Response thresholds in bacterial chemotaxis

Lele, Pushkar P.; Shrivastava, Abhishek; Roland, Thibault; Berg, Howard C.

doi:10.1126/sciadv.1500299

Cited by 27 publications

(31 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A well-known feature of the chemotactic network is the sigmoidal relation between the direction of the flagellar rotation and the CheY-p concentration ( Yuan and Berg, 2013 ). A comparison between the CheY-p concentration from our Gillespie simulation (the chemical level) and the experimental data in Mears et al ( 2014 ); Lele et al ( 2015 ); Terasawa et al ( 2011 ) is given in table 4. Any change in the CheY-p concentration would cause a change in the probability of rotational direction of flagellar motors ( Sagawa et al, 2014 ).…”

Section: Resultsmentioning

confidence: 99%

A hierarchical approach to model decision making: a study in chemotactic behavior ofEscherichia coli

Vafadar

Kavousi

Safdari

et al. 2019

Preprint

View full text Add to dashboard Cite

Reducing the complex behavior of living entities to its underlying physical and chemical 11 processes is a formidable task in biology. Complex behaviors can be characterized as decision 12 making: the ability to process the incoming information via an intracellular network and act upon 13 this information to choose appropriate strategies. Motility is one such behavior that has been the 14 focus many modeling efforts in the past. Our aim is to reduce the chemotactic behavior in E. coli to 15 its molecular constituents in order to paint a comprehensive and end-to-end picture of this 16 intricate behavior. We utilize a hierarchical approach, consisting of three layers, to achieve this goal: 17 at the first level, chemical reactions involved in chemotaxis are simulated. In the second level, the 18 chemical reactions give rise to the mechanical movement of six independent flagella. At the last 19 layer, the two lower layers are combined to allow a digital bacterium to receive information from its 20 environment and swim through it with verve. Our results are in concert with the experimental 21 studies concerning the motility of E. coli cells. In addition, we show that our detailed model of 22 chemotaxis is reducible to a non-homogeneous Markov process. 23 24 2009). Motility can be viewed as a decision-making process that benefits the living cell by enabling 30 it to find resources in its niche more efficiently (Xie and Wu, 2014). Cell motility requires sensors 31 to monitor the environment, actuators to act upon the incoming information, and an network to 32 process that information. 33 34 The majority of bacteria are motile, swimming being its most common form (Jarrell and McBride, 35 2008; Lauga, 2016). Early studies revealed a substantial amount of variation in motility of clonal 36 cells as they navigate a uniform environment (Dufour et al., 2016). In a homogeneous environment 37 with uniformly-distributed resources, all decisions apropos of motility would be equally likely to be 38 taken -i.e., random walk. Consequently in such circumstances, a motile cell would randomly navigate 39 1 of 18 the environment. Having encountered a non-uniform distribution of resources in the environment, 40 a motile cell will move to more resource-rich areas -i.e., the default random walk turns into biased 41 random walk. 42 43 Chemotaxis, the ability of bacterial cells to sense chemical cues in their environment and move 44 accordingly, predates the divergence of the eubacteria from the archaebacteria (Woese and Fox, 45 1977). The steps taken in chemotactic behavior, to seek attractants and avoid repellents, can be 46 seen as a chain of biased random steps. To illustrate this point, we can focus on Escherichia coli. E. 47 coli detects the concentration of chemoattractants in its vicinity via an array of sensors, processes 48 the sensory data via a sensory network, and swims accordingly using its flagella (Sourjik and 49 Wingreen, 2012; Frankel et al., 2014). Following a trail of chemoattractants to get to their source is 5...

show abstract

Section: Resultsmentioning

confidence: 99%

A hierarchical approach to model decision making: a study in chemotactic behavior ofEscherichia coli

