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Integrating Cellular Metabolism into a Multiscale Whole-Body Model

Overview of attention for article published in PLoS Computational Biology, October 2012
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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 (87th percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

Mentioned by

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17 X users
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1 Facebook page
linkedin
1 LinkedIn user

Citations

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115 Dimensions

Readers on

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264 Mendeley
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8 CiteULike
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Title
Integrating Cellular Metabolism into a Multiscale Whole-Body Model
Published in
PLoS Computational Biology, October 2012
DOI 10.1371/journal.pcbi.1002750
Pubmed ID
Authors

Markus Krauss, Stephan Schaller, Steffen Borchers, Rolf Findeisen, Jörg Lippert, Lars Kuepfer

Abstract

Cellular metabolism continuously processes an enormous range of external compounds into endogenous metabolites and is as such a key element in human physiology. The multifaceted physiological role of the metabolic network fulfilling the catalytic conversions can only be fully understood from a whole-body perspective where the causal interplay of the metabolic states of individual cells, the surrounding tissue and the whole organism are simultaneously considered. We here present an approach relying on dynamic flux balance analysis that allows the integration of metabolic networks at the cellular scale into standardized physiologically-based pharmacokinetic models at the whole-body level. To evaluate our approach we integrated a genome-scale network reconstruction of a human hepatocyte into the liver tissue of a physiologically-based pharmacokinetic model of a human adult. The resulting multiscale model was used to investigate hyperuricemia therapy, ammonia detoxification and paracetamol-induced toxication at a systems level. The specific models simultaneously integrate multiple layers of biological organization and offer mechanistic insights into pathology and medication. The approach presented may in future support a mechanistic understanding in diagnostics and drug development.

X Demographics

X Demographics

The data shown below were collected from the profiles of 17 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 264 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 2%
United Kingdom 4 2%
Italy 2 <1%
Netherlands 2 <1%
Japan 2 <1%
Norway 1 <1%
Finland 1 <1%
Switzerland 1 <1%
Germany 1 <1%
Other 7 3%
Unknown 237 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 78 30%
Student > Ph. D. Student 67 25%
Student > Master 22 8%
Professor > Associate Professor 16 6%
Student > Bachelor 14 5%
Other 44 17%
Unknown 23 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 100 38%
Biochemistry, Genetics and Molecular Biology 25 9%
Engineering 21 8%
Computer Science 18 7%
Medicine and Dentistry 17 6%
Other 44 17%
Unknown 39 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 28 April 2017.
All research outputs
#3,397,519
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#3,000
of 8,964 outputs
Outputs of similar age
#24,746
of 202,237 outputs
Outputs of similar age from PLoS Computational Biology
#31
of 113 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,964 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 66% 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 202,237 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 113 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.