Literature DB >> 23449035

Spatial information analysis of chemotactic trajectories.

Jan H Hoh1, William F Heinz, Jeffrey L Werbin.   

Abstract

During bacterial chemotaxis, a cell acquires information about its environment by sampling changes in the local concentration of a chemoattractant, and then uses that information to bias its motion relative to the source of the chemoattractant. The trajectory of a chemotaxing bacteria is thus a spatial manifestation of the information gathered by the cell. Here we show that a recently developed approach for computing spatial information using Fourier coefficient probabilities, the k-space information (kSI), can be used to quantify the information in such trajectories. The kSI is shown to capture expected responses to gradients of a chemoattractant. We then extend the k-space approach by developing an experimental probability distribution (EPD) that is computed from chemotactic trajectories collected under a reference condition. The EPD accounts for connectivity and other constraints that the nature of the trajectories imposes on the k-space computation. The EPD is used to compute the spatial information from any trajectory of interest, relative to the reference condition. The EPD-based spatial information also captures the expected responses to gradients of a chemoattractant, although the results differ in significant ways from the original kSI computation. In addition, the entropy calculated from the EPD provides a useful measure of trajectory space. The methods developed are highly general, and can be applied to a wide range of other trajectory types as well as non-trajectory data.

Entities:  

Keywords:  Chemotaxis; Trajectory analysis; Trajectory space; k-space information

Year:  2011        PMID: 23449035      PMCID: PMC3326145          DOI: 10.1007/s10867-011-9253-5

Source DB:  PubMed          Journal:  J Biol Phys        ISSN: 0092-0606            Impact factor:   1.365


  17 in total

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Authors:  Massimo Vergassola; Emmanuel Villermaux; Boris I Shraiman
Journal:  Nature       Date:  2007-01-25       Impact factor: 49.962

2.  Relationship between cellular response and behavioral variability in bacterial chemotaxis.

Authors:  Thierry Emonet; Philippe Cluzel
Journal:  Proc Natl Acad Sci U S A       Date:  2008-02-25       Impact factor: 11.205

3.  Swimming patterns and dynamics of simulated Escherichia coli bacteria.

Authors:  Laura Zonia; Dennis Bray
Journal:  J R Soc Interface       Date:  2009-02-25       Impact factor: 4.118

Review 4.  The two-component signaling pathway of bacterial chemotaxis: a molecular view of signal transduction by receptors, kinases, and adaptation enzymes.

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Journal:  Annu Rev Cell Dev Biol       Date:  1997       Impact factor: 13.827

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Authors:  H C Berg; D A Brown
Journal:  Antibiot Chemother (1971)       Date:  1974

6.  Fractal analysis of narwhal space use patterns.

Authors:  Kristin L Laidre; Mads P Heide-Jørgensen; Miles L Logsdon; Roderick C Hobbs; Rune Dietz; Glenn R VanBlaricom
Journal:  Zoology (Jena)       Date:  2004       Impact factor: 2.240

7.  Pathfinding by neuronal growth cones in grasshopper embryos. I. Divergent choices made by the growth cones of sibling neurons.

Authors:  J A Raper; M Bastiani; C S Goodman
Journal:  J Neurosci       Date:  1983-01       Impact factor: 6.167

8.  Biased random walk models for chemotaxis and related diffusion approximations.

Authors:  W Alt
Journal:  J Math Biol       Date:  1980-04       Impact factor: 2.259

9.  The EthoVision video tracking system--a tool for behavioral phenotyping of transgenic mice.

Authors:  A J Spink; R A Tegelenbosch; M O Buma; L P Noldus
Journal:  Physiol Behav       Date:  2001-08

10.  Dependence of bacterial chemotaxis on gradient shape and adaptation rate.

Authors:  Nikita Vladimirov; Linda Løvdok; Dirk Lebiedz; Victor Sourjik
Journal:  PLoS Comput Biol       Date:  2008-12-19       Impact factor: 4.475

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