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Systems biology of the structural proteome

Overview of attention for article published in BMC Systems Biology, March 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#16 of 1,143)
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

blogs
2 blogs
twitter
21 X users
facebook
1 Facebook page

Citations

dimensions_citation
51 Dimensions

Readers on

mendeley
176 Mendeley
citeulike
2 CiteULike
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Title
Systems biology of the structural proteome
Published in
BMC Systems Biology, March 2016
DOI 10.1186/s12918-016-0271-6
Pubmed ID
Authors

Elizabeth Brunk, Nathan Mih, Jonathan Monk, Zhen Zhang, Edward J. O’Brien, Spencer E. Bliven, Ke Chen, Roger L. Chang, Philip E. Bourne, Bernhard O. Palsson

Abstract

The success of genome-scale models (GEMs) can be attributed to the high-quality, bottom-up reconstructions of metabolic, protein synthesis, and transcriptional regulatory networks on an organism-specific basis. Such reconstructions are biochemically, genetically, and genomically structured knowledge bases that can be converted into a mathematical format to enable a myriad of computational biological studies. In recent years, genome-scale reconstructions have been extended to include protein structural information, which has opened up new vistas in systems biology research and empowered applications in structural systems biology and systems pharmacology. Here, we present the generation, application, and dissemination of genome-scale models with protein structures (GEM-PRO) for Escherichia coli and Thermotoga maritima. We show the utility of integrating molecular scale analyses with systems biology approaches by discussing several comparative analyses on the temperature dependence of growth, the distribution of protein fold families, substrate specificity, and characteristic features of whole cell proteomes. Finally, to aid in the grand challenge of big data to knowledge, we provide several explicit tutorials of how protein-related information can be linked to genome-scale models in a public GitHub repository ( https://github.com/SBRG/GEMPro/tree/master/GEMPro_recon/). Translating genome-scale, protein-related information to structured data in the format of a GEM provides a direct mapping of gene to gene-product to protein structure to biochemical reaction to network states to phenotypic function. Integration of molecular-level details of individual proteins, such as their physical, chemical, and structural properties, further expands the description of biochemical network-level properties, and can ultimately influence how to model and predict whole cell phenotypes as well as perform comparative systems biology approaches to study differences between organisms. GEM-PRO offers insight into the physical embodiment of an organism's genotype, and its use in this comparative framework enables exploration of adaptive strategies for these organisms, opening the door to many new lines of research. With these provided tools, tutorials, and background, the reader will be in a position to run GEM-PRO for their own purposes.

X Demographics

X Demographics

The data shown below were collected from the profiles of 21 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 176 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 2%
Colombia 1 <1%
Portugal 1 <1%
Japan 1 <1%
Malaysia 1 <1%
Unknown 168 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 55 31%
Researcher 34 19%
Student > Bachelor 19 11%
Student > Master 18 10%
Professor > Associate Professor 10 6%
Other 25 14%
Unknown 15 9%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 53 30%
Agricultural and Biological Sciences 50 28%
Computer Science 15 9%
Engineering 15 9%
Medicine and Dentistry 5 3%
Other 15 9%
Unknown 23 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 26. 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 27 November 2017.
All research outputs
#1,309,181
of 23,577,761 outputs
Outputs from BMC Systems Biology
#16
of 1,143 outputs
Outputs of similar age
#22,951
of 301,315 outputs
Outputs of similar age from BMC Systems Biology
#1
of 16 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,143 research outputs from this source. They receive a mean Attention Score of 3.6. This one has done particularly well, scoring higher than 98% 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 301,315 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 16 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 93% of its contemporaries.