Geylani Universal Stability Law: A Genome-Scale Information Stability Analysis Across 207,249 Human Transcripts

This manuscript introduces the GEYLAN˙IV2-BIO Universal Stability Law, a fully closed mathematical
framework designed to explain the persistence, collapse, and deformation of biological information under
energetic and entropic constraints. The formulation integrates three fundamental constants emerging
from large-scale transcriptome analysis: Biological RNAs maintain information only while remaining
above a universal stability threshold governed by phase geometry. Using GEYLAN˙IV2-BIO, a multiscale
framework unifying sequence composition, thermodynamic depth, and structural coherence, we analyze
207,249 human transcripts together with viral genomes. We identify two universal collapse constants,
0.30 and 0.83, and demonstrate that RNA instability emerges from geometric constraints rather than
organism-specific biology. A central phenomenon, described here as “cellular blindness ’ (cellular
blindness, informational threshold blindness, phase-instability blindness), occurs when cells fail to detect
sub-threshold instability, allowing collapsed RNAs to propagate functional error. Viral RNAs occupy
the extreme low-stability edge of the manifold, confirming that collapse is a fundamental informational
transition. These results establish GEYLAN˙IV2-BIO as a universal law of RNA stability.
• 0.30 — the universal collapse threshold, below which information-bearing RNA enters a destabilized
or degraded phase;
• 0.83 — the upper-phase stability bound, representing the empirical limit where coherent, high-
fidelity RNA structures retain functional integrity under perturbation;
• 50.5 — the information–energy density ratio S(g) = |MF E|
H
, found to be invariant across 207,249
human transcripts, viral RNA genomes, and cross-species samples.
Together, these constants define a universal stability surface governing RNA behaviour across healthy,
cancerous, and viral systems. GEYLAN˙IV2-BIO provides a unified phase-geometric formulation linking
free energy, entropy, and temporal perturbation operators, enabling predictive modelling of information
collapse in biological systems