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A Minimal Model of Metabolism-Based Chemotaxis

Overview of attention for article published in PLoS Computational Biology, December 2010
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Title
A Minimal Model of Metabolism-Based Chemotaxis
Published in
PLoS Computational Biology, December 2010
DOI 10.1371/journal.pcbi.1001004
Pubmed ID
Authors

Matthew D. Egbert, Xabier E. Barandiaran, Ezequiel A. Di Paolo

Abstract

Since the pioneering work by Julius Adler in the 1960's, bacterial chemotaxis has been predominantly studied as metabolism-independent. All available simulation models of bacterial chemotaxis endorse this assumption. Recent studies have shown, however, that many metabolism-dependent chemotactic patterns occur in bacteria. We hereby present the simplest artificial protocell model capable of performing metabolism-based chemotaxis. The model serves as a proof of concept to show how even the simplest metabolism can sustain chemotactic patterns of varying sophistication. It also reproduces a set of phenomena that have recently attracted attention on bacterial chemotaxis and provides insights about alternative mechanisms that could instantiate them. We conclude that relaxing the metabolism-independent assumption provides important theoretical advances, forces us to rethink some established pre-conceptions and may help us better understand unexplored and poorly understood aspects of bacterial chemotaxis.

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Mendeley readers

The data shown below were compiled from readership statistics for 110 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 4 4%
United States 4 4%
Germany 3 3%
Netherlands 1 <1%
Russia 1 <1%
India 1 <1%
Japan 1 <1%
Spain 1 <1%
Unknown 94 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 37 34%
Researcher 25 23%
Student > Master 8 7%
Student > Postgraduate 6 5%
Professor 5 5%
Other 17 15%
Unknown 12 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 32 29%
Biochemistry, Genetics and Molecular Biology 12 11%
Philosophy 11 10%
Physics and Astronomy 10 9%
Engineering 7 6%
Other 22 20%
Unknown 16 15%