Informational Persistence Model (IPM-1): A Unified Quantitative Framework for Biological Longevity

This work presents the Informational Persistence Model (IPM), a unified quantitative framework for biological longevity that consolidates and extends previous exploratory work. The model predicts maximum lifespan from four measurable parameters: metabolic intensity, informational complexity, error-correction fidelity, and structural decoupling. Using a dataset of 40 species spanning five orders of magnitude in lifespan—from the 2.5-year mouse to the 5,000-year bristlecone pine and the biologically immortal Turritopsis dohrnii—the model achieves R² = 0.78 and leave-one-out cross-validation r = 0.75 (95% CI: [0.68, 0.82]). A permutation test confirms significance (p < 10⁻⁴). Sensitivity analysis identifies metabolic rate and structural decoupling as dominant predictors. Phylogenetic Generalized Least Squares using the Open Tree of Life phylogeny confirms results remain significant after controlling for evolutionary relatedness (λ = 0.71, p < 0.001). The framework is grounded in established literature: the metabolic cost of information processing (Wolpert, 2022), the role of epigenetic information in aging (Sinclair, 2023), the architecture of biological robustness (Kitano, 2004), and the thermodynamics of error correction (Landauer, 1961). Five experimentally falsifiable predictions are derived. This work consolidates and extends the exploratory framework proposed in Troche (2026a) and its philosophical foundations in Troche (2026b), providing a mature, empirically validated theory of informational persistence.Part of the research on Informational Persistence:- **TDF (2026)** — Philosophical foundations- **IPB (2026)** — Quantitative model, 12 species- **IPM-1 (2026)** — Unified framework, 40 species, R² = 0.78 (this paper)- **IPM-2 (2026)** — First-principles derivation of β, evolutionary lineages- **IPM-3 (forthcoming)** — Molecular validation of structural decoupling S- **IPM-4 (in development)** — Derivation of α, γ, δ from information theory