Evidence map›Paper›PMID 12730377›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2003

Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling.

Jesper Tegner, M K Stephen Yeung, Jeff Hasty, James J Collins

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2003. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 125 papers.

0numbers the graph read from it
0cells of the map it votes in
125citing papers in PubMed
10.0field-weighted citation impact, top 1% of its field
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

125 citing papers in PubMed, 447 citations in OpenAlex.

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  16. Single Cell RNA-Sequencing for the Study of Atherosclerosis.Journal of lipid and atherosclerosis · 2019
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65 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 at 1 institution in 2 countries.

Jesper TegnerCenter for BioDynamics and Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA. jespert@ifm.liu.se
M K Stephen Yeung
Jeff Hasty
James J Collins
Boston University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

While the fundamental building blocks of biology are being tabulated by the various genome projects, microarray technology is setting the stage for the task of deducing the connectivity of large-scale gene networks. We show how the perturbation of carefully chosen genes in a microarray experiment can be used in conjunction with a reverse engineering algorithm to reveal the architecture of an underlying gene regulatory network. Our iterative scheme identifies the network topology by analyzing the steady-state changes in gene expression resulting from the systematic perturbation of a particular node in the network. We highlight the validity of our reverse engineering approach through the successful deduction of the topology of a linear in numero gene network and a recently reported model for the segmentation polarity network in Drosophila melanogaster. Our method may prove useful in identifying and validating specific drug targets and in deconvolving the effects of chemical compounds.

Indexed as

Models, GeneticAlgorithmsGenesGenetic EngineeringNerve NetProteinsReproducibility of ResultsProteins

Identifiers

PMID12730377
PMCPMC156306
OpenAlexW1972764318

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.