Evidence map›Paper›PMID 24185701›Full record

ArticleNucleic acids research2014

Molecular mechanisms of system responses to novel stimuli are predictable from public data.

Samuel A Danziger, Alexander V Ratushny, Jennifer J Smith, Ramsey A Saleem, Yakun Wan, Christina E Arens, Abraham M Armstrong, Katherine Sitko, Wei-Ming Chen, Jung-Hsien Chiang and 3 more

Abstract read
In one paragraph

Article in Nucleic acids research, 2014. 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. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Signaling dynamics and peroxisomes.Current opinion in cell biology · 2015
    Review
  12. Article
  13. Article
  14. Article
  15. Systems cell biology.The Journal of cell biology · 2014
    Review
  16. Article
  17. Peroxisomes take shape.Nature reviews. Molecular cell biology · 2013
    Review
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

13 authors.

Samuel A DanzigerSeattle Biomedical Research Institute, Seattle, WA 98109-5219 USA, Institute for Systems Biology, Seattle, WA 98109-5240 USA, The Key Laboratory of Developmental Genes and Human Disease, Ministry of Education, Institute of Life Science, Southeast University, Nanjing 210096, China and Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 704, Taiwan.
Alexander V Ratushny
Jennifer J Smith
Ramsey A Saleem
Yakun Wan
Christina E Arens
Abraham M Armstrong
Katherine Sitko
Wei-Ming Chen
Jung-Hsien Chiang
David J Reiss
Nitin S Baliga
John D Aitchison

Funding

Quantitative Systems BiologyP50GM076547 · NIGMS · INSTITUTE FOR SYSTEMS BIOLOGY · PI DUDLEY, AIMEE M · 2006 to 2016
$30.6M
New Tools for Exploring the Dynamic Interactome (Renewal)U54GM103511 · NIGMS · ROCKEFELLER UNIVERSITY · PI ROUT, MICHAEL P · 2012 to 2013
$6.2M
Oleate-responsive gene regulatory networks governing peroxisome proliferationR01GM075152 · NIGMS · SEATTLE BIOMEDICAL RESEARCH INSTITUTE · PI AITCHISON, JOHN D. · 2008 to 2011
$1.9M
Interrogation of systems level mechanisms controlling DNA repair processesR01GM077398 · NIGMS · INSTITUTE FOR SYSTEMS BIOLOGY · PI BALIGA, NITIN S · 2007 to 2010
$1.3M
NIGMS NIH HHS 1R01 GM077398NIGMS NIH HHS P50 GM076547NIGMS NIH HHS R01 GM075152NIGMS NIH HHS U54GM103511
6 · The paper itself

Abstract

Systems scale models provide the foundation for an effective iterative cycle between hypothesis generation, experiment and model refinement. Such models also enable predictions facilitating the understanding of biological complexity and the control of biological systems. Here, we demonstrate the reconstruction of a globally predictive gene regulatory model from public data: a model that can drive rational experiment design and reveal new regulatory mechanisms underlying responses to novel environments. Specifically, using ∼ 1500 publically available genome-wide transcriptome data sets from Saccharomyces cerevisiae, we have reconstructed an environment and gene regulatory influence network that accurately predicts regulatory mechanisms and gene expression changes on exposure of cells to completely novel environments. Focusing on transcriptional networks that induce peroxisomes biogenesis, the model-guided experiments allow us to expand a core regulatory network to include novel transcriptional influences and linkage across signaling and transcription. Thus, the approach and model provides a multi-scalar picture of gene dynamics and are powerful resources for exploiting extant data to rationally guide experimentation. The techniques outlined here are generally applicable to any biological system, which is especially important when experimental systems are challenging and samples are difficult and expensive to obtain-a common problem in laboratory animal and human studies.

Indexed as

Gene Regulatory NetworksGene Expression ProfilingGene Expression Regulation, FungalSaccharomyces cerevisiaeSystems Biology

Identifiers

PMID24185701
PMCPMC3919619

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.