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Rule-Based Cell Systems Model of Aging using Feedback Loop Motifs Mediated by Stress Responses

Overview of attention for article published in PLoS Computational Biology, June 2010
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  • Above-average Attention Score compared to outputs of the same age (64th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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1 X user
wikipedia
2 Wikipedia pages

Citations

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47 Dimensions

Readers on

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121 Mendeley
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Title
Rule-Based Cell Systems Model of Aging using Feedback Loop Motifs Mediated by Stress Responses
Published in
PLoS Computational Biology, June 2010
DOI 10.1371/journal.pcbi.1000820
Pubmed ID
Authors

Andres Kriete, William J. Bosl, Glenn Booker

Abstract

Investigating the complex systems dynamics of the aging process requires integration of a broad range of cellular processes describing damage and functional decline co-existing with adaptive and protective regulatory mechanisms. We evolve an integrated generic cell network to represent the connectivity of key cellular mechanisms structured into positive and negative feedback loop motifs centrally important for aging. The conceptual network is casted into a fuzzy-logic, hybrid-intelligent framework based on interaction rules assembled from a priori knowledge. Based upon a classical homeostatic representation of cellular energy metabolism, we first demonstrate how positive-feedback loops accelerate damage and decline consistent with a vicious cycle. This model is iteratively extended towards an adaptive response model by incorporating protective negative-feedback loop circuits. Time-lapse simulations of the adaptive response model uncover how transcriptional and translational changes, mediated by stress sensors NF-kappaB and mTOR, counteract accumulating damage and dysfunction by modulating mitochondrial respiration, metabolic fluxes, biosynthesis, and autophagy, crucial for cellular survival. The model allows consideration of lifespan optimization scenarios with respect to fitness criteria using a sensitivity analysis. Our work establishes a novel extendable and scalable computational approach capable to connect tractable molecular mechanisms with cellular network dynamics underlying the emerging aging phenotype.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 121 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 5%
United Kingdom 3 2%
Brazil 2 2%
Germany 2 2%
Mexico 1 <1%
Unknown 107 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 41 34%
Student > Ph. D. Student 25 21%
Professor > Associate Professor 14 12%
Student > Bachelor 9 7%
Student > Master 8 7%
Other 15 12%
Unknown 9 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 53 44%
Biochemistry, Genetics and Molecular Biology 18 15%
Medicine and Dentistry 15 12%
Computer Science 4 3%
Mathematics 4 3%
Other 13 11%
Unknown 14 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 12 April 2024.
All research outputs
#7,960,052
of 25,373,627 outputs
Outputs from PLoS Computational Biology
#5,295
of 8,960 outputs
Outputs of similar age
#35,559
of 103,848 outputs
Outputs of similar age from PLoS Computational Biology
#28
of 53 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 39th percentile – i.e., 39% of its peers scored the same or lower than it.
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 103,848 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.
We're also able to compare this research output to 53 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.