Kolodkin2013 - Nuclear receptor-mediated cortisol signalling network
Ontology highlight
ABSTRACT:
Kolodkin2013 - Nuclear receptor-mediated
cortisol signalling network
This model is described in the article:
Optimization of stress
response through the nuclear receptor-mediated cortisol
signalling network.
Kolodkin A, Sahin N, Phillips A,
Hood SR, Bruggeman FJ, Westerhoff HV, Plant N.
Nat Commun 2013; 4: 1792
Abstract:
It is an accepted paradigm that extended stress predisposes
an individual to pathophysiology. However, the biological
adaptations to minimize this risk are poorly understood. Using
a computational model based upon realistic kinetic parameters
we are able to reproduce the interaction of the stress hormone
cortisol with its two nuclear receptors, the high-affinity
glucocorticoid receptor and the low-affinity pregnane
X-receptor. We demonstrate that regulatory signals between
these two nuclear receptors are necessary to optimize the
body's response to stress episodes, attenuating both the
magnitude and duration of the biological response. In addition,
we predict that the activation of pregnane X-receptor by
multiple, low-affinity endobiotic ligands is necessary for the
significant pregnane X-receptor-mediated transcriptional
response observed following stress episodes. This integration
allows responses mediated through both the high and
low-affinity nuclear receptors, which we predict is an
important strategy to minimize the risk of disease from chronic
stress.
This model is hosted on
BioModels Database
and identified by:
BIOMD0000000576.
To cite BioModels Database, please use:
BioModels Database:
An enhanced, curated and annotated resource for published
quantitative kinetic models.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
SUBMITTER: Nilgun Sahin
PROVIDER: BIOMD0000000576 | BioModels | 2024-09-02
REPOSITORIES: BioModels
ACCESS DATA