Vafadar

Kavousi

Safdari

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…By utilizing MB, as opposed to nutrient-rich growth medium, only events related to bacterial attachment are observed during the experiment, though cell motility is preserved. [47,48] As complimentary analysis, scanning electron microscopy (SEM) images of the Si chips after 120 min of bacteria accumulation during PRISM assays ( Figure 2C) reveal how individual S. epidermidis cells position themselves within the corners of the OX pores, while in the OX pillar topologies, S. epidermidis cells tend to cohere to other cells as opposed to the microstructures. Contrarily, rod-shaped E. coli cells position themselves alongside the OX pillars in straight lines, while in OX pores, they adhere either to the corners (as with E. coli WT) or in the middle of the pore (as with E. coli K-12).…”

Section: Wetting and Fluid Flow Precondition The Substrate For Bacteriamentioning

confidence: 87%

Sticky Situations: Bacterial Attachment Deciphered by Interferometry of Silicon Microstructures

Leonard

Holtzman

Haimov

et al. 2019

Preprint

View full text Add to dashboard Cite

The peculiarities of surface-bound bacterial cells are often overshadowed by the study of planktonic cells in clinical microbiology. Thus, we employ phase-shift reflectometric interference spectroscopic measurements to observe the interactions between bacterial cells and abiotic, microstructured material surfaces in a label-free, real-time manner. Both material characteristics (i.e., substrate surface charge and wettability) and characteristics of the bacterial cells (i.e., motility, cell charge, biofilm formation, and physiology) drive bacteria to adhere to a particular surface. We conclude that the attachment of bacterial cells to a surface is determined by the culmination of numerous factors. When specific characteristics of the bacteria are met with factors of the surface, enhanced cell attachment and biofilm formation occur. Such knowledge can be exploited to predict antibiotic efficacy, biofilm development, enhance biosensor development, as well as prevent biofouling.

show abstract

“…The response of the chemotaxis signaling pathway to external stimuli is conventionally measured by determining the rotational bias (15,17,39,40). This is done by sticking probes to How are such multiple stochastic switchers coupled in H. pylori?…”

Section: Discussionmentioning

confidence: 99%

Anisotropic random walks reveal chemotaxis signaling output in run-reversing bacteria

Antani

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

10The bias for a particular direction of rotation of the flagellar motor is a sensitive readout of chemotaxis signaling, which mediates bacterial migration towards favorable chemical environments. The rotational bias has not been characterized in Helicobacter pylori, which limits our understanding of the signaling dynamics. Here, we determined that H. pylori swim faster (slower) whenever their flagella rotate counterclockwise (clockwise) by analyzing their 15 hydrodynamic interactions with bounding surfaces. The anisotropy in swimming speeds helped quantify the fraction of the time that the cells swam slower to report the first measurements of the bias. A stochastic model of run-reversals indicated that the anisotropy promotes faster spread compared to isotropic swimmers. The approach further revealed that the diffuse spread of H. pylori is likely limited at the physiological temperature due to increased reversal frequencies. 20 Thus, anisotropic run-reversals make it feasible to study signal-response relations in the chemotaxis network in non-model bacterial species. Page 2 of 22 Impact Statement Anisotropy in run and reversal swimming speeds promotes the spread of H. pylori and reveals temperature-dependent behavior of the flagellar switch. cell was observed, B C , to yield: FG HC*3,C = J K LM J KThe error associated with the calculation of FG HC*3,C values decreases with increasing B C . But, 15 different cells were observed for different durations; hence the FG HC*3,C values were allocated weights that corresponded to their respective durations:

show abstract

Response thresholds in bacterial chemotaxis

Abstract: The absence of responses to shallow temporal ramps of chemicals does not appear to be accounted for by motor remodeling.

Cited by 27 publications

References 31 publications

A hierarchical approach to model decision making: a study in chemotactic behavior ofEscherichia coli

A hierarchical approach to model decision making: a study in chemotactic behavior ofEscherichia coli

Sticky Situations: Bacterial Attachment Deciphered by Interferometry of Silicon Microstructures

Anisotropic random walks reveal chemotaxis signaling output in run-reversing bacteria

Contact Info

Product

Resources

About