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.

Microbial growth and division are fundamental processes relevant to many areas of life science. Of particular interest are homeostasis mechanisms, which buffer growth and division from accumulating random fluctuations. This classic problem has seen recent advance in both theory and experiments, but still much remains unclear and debatable. Because there are many coupled processes inside the cell, it is not a-priori clear which variables are under regulation and which are stabilize by their coupling to regulated variables. Here, we address this question by distentangling homeostatic set-points -- estimated as temporal averages across individual lineages -- from fluctuations around them. Applying variance decomposition to individually trapped bacteria (mother machine data), we find that phenotypic variables estimated from cell-size measurements (inter-division time, growth rate, added size and more), exhibit a range of different behaviors. Some have flexible set-points that vary significantly between lineages - "sloppy" variables, while others are tightly fixed - "stiff" variables. Analyzing pairs of trapped lineages (sisters machine data), reveals that the primary source of sloppiness is a high sensitivity to the trap environment, while lineage-specific factors contribute only a small fraction. We find strong long-term correlations between sloppy set-points, and identify a control manifold in the space of growth and division variables. Cell size is a sloppy variable, whose set-point is uncoupled to growth variables; such correlations appear only on the short, single-cycle timescale. We discuss these results in light of recent models of cell size control, and point to new avenues of research.