A Minimal Model Explains Aging Regimes and Guides Intervention Strategies
Fedichev, P.; Gruber, J.
Show abstract
Aging varies widely across species yet converges on uni-versal laws such as Gompertzian mortality. We propose a minimal framework that reduces this complexity to three macroscopic variables: the leading stress response (z0), cumulative entropic damage (Z), and regulatory noise strength (D0). The model predicts two regimes. In unstable species such as flies and mice, intrinsic instability drives exponential divergence of biomarkers and mortality. In stable species such as humans, linear damage accumulation steadily erodes the leading stress response, producing a hyperbolic trajectory toward a finite maximum lifespan. The framework reproduces survival curves and methylation dynamics across taxa and organizes intervention strategies into three levels: modulating stress responses, reducing noise, and slowing entropic damage, offering a roadmap for extending human healthspan and lifespan.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- The Synergy of Damage Repair and Retention Promotes Rejuvenation and Prolongs Healthy Lifespans in Cell Lineages 94%
- Multi-scale model suggests the trade-off between protein and ATP demand as a driver of metabolic changes during yeast replicative ageing 94%
- Dysregulation of excitatory neural firing replicates physiological and functional changes in aging visual cortex 94%
Similar papers in this journal
Similar papers in this journal
- Longitudinal analysis of blood markers reveals progressive loss of resilience and predicts ultimate limit of human lifespan 95%
- Identification of a blood test-based biomarker of aging through deep learning of aging trajectories in large phenotypic datasets of mice 95%
- Robust coexistence in competitive ecological communities 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.