DOI: https://doi.org/10.36347/sjpms.2026.v13i03.002

Pages: 94-106

The origin, structure, and universality of metabolic networks pose a major unresolved problem at the interface of biology, physics, and cosmology. Although prebiotic chemistry demonstrates plausible synthetic routes for life’s building blocks, the extreme combinatorial scale of metabolic configuration space renders the rapid emergence and global convergence of metabolism statistically implausible under unbiased evolutionary processes. This study presents a comprehensive quantitative analysis of 1,507 fully reconstructed metabolic networks spanning Bacteria, Archaea, and Eukarya, revealing a striking configuration space reduction of ~10⁶⁵-fold relative to theoretical biochemical possibilities. Network topology analysis identifies precisely two invariant universality classes, consistent across ecological, phylogenetic, and genomic variables. Convergence timescales (~10⁸–10⁹ years) are orders of magnitude shorter than random-walk predictions, indicating the presence of strong initial biasing factors. These empirical findings are quantitatively matched by predictions from Conformal Cyclic Cosmology (CCC), which proposes that topological information from prior cosmic aeons is inherited via photonic phase correlations encoded in squeezed quantum states. Casimir force calculations in squeezed vacuum predict a bias factor of 10⁶⁴–10⁶⁶, in precise agreement with observed metabolic configurational reduction, evolutionary modeling requirements, and statistical universality. Conventional explanations—vertical inheritance, horizontal gene transfer, biochemical constraints, convergent optimization, and neutral drift—fail to account for the precision, invariance, and degree of topological universality observed. While not constituting direct proof of trans-aeon information transfer, these results represent the first quantitative evidence that metabolic network organization may reflect cosmologically inherited attractor dynamics. This work provides a falsifiable interdisciplinary fr