Gerophysics: Physics-Based Descriptions of Aging
Researchers here report on a recent conference focused on bringing the viewpoint of the physics community to the study of aging. This is largely a matter of building models of aging for a variety of purposes, such as breaking down aging into different conceptual components to make more sense of the observed, somewhat confusing range of outcomes in different species, better predicting which classes of intervention may be most effective in the treatment of aging, or better understanding which aspects of cellular biochemistry are more versus less important in aging.
The first session focused on applying physics laws to understand the aging process, work on how to model three aging patterns - exponentially rising death with late slowdown, exponentially rising disease incidence with late decline, and linear decline of physiological function with age. The saturating removal (SR) model equation has damage production that rises with time (due to accumulating damage producing units) and damage removal process that saturates at high damage plus noise. This equation can explain all three aging patterns.
Furthermore, the model can analyze the shape of the survival curve to tell if geroprotective interventions act on damage production (survival scaling) or damage removal/threshold (survival steepening). Based on the SR model, longevity interventions act by reducing the damage production rate and/or increasing the damage removal rate, both of which are inherently age-dependent processes in this model. He theorized that interventions that prevent damage production will lead to scaling of both lifespan and sickspan, which leads to scaling of the lifespan curves, while interventions that increase damage removal rate can compress the sickspan, leading to the steepening of the survival curve.
Mathematical models can "find simplicity in complexity" when studying the highly intricate process of aging. By integrating diverse data and enabling in silico experiments, such models offer powerful tools to understand aging mechanisms. Researchers presented work on developing new computational tools such as the multi-scale aging model, which integrates nutrient signalling, metabolism, damage accumulation, and growth, enabling exploration of metabolic changes as the cell ages and becomes exposed to stress and protein damage. As the cell gets older, the timing and pattern of normal metabolic states change, so-called metabolic phases. By accounting for these altered patterns, the model can help guide metabolic interventions for lifespan control, by identifying points where interventions might have different effects depending on when they occur in the cell's life.