Evidence map›Paper›PMID 25340783›Full record

ArticlePLoS computational biology2014

Predicting chemical environments of bacteria from receptor signaling.

Diana Clausznitzer, Gabriele Micali, Silke Neumann, Victor Sourjik, Robert G Endres

Abstract read
In one paragraph

Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

  1. Review
  2. Persistent pseudopod splitting is an effective chemotaxis strategy in shallow gradients.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  3. Design patterns of biological cells.BioEssays : news and reviews in molecular, cellular and developmental biology · 2024
    Review
  4. Article
  5. The ecological roles of bacterial chemotaxis.Nature reviews. Microbiology · 2022
    Review
  6. Article
  7. Review
  8. Article
  9. A Multi-Scale Approach to ModelingEntropy (Basel, Switzerland) · 2020
    Article
  10. Article
  11. Article
  12. Drift and Behavior of E. coli Cells.Biophysical journal · 2017
    Article
  13. Stochastic feeding dynamics arise from the need for information and energy.Proceedings of the National Academy of Sciences of the United States of America · 2017
    Article
  14. Article
  15. The Role of Adaptation in Bacterial Speed Races.PLoS computational biology · 2016
    Article
  16. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Diana ClausznitzerDepartment of Life Sciences, Imperial College, London, United Kingdom; Centre for Integrative Systems Biology and Bioinformatics, Imperial College, London, United Kingdom; BioQuant, Heidelberg University, Heidelberg, Germany; Institute for Medical Informatics and Biometry, Technische Universität Dresden, Dresden, Germany.
Gabriele MicaliDepartment of Life Sciences, Imperial College, London, United Kingdom; Centre for Integrative Systems Biology and Bioinformatics, Imperial College, London, United Kingdom.
Silke NeumannCentre of Molecular Biology, Heidelberg University, DKFZ-ZMBH Alliance, Heidelberg, Germany.
Victor SourjikCentre of Molecular Biology, Heidelberg University, DKFZ-ZMBH Alliance, Heidelberg, Germany; Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.
Robert G EndresDepartment of Life Sciences, Imperial College, London, United Kingdom; Centre for Integrative Systems Biology and Bioinformatics, Imperial College, London, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sensory systems have evolved to respond to input stimuli of certain statistical properties, and to reliably transmit this information through biochemical pathways. Hence, for an experimentally well-characterized sensory system, one ought to be able to extract valuable information about the statistics of the stimuli. Based on dose-response curves from in vivo fluorescence resonance energy transfer (FRET) experiments of the bacterial chemotaxis sensory system, we predict the chemical gradients chemotactic Escherichia coli cells typically encounter in their natural environment. To predict average gradients cells experience, we revaluate the phenomenological Weber's law and its generalizations to the Weber-Fechner law and fold-change detection. To obtain full distributions of gradients we use information theory and simulations, considering limitations of information transmission from both cell-external and internal noise. We identify broad distributions of exponential gradients, which lead to log-normal stimuli and maximal drift velocity. Our results thus provide a first step towards deciphering the chemical nature of complex, experimentally inaccessible cellular microenvironments, such as the human intestine.

Indexed as

Models, BiologicalCellular MicroenvironmentChemotaxisComputational BiologyComputer SimulationEscherichia coliInformation TheoryModels, Chemical

Identifiers

PMID25340783
PMCPMC4207464

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.