A Control-Theoretic Model of Damage Accumulation and Boundedness in Biological Aging

TL;DR

This paper introduces a control-theoretic model of biological aging, distinguishing damage types and identifying conditions for bounded aging, with implications for interventions and biomarker selection.

Contribution

It develops a novel control-theoretic framework for aging, classifying damage into regulatable and information-limited types, and establishes conditions for bounded organismal aging.

Findings

Information-limited damage dominates aging rate

Physiological repair has limited impact beyond saturation

Intervention strategies can be guided by control boundary analysis

Abstract

Aging interventions frequently improve function and healthspan without arresting long-term deterioration, indicating that existing frameworks do not fully specify the control conditions required for bounded organismal aging. A compact control-theoretic formulation is developed in which total organismal burden is decomposed into two lesion classes with distinct controllability properties: regulatable damage, whose accumulation and clearance are modulated by endogenous systemic repair, and information-limited damage, whose detection or correction is inaccessible to physiological control. Under mild dynamical assumptions, a sufficiency theorem is established: sustained boundedness of total damage is achieved if and only if endogenous repair persistently exceeds production of regulatable damage and information-limited damage is actively bounded or removed by engineered interventions. Deterministic phase diagrams identify distinct bounded, drifting, and runaway regimes separated by a nontrivial control boundary. A global Latin-hypercube sensitivity analysis with partial rank correlations shows that production of information-limited lesions dominates the asymptotic aging rate, whereas increases in physiological repair capacity have weak marginal influence beyond saturation. Stochastic extensions reveal threshold and sequencing effects relevant to oncogenic risk. The framework yields testable predictions and operational guidance for intervention ordering, biomarker selection, and experimental design in aging research. All conclusions are statements about the dynamical model defined here; biological translation requires empirical identification of observables corresponding to the model variables.