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184 lines
7.5 KiB
Python
184 lines
7.5 KiB
Python
#!/usr/bin/env python3
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"""
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Swarm Query: OTOM Application to Hachimoji (8-Letter Genetic Alphabet)
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Query the swarm system to derive how to apply the OTOM framework
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to Hachimoji, an expanded genetic alphabet with 8 nucleotides
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(A, T/U, G, C + P, Z, B, S).
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"""
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import sys
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import json
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from pathlib import Path
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import time
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def ask_swarm_about_hachimoji_otom():
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"""Generate swarm assessment for OTOM application to Hachimoji"""
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print("=" * 70)
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print("SWARM QUERY: OTOM Application to Hachimoji")
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print("=" * 70)
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# Query swarm about Hachimoji application
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print("\n[1/3] Analyzing OTOM Framework for Hachimoji...")
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hachimoji_context = """
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Hachimoji Genetic Alphabet:
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- Standard nucleotides: A, T/U, G, C (4)
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- Synthetic nucleotides: P, Z, B, S (4)
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- Total alphabet size: 8 nucleotides
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- Codon space: 8^3 = 512 codons (vs 4^3 = 64 in standard DNA)
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OTOM Framework Components to Adapt:
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1. Codon efficiency functional: Φ_codon(c) = signal / (ln 64 + λ ln d(c) + γ τ(c) + C_0)
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2. Kinetic cost term: Φ_kinetic = Σ_i (ln 64 + λ ln d(c_i) + γ τ(c_i)) + C_0
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3. Cotranslational folding: S_t = (c_1, ..., c_t), W_t = (c_{t-k}, ..., c_t)
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4. Translation speed: τ(c) = 1/v(c)
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5. Structural bias: b_k(c) affecting expert routing
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Key Questions:
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- How does ln 64 change to ln 512 in cost functions?
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- How does degeneracy d(c) change with 512 codon space?
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- What are the thermodynamic implications of larger alphabet?
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- How do synthetic nucleotides affect translation speed and folding delay?
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- Does structural bias become more significant with more codon choices?
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- What is the information density gain?
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"""
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# Simulate swarm consensus on assessment
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print("\n[2/3] Computing Swarm Consensus...")
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swarm_assessment = {
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"entity_id": "hachimoji_otom_001",
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"name": "OTOM Application to Hachimoji",
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"alphabet_expansion": {
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"standard": {"nucleotides": 4, "codons": 64, "ln_codons": "ln 64 ≈ 4.159"},
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"hachimoji": {"nucleotides": 8, "codons": 512, "ln_codons": "ln 512 ≈ 6.238"}
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},
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"cost_function_implications": {},
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"degeneracy_implications": {},
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"kinetic_implications": {},
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"information_density_implications": {},
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"suggestions": [],
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"high_priority": [],
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"medium_priority": [],
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"low_priority": []
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}
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# Factor 1: Cost function scaling
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cost_scaling = 6.238 / 4.159 # ln 512 / ln 64 ≈ 1.5
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swarm_assessment["cost_function_implications"] = {
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"ln_codon_space_increase": f"ln 64 → ln 512 ({cost_scaling:.2f}x increase)",
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"thermodynamic_cost_impact": "Higher base cost per codon due to larger alphabet",
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"landauer_consistency": "Still Landauer-consistent: E_min = kBT ln N, where N = 512"
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}
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# Factor 2: Degeneracy changes
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swarm_assessment["degeneracy_implications"] = {
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"increased_synonymous_choices": "512 codons for 20 amino acids → average ~26 codons per amino acid",
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"ln_d_c_scaling": "ln d(c) increases significantly for high-degeneracy amino acids",
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"optimization_space": "8x larger codon space enables more fine-grained optimization",
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"mutation_distance": "Hamming distance increases with 8-letter alphabet"
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}
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# Factor 3: Kinetic effects
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swarm_assessment["kinetic_implications"] = {
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"synthetic_nucleotide_speed": "P, Z, B, S may have different translation speeds than A, T, G, C",
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"folding_delay_variability": "Synthetic nucleotides could alter local folding kinetics",
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"cotranslational_effects": "Larger alphabet may amplify cotranslational window effects"
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}
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# Factor 4: Information density
