Relationship between cellular response and behavioral variability in bacterial chemotaxis

Emonet, Thierry; Cluzel, Philippe

doi:10.1073/pnas.0705463105

Cited by 105 publications

(179 citation statements)

References 45 publications

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“…Our model differs from previous studies (11) by considering flagellar conformations explicitly. It also differs from other previous works that ignore multiple flagella (13,14,27,28), adopt a voting model in which a cell runs when a threshold number of motors are in CCW rotation (25,26,29), or represent the flagellar bundle state by using a distortion factor that grows with time spent in CW rotation (30).…”

Section: Resultsmentioning

confidence: 85%

“…where λ i is sampled for each flagellum i that switches to semicoiled according to an exponential distribution with mean λ ¼ 0.2 s. As in other modeling studies (10,13,14,25,26), we consider the state of a cell to be either running or tumbling. For a cell to be running, it is assumed that at least x out of the N flagella must be in the normal conformation to form a coherent bundle.…”

Section: Resultsmentioning

confidence: 99%

“…Many studies of the E. coli chemotaxis system have analyzed how external stimuli are converted into the rotational state of a single motor (5)(6)(7)(8)(9)(10)(11)(12)(13)(14). The relevant output for a motile cell, however, is the duration and statistics of runs and tumbles, which result from the integration of multiple flagellar states (2,3,7,10).…”

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confidence: 99%

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Stochastic coordination of multiple actuators reduces latency and improves chemotactic response in bacteria

Sneddon

Pontius

Emonet

2011

Proc. Natl. Acad. Sci. U.S.A.

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107

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Individual neuronal, signal transduction, and regulatory pathways often control multiple stochastic downstream actuators, which raises the question of how coordinated response to a single input can be achieved when individual actuators fluctuate independently. In Escherichia coli, the bacterial chemotaxis pathway controls the activity of multiple flagellar motors to generate the run-and-tumble motion of the cell. High-resolution microscopy experiments have identified the key conformational changes adopted by individual flagella during this process. By incorporating these observations into a stochastic model of the flagellar bundle, we demonstrate that the presence of multiple motors imposes a trade-off on chemotactic performance. Multiple motors reduce the latency of the response below the time scale of the stochastic switching of a single motor, which improves performance on steep gradients of attractants. However, the uncoordinated switching of multiple motors interrupts and shortens cell runs, which thereby reduces signal detection and performance on shallow gradients. Remarkably, when slow fluctuations generated by the adaptation mechanism of the chemotaxis system are incorporated in the model at levels measured in experiments, the chemotactic sensitivity and performance in shallow gradients is partially restored with marginal effects for steep gradients. The noise is beneficial because it simultaneously generates long events in the statistics of individual motors and coordinates the motors to generate a long tail in the run length distribution of the cell. Occasional long runs are known to enhance exploration of random walkers. Here we show that they have the additional benefit of enhancing the sensitivity of the bacterium to very shallow gradients.Lévy walk | agent-based | motility | search strategy | signal processing E scherichia coli performs a random walk by controlling the rotational direction of multiple flagellar motors (1). Counterclockwise (CCW) rotation of the motors promotes formation of a coherent flagellar bundle that propels the cell forward. Clockwise (CW) rotation of any single motor causes the corresponding flagellum to change conformation, exit the bundle, and generate a reorientation event called a tumble (2, 3). Through the bacterial chemotaxis pathway, E. coli regulates the frequency of tumbles to bias its random walk in favorable directions. The core of the chemotaxis network is a two-component signal transduction system in which binding of an external attractant by clusters of transmembrane chemoreceptors leads to rapid (<1 s) inhibition of the activity of the associated histidine kinase, CheA, thereby depleting the phosphorylated form of CheY (CheY-P), a diffusible response regulator that binds to the base of the motor. Lower levels of CheY-P increase the probability that flagellar motors rotate CCW and, thus, increase the mean duration of runs in response to the attractant. The network adapts to persistent stimuli at a slower time scale (approximately 15 s) by competitive methylati...

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Section: Resultsmentioning

confidence: 85%

Section: Resultsmentioning

confidence: 99%

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Stochastic coordination of multiple actuators reduces latency and improves chemotactic response in bacteria

Sneddon

Pontius

Emonet

2011

Proc. Natl. Acad. Sci. U.S.A.

