The generational scalability of single-cell replicative aging

elicit proportional scaling of survival curve dynamics. The scalable nature of replicative lifespan distributions indicates that replicative aging is governed by a global state variable that determines cell survival by integrating effects from different risk factors. We also show that the Weibull survival function, a scale-invariant mathematical form, is capable of accurately predicting experimental survival distributions. We demonstrate that a drift-diffusion model of aging state with random challenge arrival effectively captures mortality risk. Measuring single-cell generation durations during aging, we uncover power-law dynamics with strain-specific speeds of increase in generation durations. Our application of quantitative modeling approaches to high-precision replicative aging data offers novel insights into aging dynamics and lifespan determinants in single cells.