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Dynamic transitions in a model of the hypothalamic-pituitary-adrenal axis

Overview of attention for article published in Chaos, March 2016
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Title
Dynamic transitions in a model of the hypothalamic-pituitary-adrenal axis
Published in
Chaos, March 2016
DOI 10.1063/1.4944040
Pubmed ID
Authors

Željko Čupić, Vladimir M. Marković, Stevan Maćešić, Ana Stanojević, Svetozar Damjanović, Vladana Vukojević, Ljiljana Kolar-Anić

Abstract

Dynamic properties of a nonlinear five-dimensional stoichiometric model of the hypothalamic-pituitary-adrenal (HPA) axis were systematically investigated. Conditions under which qualitative transitions between dynamic states occur are determined by independently varying the rate constants of all reactions that constitute the model. Bifurcation types were further characterized using continuation algorithms and scale factor methods. Regions of bistability and transitions through supercritical Andronov-Hopf and saddle loop bifurcations were identified. Dynamic state analysis predicts that the HPA axis operates under basal (healthy) physiological conditions close to an Andronov-Hopf bifurcation. Dynamic properties of the stress-control axis have not been characterized experimentally, but modelling suggests that the proximity to a supercritical Andronov-Hopf bifurcation can give the HPA axis both, flexibility to respond to external stimuli and adjust to new conditions and stability, i.e., the capacity to return to the original dynamic state afterwards, which is essential for maintaining homeostasis. The analysis presented here reflects the properties of a low-dimensional model that succinctly describes neurochemical transformations underlying the HPA axis. However, the model accounts correctly for a number of experimentally observed properties of the stress-response axis. We therefore regard that the presented analysis is meaningful, showing how in silico investigations can be used to guide the experimentalists in understanding how the HPA axis activity changes under chronic disease and/or specific pharmacological manipulations.

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Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 40%
Professor 2 10%
Student > Master 2 10%
Student > Ph. D. Student 1 5%
Other 1 5%
Other 2 10%
Unknown 4 20%
Readers by discipline Count As %
Psychology 4 20%
Mathematics 3 15%
Medicine and Dentistry 2 10%
Biochemistry, Genetics and Molecular Biology 1 5%
Social Sciences 1 5%
Other 3 15%
Unknown 6 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 23 March 2016.
All research outputs
#22,759,452
of 25,373,627 outputs
Outputs from Chaos
#2,197
of 3,085 outputs
Outputs of similar age
#271,906
of 314,789 outputs
Outputs of similar age from Chaos
#15
of 47 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,085 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 47 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.