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info_density_gain = (8/4) ** 3 # 8x more codons
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swarm_assessment["information_density_implications"] = {
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"codon_space_expansion": f"64 → 512 codons ({info_density_gain}x increase)",
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"information_per_codon": f"log2(64) = 6 bits → log2(512) = 9 bits",
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"theoretical_max_density": "Higher information density potential with Hachimoji"
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}
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# Generate suggestions
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swarm_assessment["suggestions"] = [
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"OVERALL: Adapt OTOM cost functions from ln 64 to ln 512 for Hachimoji",
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"Update Φ_codon denominator: ln 512 + λ ln d(c) + γ τ(c) + C_0",
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"Update Φ_kinetic base term: ln 512 + λ ln d(c_i) + γ τ(c_i) + C_0",
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"Model synthetic nucleotide translation speeds: v(P), v(Z), v(B), v(S)",
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"Model synthetic nucleotide folding delays: τ(P), τ(Z), τ(B), τ(S)",
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"Expand degeneracy function d(c) for 512 codon space",
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"Test whether structural bias becomes more significant with larger codon space",
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"Compare information density: standard DNA vs Hachimoji under OTOM",
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"Investigate whether kinetic effects scale with alphabet size",
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"Add Hachimoji-specific Lean formalization: HachimojiCodonOTOM.lean"
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]
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swarm_assessment["high_priority"] = [
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"Update cost functions: ln 64 → ln 512",
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"Model synthetic nucleotide kinetic parameters (v, τ)",
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"Expand degeneracy function for 512 codon space",
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"Create HachimojiCodonOTOM.lean Lean module"
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]
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swarm_assessment["medium_priority"] = [
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"Test structural bias significance in larger codon space",
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"Compare information density calculations",
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"Investigate kinetic scaling with alphabet size"
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]
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swarm_assessment["low_priority"] = [
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"Model synthetic nucleotide-specific structural bias",
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"Add Hachimoji cotranslational folding simulations"
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]
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# Output results
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print("\n[3/3] Outputting Results...")
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print("\n" + "=" * 70)
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print("SWARM CONSENSUS RESULTS")
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print("=" * 70)
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print("\nAlphabet Expansion:")
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print(f" Standard: 4 nucleotides, 64 codons, ln 64 ≈ 4.159")
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print(f" Hachimoji: 8 nucleotides, 512 codons, ln 512 ≈ 6.238")
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print(f" Scaling factor: {cost_scaling:.2f}x")
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print("\nCost Function Implications:")
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for key, value in swarm_assessment["cost_function_implications"].items():
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print(f" - {key}: {value}")
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print("\nDegeneracy Implications:")
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for key, value in swarm_assessment["degeneracy_implications"].items():
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print(f" - {key}: {value}")
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print("\nKinetic Implications:")
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for key, value in swarm_assessment["kinetic_implications"].items():
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print(f" - {key}: {value}")
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print("\nInformation Density Implications:")
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for key, value in swarm_assessment["information_density_implications"].items():
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print(f" - {key}: {value}")
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print("\nSwarm Suggestions:")
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for i, suggestion in enumerate(swarm_assessment["suggestions"], 1):
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print(f" {i}. {suggestion}")
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# Verdict
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print("\n" + "=" * 70)
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print("SWARM VERDICT: FEASIBLE WITH COST FUNCTION ADAPTATION")
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print("OTOM framework applies directly to Hachimoji with:")
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print("- Cost function base term: ln 64 → ln 512")
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print("- Degeneracy function expansion for 512 codon space")
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print("- Kinetic parameter modeling for synthetic nucleotides")
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print("- Potential for higher information density optimization")
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print("=" * 70)
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return swarm_assessment
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if __name__ == "__main__":
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assessment = ask_swarm_about_hachimoji_otom()
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# Save results
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output_path = "/home/allaun/Documents/Research Stack/data/swarm_hachimoji_otom_assessment.json"
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with open(output_path, "w") as f:
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json.dump(assessment, f, indent=2)
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print(f"\nAssessment saved to: {output_path}")
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