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Chemotaxis when Bacteria Remember: Drift versus Diffusion

Overview of attention for article published in PLoS Computational Biology, December 2011
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Title
Chemotaxis when Bacteria Remember: Drift versus Diffusion
Published in
PLoS Computational Biology, December 2011
DOI 10.1371/journal.pcbi.1002283
Pubmed ID
Authors

Sakuntala Chatterjee, Rava Azeredo da Silveira, Yariv Kafri

Abstract

Escherichia coli (E. coli) bacteria govern their trajectories by switching between running and tumbling modes as a function of the nutrient concentration they experienced in the past. At short time one observes a drift of the bacterial population, while at long time one observes accumulation in high-nutrient regions. Recent work has viewed chemotaxis as a compromise between drift toward favorable regions and accumulation in favorable regions. A number of earlier studies assume that a bacterium resets its memory at tumbles - a fact not borne out by experiment - and make use of approximate coarse-grained descriptions. Here, we revisit the problem of chemotaxis without resorting to any memory resets. We find that when bacteria respond to the environment in a non-adaptive manner, chemotaxis is generally dominated by diffusion, whereas when bacteria respond in an adaptive manner, chemotaxis is dominated by a bias in the motion. In the adaptive case, favorable drift occurs together with favorable accumulation. We derive our results from detailed simulations and a variety of analytical arguments. In particular, we introduce a new coarse-grained description of chemotaxis as biased diffusion, and we discuss the way it departs from older coarse-grained descriptions.

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Geographical breakdown

Country Count As %
United States 3 4%
United Kingdom 2 2%
France 1 1%
Spain 1 1%
Canada 1 1%
Unknown 77 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 23 27%
Student > Ph. D. Student 16 19%
Professor > Associate Professor 15 18%
Student > Master 8 9%
Student > Doctoral Student 5 6%
Other 13 15%
Unknown 5 6%
Readers by discipline Count As %
Physics and Astronomy 30 35%
Agricultural and Biological Sciences 23 27%
Engineering 6 7%
Biochemistry, Genetics and Molecular Biology 5 6%
Environmental Science 3 4%
Other 10 12%
Unknown 8 9%