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Review and application of group theory to molecular systems biology

Overview of attention for article published in Theoretical Biology and Medical Modelling, June 2011
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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 (87th percentile)

Mentioned by

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7 X users
facebook
1 Facebook page
q&a
1 Q&A thread

Citations

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28 Dimensions

Readers on

mendeley
142 Mendeley
citeulike
1 CiteULike
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1 Connotea
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Title
Review and application of group theory to molecular systems biology
Published in
Theoretical Biology and Medical Modelling, June 2011
DOI 10.1186/1742-4682-8-21
Pubmed ID
Authors

Edward A Rietman, Robert L Karp, Jack A Tuszynski

Abstract

In this paper we provide a review of selected mathematical ideas that can help us better understand the boundary between living and non-living systems. We focus on group theory and abstract algebra applied to molecular systems biology. Throughout this paper we briefly describe possible open problems. In connection with the genetic code we propose that it may be possible to use perturbation theory to explore the adjacent possibilities in the 64-dimensional space-time manifold of the evolving genome. With regards to algebraic graph theory, there are several minor open problems we discuss. In relation to network dynamics and groupoid formalism we suggest that the network graph might not be the main focus for understanding the phenotype but rather the phase space of the network dynamics. We show a simple case of a C6 network and its phase space network. We envision that the molecular network of a cell is actually a complex network of hypercycles and feedback circuits that could be better represented in a higher-dimensional space. We conjecture that targeting nodes in the molecular network that have key roles in the phase space, as revealed by analysis of the automorphism decomposition, might be a better way to drug discovery and treatment of cancer.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 142 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 3 2%
United States 3 2%
United Kingdom 2 1%
Italy 1 <1%
Netherlands 1 <1%
Switzerland 1 <1%
Singapore 1 <1%
Canada 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 127 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 39 27%
Researcher 33 23%
Student > Bachelor 13 9%
Professor 11 8%
Professor > Associate Professor 11 8%
Other 21 15%
Unknown 14 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 43 30%
Biochemistry, Genetics and Molecular Biology 18 13%
Mathematics 14 10%
Engineering 13 9%
Physics and Astronomy 9 6%
Other 28 20%
Unknown 17 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 06 June 2022.
All research outputs
#4,681,278
of 25,121,016 outputs
Outputs from Theoretical Biology and Medical Modelling
#62
of 286 outputs
Outputs of similar age
#22,980
of 120,357 outputs
Outputs of similar age from Theoretical Biology and Medical Modelling
#2
of 8 outputs
Altmetric has tracked 25,121,016 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 286 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. 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 120,357 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 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.