2019
DOI: 10.1038/s41567-019-0460-5
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Bacteria display optimal transport near surfaces

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Cited by 93 publications
(64 citation statements)
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“…Similar running and lingering phases for cells near surface motion has also been reported in enterohaemorrhagic E. coli (EHEC) cells ( Perez Ipiña et al, 2019 ), where results suggested that by choosing the optimal transition rates, EHEC bacterial diffusivity is maximized and the surface exploration efficiency is greatly improved. In a future work, it will be interesting to apply similar analysis in V. cholerae .…”
Section: Discussionsupporting
confidence: 57%
“…Similar running and lingering phases for cells near surface motion has also been reported in enterohaemorrhagic E. coli (EHEC) cells ( Perez Ipiña et al, 2019 ), where results suggested that by choosing the optimal transition rates, EHEC bacterial diffusivity is maximized and the surface exploration efficiency is greatly improved. In a future work, it will be interesting to apply similar analysis in V. cholerae .…”
Section: Discussionsupporting
confidence: 57%
“…Interestingly, for E. coli cells, as a consequence of a hydrodynamic torque, forwardscattering events on the obstacles also lead the cells' trajectory to leave the surface. Along with the intermittent motion shown by some pathogenic strains of E. coli near a flat surface [48], this behaviour can thus offer a way to potentially reduce escape times when swimming near it and maximise near-surface diffusivity [23][24][25][26]. As our study focused on flat surfaces, promising future directions include testing the robustness of the identified forward-scattering mechanism on curved surfaces (where the surface curvature varies on a length scale compa-rable to the cells' persistence length), near interfaces in the presence of floating obstacles as well as in 3D porous structures.…”
Section: Discussionmentioning
confidence: 99%
“…Processing of data using artificial intelligence can automate the mapping of bacterial density along the root (Carbone et al, 2017). The ability to track bacteria has drastically improved since the early work of Shimshick and Hebert (1979), for example, observation of single bacterial cell and visualization of their attachment is now routinely achieved with modern microscopes (Duvernoy et al, 2018;Ipina et al, 2019). Mathematical frameworks will be essential to interpret such complex experimental data because they can establish links between attachment rates, root growth, bacterial proliferation, and the complex distribution of bacterial density along the root (Dupuy and Silk, 2016).…”
Section: Application Of Mathematical Framework For Estimation Of Attamentioning
confidence: 99%