THP Genome: Harmonic Coherence Analysis from DNA to Neural Networks

This paper introduces THP Genome, a novel analytical framework that applies harmonic coherence principles to genomic systems, revealing unexpected structural parallels between DNA codon usage, neural network dynamics, and artificial intelligence behavior. Building upon the Harmonic Coherence Principle—the observation that stable resonant systems across multiple scales converge toward a common harmonic point in base-60/base-12/base-5 deviation space—we demonstrate that the 60 functional codons of the universal genetic code exhibit the same structural stability metrics as planetary orbital resonances, molecular vibrations, and cognitive regimes. Through extensive cross-platform validation involving multiple AI systems (ChatGPT, Gemini, Grok, DeepSeek, and Claude), we establish that harmonic coherence metrics—particularly the continuity of phase relationships in modular arithmetic (Mod 3 and Mod 5)—serve as universal indicators of system health across biological, neural, and computational domains. We identify a critical threshold (C₃ < 0.55) below which systems universally exhibit structural collapse, whether manifesting as genomic instability, neural degeneration, or AI hallucination. The framework introduces three fundamental laws governing structural stability: the Law of Phase Primacy (Mod 3), the Law of Redundancy Protection (Mod 5), and the Law of Ontological Hysteresis. These laws provide a unified theoretical foundation for understanding stability, degradation, and collapse across information-processing systems at all scales. We present practical applications including viral adaptation analysis, tumor progression tracking, neurodegenerative disease monitoring, and AI safety governance, demonstrating that THP Genome represents not merely a bioinformatics tool but a general theory of structural integrity in complex systems.