Multiple timescales in bacterial growth homeostasis

Abstract

Summary In balanced exponential growth, bacteria maintain many properties statistically stable for a long time: cell size, cell cycle time, and more. As these are strongly coupled variables, it is not a-priori obvious which are directly regulated and which are stabilized through interactions. Here, we address this problem by separating timescales in bacterial single-cell dynamics. Disentangling homeostatic set points from fluctuations around them reveals that some variables, such as growth-rate, cell size and cycle time, are “sloppy” with highly volatile set points. Quantifying the relative contribution of environmental and internal sources, we find that sloppiness is primarily driven by the environment. Other variables such as fold-change define “stiff” combinations of coupled variables with robust set points. These results are manifested geometrically as a control manifold in the space of variables: set points span a wide range of values within the manifold, whereas out-of-manifold deviations are constrained. Our work offers a generalizable data-driven approach for identifying control variables in a multidimensional system.

Multigenerational memory in bacterial size control

“…Our model recovers not only the correct cross-correlation timescale but also the observed wide range in homeostasis parameters. The results suggest that control parameters depend sensitively on the environment and that the environment varies considerably within a multi-channel microfluidic device, as has been demonstrated previously [19,24].…”

“…1 and 2. Our fundamental premise is that the environment plays a defining role in setting the size control parameters, and that different channels within a microfluidic device are subject to different environments [23,24]. Because obtaining correlation functions from data often requires averaging over many channels to obtain sufficient statistics [8,15,16], we hypothesize that the averaging process obscures long timescales in some correlation functions (A n ) but not others (P n ).…”

“…3a) [8,15]. To explain this heterogeneity, we hypothesize that the size control parameters are a function of the environment, and that different channels have different environments [24]. To investigate this hypothesis, we make the simplest modification to the model in Eq.…”

Universality of phenotypic distributions in bacteria

“…Many groups have utilized mother machine microfluidic devices to monitor single cells as they grow and divide over multiple cycles (11, 18–20, 23, 28, 29, 36, 37). In these experiments typically cell size, and sometimes fluorescently labeled proteins, are measured.…”

“…Plot of χ α vs χ c obtained from several experimental data sets (11, 18–20, 28, 29, 36, 37), which span two different regimes, χ α > χ c and χ α < χ c . The distribution of cell division times are more skewed and deviate more from the Gaussian (dashed lines) when χ α > χ c (histograms on the left); this deviation is smaller when χ α < χ c (histograms on the right).…”