Researchers are advancing a physics-inspired framework for studying aging, presented at a recent scientific conference exploring how physical laws and mathematical modeling can be applied to biological aging processes.

The emerging field, sometimes referred to as “gerophysics,” aims to simplify the complexity of aging by developing models that break down age-related decline into measurable and predictable components. These models are intended to improve understanding of aging patterns across species, support predictions about the effectiveness of anti-aging interventions, and clarify which biological processes most strongly influence aging outcomes.

A central focus of the conference was the application of physics-based equations to describe key aging trends, including exponentially increasing mortality with late-life slowdown, disease incidence patterns that rise and later decline, and the linear deterioration of physiological function over time. One proposed framework, the “saturating removal (SR) model,” describes aging as a balance between increasing damage accumulation and a removal process that becomes saturated under high damage conditions. Researchers reported that this model can reproduce all three observed aging patterns.

The SR model also provides a potential method for evaluating the impact of longevity interventions. According to this framework, changes in survival curves may indicate whether an intervention primarily reduces the production of biological damage or enhances the body’s ability to remove it. Interventions that reduce damage production are theorized to extend both lifespan and the period of healthy life, while those that improve damage removal may compress late-life morbidity and steepen survival curves.

In addition to this model, researchers are developing multi-scale computational tools that integrate nutrient signaling, metabolism, damage accumulation, and cellular growth. These models aim to simulate how metabolic states shift with age, including changes in timing and patterns of cellular activity. By accounting for these “metabolic phases,” scientists hope to identify optimal windows for intervention and better predict how treatments may influence aging depending on when they are applied.

Researchers emphasize that mathematical and computational approaches can help uncover underlying simplicity within the complex biological processes of aging, offering new pathways for in silico experimentation and the design of targeted longevity strategies.