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Recent Developments in Systems Biology and Metabolic Engineering of Plant–Microbe Interactions

Overview of attention for article published in Frontiers in Plant Science, September 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

blogs
1 blog
twitter
2 X users
facebook
1 Facebook page

Citations

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72 Dimensions

Readers on

mendeley
132 Mendeley
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Title
Recent Developments in Systems Biology and Metabolic Engineering of Plant–Microbe Interactions
Published in
Frontiers in Plant Science, September 2016
DOI 10.3389/fpls.2016.01421
Pubmed ID
Authors

Vishal Kumar, Mehak Baweja, Puneet K Singh, Pratyoosh Shukla

Abstract

Microorganisms play a crucial role in the sustainability of the various ecosystems. The characterization of various interactions between microorganisms and other biotic factors is a necessary footstep to understand the association and functions of microbial communities. Among the different microbial interactions in an ecosystem, plant-microbe interaction plays an important role to balance the ecosystem. The present review explores plant-microbe interactions using gene editing and system biology tools toward the comprehension in improvement of plant traits. Further, system biology tools like FBA (flux balance analysis), OptKnock, and constraint-based modeling helps in understanding such interactions as a whole. In addition, various gene editing tools have been summarized and a strategy has been hypothesized for the development of disease free plants. Furthermore, we have tried to summarize the predictions through data retrieved from various types of sources such as high throughput sequencing data (e.g., single nucleotide polymorphism detection, RNA-seq, proteomics) and metabolic models have been reconstructed from such sequences for species communities. It is well known fact that systems biology approaches and modeling of biological networks will enable us to learn the insight of such network and will also help further in understanding these interactions.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users 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 132 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Chile 1 <1%
Portugal 1 <1%
Ireland 1 <1%
Brazil 1 <1%
Unknown 128 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 27 20%
Student > Ph. D. Student 25 19%
Student > Master 19 14%
Student > Bachelor 7 5%
Student > Doctoral Student 6 5%
Other 18 14%
Unknown 30 23%
Readers by discipline Count As %
Agricultural and Biological Sciences 51 39%
Biochemistry, Genetics and Molecular Biology 24 18%
Environmental Science 7 5%
Computer Science 1 <1%
Immunology and Microbiology 1 <1%
Other 9 7%
Unknown 39 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 14 November 2016.
All research outputs
#3,685,619
of 22,893,031 outputs
Outputs from Frontiers in Plant Science
#1,841
of 20,304 outputs
Outputs of similar age
#62,473
of 322,701 outputs
Outputs of similar age from Frontiers in Plant Science
#31
of 394 outputs
Altmetric has tracked 22,893,031 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 20,304 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done particularly well, scoring higher than 90% 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 322,701 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 80% of its contemporaries.
We're also able to compare this research output to 394 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.