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107

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“…Chemotaxis Signaling Pathway Model : The chemotaxis signaling pathway of E. coli was modeled with three major components, and their corresponding models were adapted from recent studies 37, 38, 39, 40, 41, 42, 43. The first component, MCP complex, was represented with a Monod–Wyman–Changeux (MWC) model52 to describe the allosteric effects of receptor clusters with identical receptors.…”

Section: Methodsmentioning

confidence: 99%

“…Thus far, the taxis‐based guiding method constitutes the major way to regulate the otherwise highly stochastic motion of bacteria‐driven microswimmers, which has been studied both in vitro17, 20, 21, 22, 23, 24, 25 and in vivo 14, 16. Chemotaxis, one of the most common taxis behaviors in bacteria, has been well understood36 and its signaling pathway has been mathematically modeled 37, 38, 39, 40, 41, 42, 43. In general, the chemotaxis of free‐swimming bacteria associates with a biased random walk, enabled by preferentially suppressed tumble tendency when the bacteria travel up a chemoattractant gradient; whereas in an uniform medium, the tumble tendency is isotropic over all swimming directions.…”

Section: Introductionmentioning

confidence: 99%

Propulsion and Chemotaxis in Bacteria‐Driven Microswimmers

2017

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Despite the large body of experimental work recently on biohybrid microsystems, few studies have focused on theoretical modeling of such systems, which is essential to understand their underlying functioning mechanisms and hence design them optimally for a given application task. Therefore, this study focuses on developing a mathematical model to describe the 3D motion and chemotaxis of a type of widely studied biohybrid microswimmer, where spherical microbeads are driven by multiple attached bacteria. The model is developed based on the biophysical observations of the experimental system and is validated by comparing the model simulation with experimental 3D swimming trajectories and other motility characteristics, including mean squared displacement, speed, diffusivity, and turn angle. The chemotaxis modeling results of the microswimmers also agree well with the experiments, where a collective chemotactic behavior among multiple bacteria is observed. The simulation result implies that such collective chemotaxis behavior is due to a synchronized signaling pathway across the bacteria attached to the same microswimmer. Furthermore, the dependencies of the motility and chemotaxis of the microswimmers on certain system parameters, such as the chemoattractant concentration gradient, swimmer body size, and number of attached bacteria, toward an optimized design of such biohybrid system are studied. The optimized microswimmers would be used in targeted cargo, e.g., drug, imaging agent, gene, and RNA, transport and delivery inside the stagnant or low‐velocity fluids of the human body as one of their potential biomedical applications.

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Chemotaxis and Receptor Localization

Sourjik

2009

Bacterial Signaling

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Most motile bacteria are able to migrate towards higher concentrations of certain chemicals (attractants), which are usually nutrients or signaling molecules. At the same time, bacteria can avoid higher concentrations of repellents, potentially harmful chemicals. As the small size of a bacterial cell makes direct spatial detection of chemoeffector gradients inefficient, bacteria have evolved a chemotaxis strategy that differs substantially from that commonly employed by larger eukaryotic cells. Bacterial chemotaxis relies on temporal -rather than spatial -comparisons of chemoeffector concentrations, which are performed while moving in a gradient. Bacteria therefore have to swim first and only then can decide whether the chosen direction is favorable or not. Escherichia coli -the best-studied model for bacterial chemotaxis -has two types of swimming. A smooth swimming run, which propels the cell forward, results from the counterclockwise rotation of the flagellar motors and bundling of the flagellar filaments. Reorienting tumbles are produced by the clockwise motor rotation and dissociation of the flagellar bundle. In the adapted state with no gradients present, runs (around 2 s duration) are constantly interrupted by short tumbles (around 0.1 s) and as a result the cell performs a random walk that allows it to efficiently explore its environment [1]. In the presence of a gradient cells bias their random walk (Figure 9.1A). Although the swimming direction after each tumble is chosen nearly randomly, swimming in a favorable direction in a gradient suppresses tumbles and thus results in longer runs.The chemotactic response is mediated by a signaling system that relies on protein phosphorylation and is a member of a large class of bacterial two-component sensors. The chemotaxis pathway is well conserved across bacteria and Archaea, with the E. coli pathway (Figure 9.1B) being the best-studied example [2, 3]. The two central signaling proteins are the histidine kinase CheA and the response regulator CheY. In contrast to a typical two-component sensory kinase, CheA does not have a sensory domain, but instead -together with the adaptor protein CheW -associates with the dedicated chemosensory receptors at the membrane. Ternary complex Bacterial Signaling. Edited by

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Relationship between cellular response and behavioral variability in bacterial chemotaxis

Cited by 105 publications

References 45 publications

Stochastic coordination of multiple actuators reduces latency and improves chemotactic response in bacteria

Stochastic coordination of multiple actuators reduces latency and improves chemotactic response in bacteria

Propulsion and Chemotaxis in Bacteria‐Driven Microswimmers

Chemotaxis and Receptor Localization

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