ARC: A Computational Ontology for Semi-Quantitative Scoring of Regenerative Capacity Across Vertebrate and Invertebrate Systems

Regeneration in adult mammals is constrained not by absence of regenerative information, but by coordinated epigenetic, positional, neural, microenvironmental, and termination locks. The Architecture of Regenerative Control (ARC) formalizes regenerative capacity as a logistic function of six mechanistic layers — Permission (P), Coordinates (C), Synchronization (S), Sandbox (B), Termination (T), and Metabolism (M) — with a statistically validated S×B interaction term (ΔAIC = −10.6). Trained on 16 biological systems across 6 species, the model achieves 100% cross-validated accuracy. Independent out-of-sample validation on zebrafish heart yields Φ = 0.871. Preliminary external validation on 10 systems spanning 5 phyla confirms predictive generalization. ARC provides a falsifiable, quantitative framework for predicting regenerative outcomes and designing multi-layer intervention protocols.