Abstract
SummaryBacterial chemotaxis is a major testing ground for systems biology, including the role of fluctuations and individual variation. Individual bacteria vary in their tumbling frequency and adaptation time. Recently, large cell-cell variation was also discovered in chemotaxis gain, which determines the sensitivity of the tumbling rate to attractant gradients. Variation in gain is puzzling, because low gain impairs chemotactic velocity. Here, we provide a functional explanation for gain variation by establishing a formal analogy between chemotaxis and algorithms for sampling probability distributions. We show that temporal fluctuations in gain implement simulated tempering, which allows sampling of attractant distributions with many local peaks. Periods of high gain allow bacteria to detect and climb gradients quickly, and periods of low gain allow them to move to new peaks. Gain fluctuations thus allow bacteria to thrive in complex environments, and more generally they may play an important functional role for organism navigation.
Highlights
Bacteria navigate up and down gradients of chemicals in a process called chemotaxis
Individual bacteria vary in their tumbling frequency and adaptation time
We show that temporal fluctuations in gain implement simulated tempering, which allows sampling of attractant distributions with many local peaks
Summary
Bacteria navigate up and down gradients of chemicals in a process called chemotaxis. Bacteria climb gradients of chemical ligands by modulating their tumbling frequency, so that tumbling rate decreases when the bacteria move up gradients of chemoattractants and increases when they go down the gradient (Figure 1A). Chemotaxis is well-characterized in terms of its molecular signaling circuit. The circuit implements a nonlinear integral feedback loop, which brings tumbling rates precisely back to the baseline after changes in input (exact adaptation) and allows the bacteria to respond to relative changes in ligand input (fold-change detection [FCD]) across a wide dynamic range (Shoval et al, 2010; Lazova et al, 2011). Analogous navigation systems appear in eukaryotes (Polin et al, 2009; Arrieta et al, 2017) and simple animals (Larsch et al, 2015; Borba et al, 2020)
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