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Reverse Engineering: A Key Component of Systems Biology to Unravel Global Abiotic Stress Cross-Talk

Overview of attention for article published in Frontiers in Plant Science, January 2012
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  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

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1 X user
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1 Facebook page
wikipedia
1 Wikipedia page

Citations

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33 Dimensions

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134 Mendeley
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Title
Reverse Engineering: A Key Component of Systems Biology to Unravel Global Abiotic Stress Cross-Talk
Published in
Frontiers in Plant Science, January 2012
DOI 10.3389/fpls.2012.00294
Pubmed ID
Authors

Swetlana Friedel, Björn Usadel, Nicolaus von Wirén, Nese Sreenivasulu

Abstract

Understanding the global abiotic stress response is an important stepping stone for the development of universal stress tolerance in plants in the era of climate change. Although co-occurrence of several stress factors (abiotic and biotic) in nature is found to be frequent, current attempts are poor to understand the complex physiological processes impacting plant growth under combinatory factors. In this review article, we discuss the recent advances of reverse engineering approaches that led to seminal discoveries of key candidate regulatory genes involved in cross-talk of abiotic stress responses and summarized the available tools of reverse engineering and its relevant application. Among the universally induced regulators involved in various abiotic stress responses, we highlight the importance of (i) abscisic acid (ABA) and jasmonic acid (JA) hormonal cross-talks and (ii) the central role of WRKY transcription factors (TF), potentially mediating both abiotic and biotic stress responses. Such interactome networks help not only to derive hypotheses but also play a vital role in identifying key regulatory targets and interconnected hormonal responses. To explore the full potential of gene network inference in the area of abiotic stress tolerance, we need to validate hypotheses by implementing time-dependent gene expression data from genetically engineered plants with modulated expression of target genes. We further propose to combine information on gene-by-gene interactions with data from physical interaction platforms such as protein-protein or TF-gene networks.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 134 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 1%
Brazil 2 1%
United States 2 1%
Sri Lanka 1 <1%
Netherlands 1 <1%
Unknown 126 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 42 31%
Researcher 30 22%
Student > Master 13 10%
Student > Postgraduate 10 7%
Student > Doctoral Student 7 5%
Other 20 15%
Unknown 12 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 84 63%
Biochemistry, Genetics and Molecular Biology 14 10%
Computer Science 6 4%
Business, Management and Accounting 3 2%
Environmental Science 3 2%
Other 10 7%
Unknown 14 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 June 2020.
All research outputs
#6,919,691
of 22,691,736 outputs
Outputs from Frontiers in Plant Science
#4,134
of 19,888 outputs
Outputs of similar age
#63,946
of 244,134 outputs
Outputs of similar age from Frontiers in Plant Science
#32
of 195 outputs
Altmetric has tracked 22,691,736 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 19,888 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done well, scoring higher than 78% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 244,134 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 195 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.