Predictive Bioharmonics: An Algorithmic Framework for the Analysis and Regulation of Biological Aging Dynamics

Biological aging is typically attributed to time, genetics, or cumulative molecular damage. Here, aging is reframed as a dynamical process driven by the accumulation of internal instability (“drift”) beyond reversible regimes. Predictive Bioharmonics (PB) is introduced as a minimal algorithmic framework to detect, quantify, and regulate biological drift before irreversible transitions occur, using physiological regulation dynamics and targeted pre-instability adjustments. The paper presents a dynamical aging model and an empirical application of PB Test 2 (Drift & Recovery) on real human ECG-derived HRV data. Systems show distinct recovery behaviors after perturbation, with some rapidly returning to low-drift states while others exhibit prolonged instability. These results support aging-related decline as a function of recovery capacity rather than chronological time. No pharmaceuticals, invasive procedures, genetic modification, or advanced biotechnology are involved. All methods and pseudocode are provided for independent evaluation and replication.