Excitation and Adaptation in Bacteria–a Model Signal Transduction System that Controls Taxis and Spatial Pattern Formation

Othmer, Hans G.; Xin, Xiangrong; Xue, Chuan

doi:10.3390/ijms14059205

Cited by 24 publications

(17 citation statements)

References 199 publications

(264 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To understand the underlying reasons of this result, we derived an analytical relationship between CheY-P concentration and drift velocity along a one-dimensional gradient. For simplicity we used a one-dimensional analytical representation of bacterial chemotaxis in two or three dimensions [27] , [33] , [34] , [36] , [37] . In this framework, cells either go up or down the gradient or tumble and the effect of rotational diffusion can be represented as a jump process between runs up and runs down the gradient with transition rate ( d -1) D r , where d represents the number of spatial dimension [36] .…”

Section: Resultsmentioning

confidence: 99%

Limits of Feedback Control in Bacterial Chemotaxis

et al. 2014

View full text Add to dashboard Cite

Inputs to signaling pathways can have complex statistics that depend on the environment and on the behavioral response to previous stimuli. Such behavioral feedback is particularly important in navigation. Successful navigation relies on proper coupling between sensors, which gather information during motion, and actuators, which control behavior. Because reorientation conditions future inputs, behavioral feedback can place sensors and actuators in an operational regime different from the resting state. How then can organisms maintain proper information transfer through the pathway while navigating diverse environments? In bacterial chemotaxis, robust performance is often attributed to the zero integral feedback control of the sensor, which guarantees that activity returns to resting state when the input remains constant. While this property provides sensitivity over a wide range of signal intensities, it remains unclear how other parameters such as adaptation rate and adapted activity affect chemotactic performance, especially when considering that the swimming behavior of the cell determines the input signal. We examine this issue using analytical models and simulations that incorporate recent experimental evidences about behavioral feedback and flagellar motor adaptation. By focusing on how sensory information carried by the response regulator is best utilized by the motor, we identify an operational regime that maximizes drift velocity along chemical concentration gradients for a wide range of environments and sensor adaptation rates. This optimal regime is outside the dynamic range of the motor response, but maximizes the contrast between run duration up and down gradients. In steep gradients, the feedback from chemotactic drift can push the system through a bifurcation. This creates a non-chemotactic state that traps cells unless the motor is allowed to adapt. Although motor adaptation helps, we find that as the strength of the feedback increases individual phenotypes cannot maintain the optimal operational regime in all environments, suggesting that diversity could be beneficial.

show abstract

Section: Resultsmentioning

confidence: 99%

Limits of Feedback Control in Bacterial Chemotaxis

et al. 2014

View full text Add to dashboard Cite

show abstract

“…To complete our model we have to specify the distribution of tumble angles | β |. For the E. coli bacterium, we are inspired by the seminal work of Berg and Brown [ 2 ] and choose a gamma distribution restricted to the domain [0, π ] [ 40 ]: The lower, incomplete gamma function comes in when normalizing P (| β |) to one on the interval [0, π ]. For k > 1 the gamma distribution has a maximum at ( k −1) σ .…”

Section: Modelsmentioning

confidence: 99%

Inferring the Chemotactic Strategy of P. putida and E. coli Using Modified Kramers-Moyal Coefficients

et al. 2017

View full text Add to dashboard Cite

Many bacteria perform a run-and-tumble random walk to explore their surrounding and to perform chemotaxis. In this article we present a novel method to infer the relevant parameters of bacterial motion from experimental trajectories including the tumbling events. We introduce a stochastic model for the orientation angle, where a shot-noise process initiates tumbles, and analytically calculate conditional moments, reminiscent of Kramers-Moyal coefficients. Matching them with the moments calculated from experimental trajectories of the bacteria E. coli and Pseudomonas putida, we are able to infer their respective tumble rates, the rotational diffusion constants, and the distributions of tumble angles in good agreement with results from conventional tumble recognizers. We also define a novel tumble recognizer, which explicitly quantifies the error in recognizing tumbles. In the presence of a chemical gradient we condition the moments on the bacterial direction of motion and thereby explore the chemotaxis strategy. For both bacteria we recover and quantify the classical chemotactic strategy, where the tumble rate is smallest along the chemical gradient. In addition, for E. coli we detect some cells, which bias their mean tumble angle towards smaller values. Our findings are supported by a scaling analysis of appropriate ratios of conditional moments, which are directly calculated from experimental data.

show abstract

“…Finally, we would like to highlight that the link between the measures ν∞ and µ∞ may be expressed in another way than (31). Indeed, for any x ∈ E, we have…”

Section: A Ergodicity and Invariant Measuresmentioning

confidence: 99%

“…In both cases, the post-jump location of the process at time T1 is governed by the transition distribution Q(Φ(X0, T1), dy) and the motion restarts from this new point as before. This family of stochastic models is well-adapted for tackling various problems arising for example in biology [9,16,30,31,32,33,34], in neuroscience [22] or in reliability [7,14,13,18]. Indeed, most of applications involving both deterministic motion and punctual random events may be modeled by a PDMP.…”

mentioning

confidence: 99%

Optimal choice among a class of nonparametric estimators of the jump rate for piecewise-deterministic Markov processes

Azaïs¹,

Muller-Gueudin²

2016

Electron. J. Statist.

View full text Add to dashboard Cite

A piecewise-deterministic Markov process is a stochastic process whose behavior is governed by an ordinary differential equation punctuated by random jumps occurring at random times. We focus on the nonparametric estimation problem of the jump rate for such a stochastic model observed within a long time interval under an ergodicity condition. We introduce an uncountable class (indexed by the deterministic flow) of recursive kernel estimates of the jump rate and we establish their strong pointwise consistency as well as their asymptotic normality. We propose to choose among this class the estimator with the minimal variance, which is unfortunately unknown and thus remains to be estimated. We also discuss the choice of the bandwidth parameters by cross-validation methods.

show abstract

Excitation and Adaptation in Bacteria–a Model Signal Transduction System that Controls Taxis and Spatial Pattern Formation

Cited by 24 publications

References 199 publications

Limits of Feedback Control in Bacterial Chemotaxis

Limits of Feedback Control in Bacterial Chemotaxis

Inferring the Chemotactic Strategy of P. putida and E. coli Using Modified Kramers-Moyal Coefficients

Optimal choice among a class of nonparametric estimators of the jump rate for piecewise-deterministic Markov processes

Contact Info

Product

Resources

About