Network Resilience Theory of Aging

Two major theories compete to explain the origin of aging. The first, proposed in 1959 by LeoSzilard, attributes aging to the accumulation of DNA damage. The second, articulated by Robin Holliday in the 1980s, emphasizes the role of epigenetic alterations. While they reveal potential molecular origins of aging, both theories leave important puzzles unresolved. First, mutation and epimutation burdens increase linearly with age. In contrast, aging phenotypes follow markedly nonlinear trajectories: mortality risk rises exponentially with age (Gompertz law), and the onset of age-related diseases increases nonlinearly with age. Second, key aging phenotypes such as cell-to-cell variability, inflammaging, and immunosenescence cannot be linked to specific genetic or epigenetic alterations. Instead, they appear as collective phenomena emerging fromthe cumulative impact of these alterations on cellular function.

In this talk, I will present a network resilience theory of aging to resolve these puzzles. Network resilience is formalized as the ability of a network to sustain its basic functions under changes in its topology (mutations) and dynamical variables (epigenetics). Our theory conceptualizes aging as the progressive loss of network resilience as cells approach a novel critical mutation-epigenetic line. We characterize the changes in cellular susceptibility over time, identifying twofundamental regimes of cellular stability. Away from the critical line (young age), cells remainstable despite the accumulation of mutations and epimutations. In contrast, cells approaching thecritical line (old age) exhibit a marked nonlinear increase in cellular susceptibility.

Using both GTEx data and numerical simulations, we formalize a fluctuation–dissipation relationlinking increased transcriptional noise to elevated cellular susceptibility with age. We alsopredict a nonlinear temporal delay in gene regulatory network (GRN) dynamics as the cell approaches the critical line, analogous to critical slowing down in statistical physics. Simulations of the mouse lung GRN reproduce this behavior and match experimental observations of delayed immune activation in aged mice.

Our framework also predicts cellular lifespan and rejuvenation potential across different tissues and links the accumulation of mutations and epimutations to aging phenotypes through resilience and critical phenomena, offering a unified perspective on the aging process.