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A Computational Model of Liver Iron Metabolism

Overview of attention for article published in PLoS Computational Biology, November 2013
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

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14 X users
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2 Google+ users

Citations

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Readers on

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50 Mendeley
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6 CiteULike
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Title
A Computational Model of Liver Iron Metabolism
Published in
PLoS Computational Biology, November 2013
DOI 10.1371/journal.pcbi.1003299
Pubmed ID
Authors

Simon Mitchell, Pedro Mendes

Abstract

Iron is essential for all known life due to its redox properties; however, these same properties can also lead to its toxicity in overload through the production of reactive oxygen species. Robust systemic and cellular control are required to maintain safe levels of iron, and the liver seems to be where this regulation is mainly located. Iron misregulation is implicated in many diseases, and as our understanding of iron metabolism improves, the list of iron-related disorders grows. Recent developments have resulted in greater knowledge of the fate of iron in the body and have led to a detailed map of its metabolism; however, a quantitative understanding at the systems level of how its components interact to produce tight regulation remains elusive. A mechanistic computational model of human liver iron metabolism, which includes the core regulatory components, is presented here. It was constructed based on known mechanisms of regulation and on their kinetic properties, obtained from several publications. The model was then quantitatively validated by comparing its results with previously published physiological data, and it is able to reproduce multiple experimental findings. A time course simulation following an oral dose of iron was compared to a clinical time course study and the simulation was found to recreate the dynamics and time scale of the systems response to iron challenge. A disease state simulation of haemochromatosis was created by altering a single reaction parameter that mimics a human haemochromatosis gene (HFE) mutation. The simulation provides a quantitative understanding of the liver iron overload that arises in this disease. This model supports and supplements understanding of the role of the liver as an iron sensor and provides a framework for further modelling, including simulations to identify valuable drug targets and design of experiments to improve further our knowledge of this system.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 6%
United States 2 4%
Poland 1 2%
Unknown 44 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 28%
Student > Ph. D. Student 12 24%
Student > Bachelor 7 14%
Other 4 8%
Lecturer > Senior Lecturer 3 6%
Other 7 14%
Unknown 3 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 38%
Medicine and Dentistry 10 20%
Biochemistry, Genetics and Molecular Biology 6 12%
Engineering 4 8%
Nursing and Health Professions 2 4%
Other 6 12%
Unknown 3 6%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 03 April 2014.
All research outputs
#3,590,649
of 25,707,225 outputs
Outputs from PLoS Computational Biology
#3,066
of 9,024 outputs
Outputs of similar age
#32,518
of 229,660 outputs
Outputs of similar age from PLoS Computational Biology
#50
of 150 outputs
Altmetric has tracked 25,707,225 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 9,024 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.3. This one has gotten more attention than average, scoring higher than 65% 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 229,660 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 85% of its contemporaries.
We're also able to compare this research output to 150 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 66% of its contemporaries.