↓ Skip to main content

Computational Modeling of Auxin: A Foundation for Plant Engineering

Overview of attention for article published in Frontiers in Plant Science, December 2016
Altmetric Badge

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 (87th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

twitter
22 X users
facebook
1 Facebook page

Citations

dimensions_citation
11 Dimensions

Readers on

mendeley
38 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Computational Modeling of Auxin: A Foundation for Plant Engineering
Published in
Frontiers in Plant Science, December 2016
DOI 10.3389/fpls.2016.01881
Pubmed ID
Authors

Alejandro Morales-Tapia, Alfredo Cruz-Ramírez

Abstract

Since the development of agriculture, humans have relied on the cultivation of plants to satisfy our increasing demand for food, natural products, and other raw materials. As we understand more about plant development, we can better manipulate plants to fulfill our particular needs. Auxins are a class of simple metabolites that coordinate many developmental activities like growth and the appearance of functional structures in plants. Computational modeling of auxin has proven to be an excellent tool in elucidating many mechanisms that underlie these developmental events. Due to the complexity of these mechanisms, current modeling efforts are concerned only with single phenomena focused on narrow spatial and developmental contexts; but a general model of plant development could be assembled by integrating the insights from all of them. In this perspective, we summarize the current collection of auxin-driven computational models, focusing on how they could come together into a single model for plant development. A model of this nature would allow researchers to test hypotheses in silico and yield accurate predictions about the behavior of a plant under a given set of physical and biochemical constraints. It would also provide a solid foundation toward the establishment of plant engineering, a proposed discipline intended to enable the design and production of plants that exhibit an arbitrarily defined set of features.

X Demographics

X Demographics

The data shown below were collected from the profiles of 22 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Chile 1 3%
Unknown 36 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 21%
Student > Bachelor 5 13%
Researcher 5 13%
Student > Master 4 11%
Student > Postgraduate 3 8%
Other 5 13%
Unknown 8 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 53%
Biochemistry, Genetics and Molecular Biology 5 13%
Computer Science 2 5%
Psychology 1 3%
Chemistry 1 3%
Other 0 0%
Unknown 9 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 22 January 2017.
All research outputs
#2,899,795
of 26,454,856 outputs
Outputs from Frontiers in Plant Science
#1,274
of 25,287 outputs
Outputs of similar age
#53,557
of 428,670 outputs
Outputs of similar age from Frontiers in Plant Science
#17
of 494 outputs
Altmetric has tracked 26,454,856 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 25,287 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done particularly well, scoring higher than 94% 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 428,670 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 87% of its contemporaries.
We're also able to compare this research output to 494 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 96% of its contemporaries.