Disentangling mechanisms of single-cell growth rate fluctuations

Single-cell growth rates fluctuate across time and generations, but identifying the biological origin of this variability is difficult because growth rate is usually inferred from noisy measurements of cell size or mass rather than measured directly. Here, we develop a simple and interpretable framework that separates measurement noise from biological sources of growth rate variability. We show that the autocovariance of inferred instantaneous growth rates carries robust signatures of the measurement process that are largely independent of the underlying biological growth dynamics, allowing the form and magnitude of measurement noise to be identified directly from data before introducing a model for the biological dynamics. We then use the autocovariance of accumulated growth to distinguish continuous within-cycle fluctuations, division-associated perturbations, and lineage-to-lineage variability. Applying this framework to bacterial and mammalian single-cell datasets, we find evidence for continuous growth rate noise in both systems. In E. coli, division-associated perturbations are large at birth compared with continuous fluctuations, but their contribution to growth accumulated over the full cell cycle is reduced by rapid relaxation. In contrast, mammalian cells show no division kicks, but stronger lineage-to-lineage variability. More broadly, our results provide a direct and interpretable route to identifying the biological origin of growth rate variability in noisy single-cell measurements.