Evidence map›Paper›PMID 23110896›Full record

ReviewThe Plant cell2012

Modeling regulatory networks to understand plant development: small is beautiful.

Alistair M Middleton, Etienne Farcot, Markus R Owen, Teva Vernoux

Abstract readReview
In one paragraph

Review in The Plant cell, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Modelling hormonal response and development.Trends in plant science · 2014
    Review
  7. Plant and animal stem cells: similar yet different.Nature reviews. Molecular cell biology · 2014
    Review
  8. Light intensity modulates the regulatory network of the shade avoidance response in Arabidopsis.Proceedings of the National Academy of Sciences of the United States of America · 2014
    Article
  9. Article
  10. Regulatory network features in Listeria monocytogenes-changing the way we talk.Frontiers in cellular and infection microbiology · 2014
    Review
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. 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

4 authors.

Alistair M MiddletonCenter for Modeling and Simulation in the Biosciences and Interdisciplinary Center for Scientific Computing, University of Heidelberg, 69120 Heidelberg, Germany.
Etienne Farcot
Markus R Owen
Teva Vernoux

Funding

Biotechnology and Biological Sciences Research Council BB/D019613/1Biotechnology and Biological Sciences Research Council BB/D0196131
6 · The paper itself

Abstract

We now have unprecedented capability to generate large data sets on the myriad genes and molecular players that regulate plant development. Networks of interactions between systems components can be derived from that data in various ways and can be used to develop mathematical models of various degrees of sophistication. Here, we discuss why, in many cases, it is productive to focus on small networks. We provide a brief and accessible introduction to relevant mathematical and computational approaches to model regulatory networks and discuss examples of small network models that have helped generate new insights into plant biology (where small is beautiful), such as in circadian rhythms, hormone signaling, and tissue patterning. We conclude by outlining some of the key technical and modeling challenges for the future.

Indexed as

Gene Regulatory NetworksModels, BiologicalBody PatterningCircadian ClocksFeedback, PhysiologicalPlant Development

Identifiers

PMID23110896
PMCPMC3517225

What OpenQuestion holds

Textmetadata
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.