4.5 Article

Steady-state running rate sets the speed and accuracy of accumulation of swimming bacteria

Journal

BIOPHYSICAL JOURNAL
Volume 121, Issue 18, Pages 3435-3444

Publisher

CELL PRESS
DOI: 10.1016/j.bpj.2022.08.012

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Funding

  1. Office of Naval Research Global and Defense Advanced Research Projects Agency [12420502]
  2. BrisSynBio, BBSRC/EPSRC Advanced Synthetic Biology Research Centre [BB/L01386X/1]
  3. Grand Challenge NetworkPlus in Emergence and Physics Far From Equilibrium
  4. Engineering and Physical Sciences Research Council (EPSRC) [EP/P007198/1]

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This study establishes a link between the measured steady-state switching rates of the flagellar rotary system and the directional changes of individual swimming bacteria in a gradient, using a probabilistic model. The complex response of a bacterial population to the gradient suggests that both the average speed and distribution width of the response should be considered when optimizing the overall population's performance in complex environments.
We study the chemotaxis of a population of genetically identical swimming bacteria undergoing run and tumble dynamics driven by stochastic switching between clockwise and counterclockwise rotation of the flagellar rotary system, where the steady-state rate of the switching changes in different environments. Understanding chemotaxis quantitatively requires that one links the measured steady-state switching rates of the rotary system, as well as the directional changes of individual swimming bacteria in a gradient of chemoattractant/repellant, to the efficiency of a population of bacteria in moving up/down the gradient. Here we achieve this by using a probabilistic model, parametrized with our experimental data, and show that the response of a population to the gradient is complex. We find the changes to the steady-state switching rate in the absence of gradients affect the average speed of the swimming bacterial population response as well as the width of the distribution. Both must be taken into account when optimizing the overall response of the population in complex environments.

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