Evidence map›Paper›PMID 29784812›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2018

Distributed and dynamic intracellular organization of extracellular information.

Alejandro A Granados, Julian M J Pietsch, Sarah A Cepeda-Humerez, Iseabail L Farquhar, Gašper Tkačik, Peter S Swain

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing 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

36 citing papers in PubMed.

  1. Article
  2. Review
  3. Characterizing Optogenetic Tools for Use in Synthetic Gene Circuits.Methods in molecular biology (Clifton, N.J.) · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Dynamics of positional information in the vertebrate neural tube.Journal of the Royal Society, Interface · 2024
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Quantifying information of intracellular signaling: progress with machine learning.Reports on progress in physics. Physical Society (Great Britain) · 2022
    Review
  18. Article
  19. Article
  20. 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

6 authors.

Alejandro A GranadosDepartment of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom.
Julian M J PietschSynthSys-Synthetic and Systems Biology, University of Edinburgh, Edinburgh EH9 3BF, United Kingdom.ORCID 0000-0002-9992-2384
Sarah A Cepeda-HumerezInstitute of Science and Technology Austria, 3400 Klosterneuburg, Austria.
Iseabail L FarquharSynthSys-Synthetic and Systems Biology, University of Edinburgh, Edinburgh EH9 3BF, United Kingdom.
Gašper TkačikInstitute of Science and Technology Austria, 3400 Klosterneuburg, Austria.ORCID 0000-0002-6699-1455
Peter S SwainSynthSys-Synthetic and Systems Biology, University of Edinburgh, Edinburgh EH9 3BF, United Kingdom; peter.swain@ed.ac.uk.ORCID 0000-0001-7489-8587

Funding

Biotechnology and Biological Sciences Research Council
6 · The paper itself

Abstract

Although cells respond specifically to environments, how environmental identity is encoded intracellularly is not understood. Here, we study this organization of information in budding yeast by estimating the mutual information between environmental transitions and the dynamics of nuclear translocation for 10 transcription factors. Our method of estimation is general, scalable, and based on decoding from single cells. The dynamics of the transcription factors are necessary to encode the highest amounts of extracellular information, and we show that information is transduced through two channels: Generalists (Msn2/4, Tod6 and Dot6, Maf1, and Sfp1) can encode the nature of multiple stresses, but only if stress is high; specialists (Hog1, Yap1, and Mig1/2) encode one particular stress, but do so more quickly and for a wider range of magnitudes. In particular, Dot6 encodes almost as much information as Msn2, the master regulator of the environmental stress response. Each transcription factor reports differently, and it is only their collective behavior that distinguishes between multiple environmental states. Changes in the dynamics of the localization of transcription factors thus constitute a precise, distributed internal representation of extracellular change. We predict that such multidimensional representations are common in cellular decision-making.

Indexed as

Gene-Environment InteractionCell NucleusCyclic AMP-Dependent Protein KinasesCytoplasmDNA-Binding ProteinsEnvironmentExtracellular SpaceGene Expression Regulation, FungalIntracellular Signaling Peptides and ProteinsMitogen-Activated Protein KinasesModels, BiologicalProtein TransportSaccharomyces cerevisiaeSaccharomyces cerevisiae ProteinsSaccharomycetalesSignal TransductionCyclic AMP-Dependent Protein KinasesDNA-Binding ProteinsIntracellular Signaling Peptides and ProteinsMitogen-Activated Protein KinasesSaccharomyces cerevisiae ProteinsTranscription Factorscell signalingmutual informationstresstime seriestranscription factors

Identifiers

PMID29784812
PMCPMC6003323

What OpenQuestion holds

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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.