PCR

Build & Test

cd python
python3 -m py_compile phi/*.py equation_dna_encoder.py
python3 -c "import phi; print(phi.encode_phi('x + 1 = 2'))"

SilverSight Relationship

BioSight is the DNA-computing branch of the Research Stack ecosystem. SilverSight (/home/allaun/SilverSight) owns all formal Lean logic. If BioSight needs a new theorem or receipt, it requests it from SilverSight.

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