Evidence map›Paper›PMID 27058171›Full record

ArticleeLife2016

High-throughput mathematical analysis identifies Turing networks for patterning with equally diffusing signals.

Luciano Marcon, Xavier Diego, James Sharpe, Patrick Müller

Abstract read
In one paragraph

Article in eLife, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.

0numbers the graph read from it
0cells of the map it votes in
64citing 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

64 citing papers in PubMed.

  1. Article
  2. How nature discovers rare Turing islands: Exploration by common limit cycles.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  3. Article
  4. Revisiting Turing's Chemical Basis of Morphogenesis.Bulletin of mathematical biology · 2026
    Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
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  13. Article
  14. Information content and optimization of self-organized developmental systems.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  15. Article
  16. Article
  17. Minimal reaction schemes for pattern formation.Journal of the Royal Society, Interface · 2024
    Article
  18. Article
  19. Self-organized BMP signaling dynamics underlie the development and evolution of digit segmentation patterns in birds and mammals.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  20. Article

4 more citing papers are in PubMed but not listed here.

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

4 authors.

Luciano MarconFriedrich Miescher Laboratory of the Max Planck Society, Tübingen, Germany.ORCID 0000-0003-0957-9170
Xavier DiegoEMBL-CRG Systems Biology Research Unit, Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain.
James SharpeEMBL-CRG Systems Biology Research Unit, Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain.
Patrick MüllerFriedrich Miescher Laboratory of the Max Planck Society, Tübingen, Germany.ORCID 0000-0002-0702-6209

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Turing reaction-diffusion model explains how identical cells can self-organize to form spatial patterns. It has been suggested that extracellular signaling molecules with different diffusion coefficients underlie this model, but the contribution of cell-autonomous signaling components is largely unknown. We developed an automated mathematical analysis to derive a catalog of realistic Turing networks. This analysis reveals that in the presence of cell-autonomous factors, networks can form a pattern with equally diffusing signals and even for any combination of diffusion coefficients. We provide a software (available at http://www.RDNets.com) to explore these networks and to constrain topologies with qualitative and quantitative experimental data. We use the software to examine the self-organizing networks that control embryonic axis specification and digit patterning. Finally, we demonstrate how existing synthetic circuits can be extended with additional feedbacks to form Turing reaction-diffusion systems. Our study offers a new theoretical framework to understand multicellular pattern formation and enables the wide-spread use of mathematical biology to engineer synthetic patterning systems.

Indexed as

Body PatterningModels, TheoreticalEmbryologySoftwarecomputational biologydevelopmental biologydifferential diffusivitydiffusion-driven instabilitymousepattern formationS. cerevisiaeself-organizationstem cellssystems biologyTuring patternszebrafish

Identifiers

PMID27058171
PMCPMC4922859

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Registered trials

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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.