Frequency-Dependent<i>Escherichia coli</i>Chemotaxis Behavior

Zhu, Xuejun; Si, Guangwei; Deng, Nianpei; Ouyang, Qi; Wu, Tailin; He, Zhuoran; Jiang, Lili; Luo, Chunxiong; Tu, Yuhai

doi:10.1103/physrevlett.108.128101

Cited by 58 publications

(68 citation statements)

References 22 publications

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“…For 8 µm/s > υ w > 2 µm/s , υ d starts to deviate from υ w but still increases with υ w albeit sub-linearly. This slowing down is likely caused by the effect of a finite adaptation time of E. coli in tracking/computing the attractant gradient [15, 16, 19]. However, the most surprising observation is that υ d decreases sharply with υ w instead of saturating to a constant value when υ w ≥ 8 µm/s .…”

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confidence: 99%

Barrier Crossing in Escherichia coli Chemotaxis

Cai

Zhang

et al. 2017

Phys. Rev. Lett.

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Cells live in time varying environments with multiple desirable locations separated by unfavorable regions. To study cell navigation in spatiotemporally varying environments, we developed a microfluidic “race-track” device to create traveling attractant waves with multiple peaks and a tunable wave speed (υw). We found that while the population-averaged chemotaxis drift speed (υd) increases with υw for low υw, it decreases sharply for high υw. Our single cell measurements revealed that this reversed dependence of υd on υw is caused by a “barrier-crossing” phenomenon, where a cell hops backwards from one peak attractant location to the peak behind by crossing an unfavorable (barrier) region with low attractant concentrations. The barrier-crossing process is enabled by the cell’s random motion, which acts as temperature in thermally activated processes. Our simulation results and theoretical analysis showed that the backward barrier is lowered by υw and the backward drift speed depends exponentially on υw, leading to the observed sharp drop in υd for high υw. The barrier-crossing effect is further confirmed in double well experiments.

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confidence: 99%

Barrier Crossing in Escherichia coli Chemotaxis

Cai

Zhang

et al. 2017

Phys. Rev. Lett.

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“…The combination of the standard model for the signaling pathway and a quantitative model for the flagellar motor response has yielded significant insights and quantitative predictions on how motile cells behave in spatial-temporally varying environments. These predictions have been tested in microfluidic experiments with stationay, stand-wave, and traveling wave gradients (24, 25, 26). …”

Section: Adaptation In Bacterial Chemotaxismentioning

confidence: 99%

Adaptation in Living Systems

Rappel

2018

Annu. Rev. Condens. Matter Phys.

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Adaptation refers to the biological phenomenon where living systems change their internal states in response to changes in their environments in order to maintain certain key functions critical for their survival and fitness. Adaptation is one of the most ubiquitous and arguably one of the most fundamental properties of living systems. It occurs throughout all biological scales, from adaptation of populations of species over evolutionary time to adaptation of a single cell to different environmental stresses during its life span. In this article, we review some of the recent progress made in understanding molecular mechanisms of cellular level adaptation. We take the minimalist (or the physicist) approach and study the simplest systems that exhibit generic adaptive behaviors. We focus on understanding the basic biochemical interaction networks in living matter that are responsible for adaptation dynamics. By combining theoretical modeling with quantitative experimentation, we demonstrate universal features in adaptation as well as important differences in different cellular systems, including chemotaxis in bacterium cells (Escherichia coli) and eukaryotic cells (Dictyostelium). Future work in extending the modeling framework to study adaptation in more complex systems such as sensory neurons are discussed.

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“…[45] Using the chemoattractant as stimulant, their model was then verified experimentally in microfluidics. [46] Besides the chemical cues, osmotic pressure is also considered as a common stimulant to cellular behaviors. Microfluidics has proven to be a tunable and flexible technology for such research.…”

Section: Flexible Manipulationmentioning

confidence: 99%

Applications of Microfluidics in Quantitative Biology

Bai

Gao

Wen

et al. 2017

Biotechnology Journal

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Quantitative biology is dedicated to taking advantage of quantitative reasoning and advanced engineering technologies to make biology more predictable. Microfluidics, as an emerging technique, provides new approaches to precisely control fluidic conditions on small scales and collect data in highthroughput and quantitative manners. In this review, the authors present the relevant applications of microfluidics to quantitative biology based on two major categories (channel-based microfluidics and droplet-based microfluidics), and their typical features. We also envision some other microfluidic techniques that may not be employed in quantitative biology right now, but have great potential in the near future.

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Frequency-DependentEscherichia coliChemotaxis Behavior

Cited by 58 publications

References 22 publications

Barrier Crossing in Escherichia coli Chemotaxis

Barrier Crossing in Escherichia coli Chemotaxis

Adaptation in Living Systems

Applications of Microfluidics in Quantitative Biology

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