Cited by 31Stress Responses and CortisolAdrenal Hormones and DisordersGene Regulatory Network Analysis

Modelling cholesterol effects on the dynamics of the hypothalamic–pituitary–adrenal (HPA) axis

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Marković, Vladimir M., Čupić, Željko, Maćešić, Stevan, Stanojević, Ana, Vukojević, Vladana, Kolar-Anić, Ljiljana·2016·Mathematical Medicine and Biology

Abstract

A mathematical model of the hypothalamic-pituitary-adrenal (HPA) axis with cholesterol as a dynamical variable was derived to investigate the effects of cholesterol, the primary precursor of all steroid hormones, on the ultradian and circadian HPA axis activity. To develop the model, the parameter space was systematically examined by stoichiometric network analysis to identify conditions for ultradian oscillations, determine conditions under which dynamic transitions, i.e. bifurcations occur and identify bifurcation types. The bifurcations were further characterized using numerical simulations. Model predictions agree well with empirical findings reported in the literature, indicating that cholesterol levels may critically affect the global dynamics of the HPA axis. The proposed model provides a base for better understanding of experimental observations, it may be used as a tool for designing experiments and offers useful insights into the characteristics of basic dynamic regulatory mechanisms that, when impaired, may lead to the development of some modern-lifestyle-associated diseases.

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Crossref · 10.1093/imammb/dqu020

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74 of 74 references verified0 citation errors · 0 not checkable

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  • Values of the rate constants of reaction steps (R2.2), (R4), (R5.2), (R5.3) and (R12), associated with AVP effects, were derived from (39, 54, 59, 60) unless otherwise specified.

    “Values of the rate constants of reaction steps (R2.2), (R4), (R5.2), (R5.3) and (R12), associated with AVP effects were derived from (39, 54, 59, 60) unless otherwise specified.”

    From Influence of arginine vasopressin on the ultradian dynamics of Hypothalamic-Pituitary-Adrenal axis (2022) · cites this paper as [54]

    Section 2.1 (page unavailable) gives rate constants for R4 and R12, and Computational Details (page unavailable) says the constants were taken from previous work whenever possible. The copy does not identify AVP-associated steps R2.2, R5.2, or R5.3, so it supports only part of the attributed list. (LLM, conf=0.82)

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  • The response to perturbations is highly variable and depends strongly on the phase angle of the selected ultradian oscillation at which the first perturbation was applied; cortisol oscillation amplitude may decrease, increase or remain unaltered.

    “Moreover, as expected from our previous experience (54, 62), perturbations caused a highly variable response depending on the phase angle of the selected ultradian oscillation at which the first perturbation was applied.”

    From Influence of arginine vasopressin on the ultradian dynamics of Hypothalamic-Pituitary-Adrenal axis (2022) · cites this paper as [54]

    Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

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  • When k2.2, k4 and initial AVP concentration are all zero (the initial model), cortisol oscillation amplitudes do not depend on k5.3.

    “In the first case k2.2, k4 and initial concentration of AVP are all equal to zero. This corresponds to the initial model (54). Under these conditions, amplitudes do not depend on k5.3 and hence only one value of amplitude is given for each k2.1 value.”

    From Influence of arginine vasopressin on the ultradian dynamics of Hypothalamic-Pituitary-Adrenal axis (2022) · cites this paper as [54]

    Section 2.2 describes a cholesterol-regulated HPA model with rate constants k1–k13, and Section 3.2 discusses cortisol oscillation amplitude in relation to cholesterol. Searches for the claimed k2.2, k5.3 and AVP conditions found no passage addressing whether amplitude is independent of k5.3. (LLM, conf=0.78)

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Cites

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Research works printed but not linked: 4

  1. Čupić, Ž., Marković, V., Ivanović, A. & Kolar-Anić, Lj. (2011) Modeling of the complex nonlinear processes: Determination of the instability region by the stoichiometric network analysis. Mathematical Modelling (C. R. Brennan ed.). New York: Nova Science Publishers, Inc., pp. 111-178.
  2. Ingalls, B. P. (2013) Mathematical Modelling in Systems Biology: An Introduction, 1st edn. Cambridge, MA, USA: The MIT Press.
  3. Jelić, S., Čupić, Ž. & Kolar-Anić, Lj. (2008) Modelling of the hypothalamic-pituitary-adrenal system activity based on the stoichiometric analysis. New Research on Neurosecretory Systems (E. Romano & S. De Luca eds). New York: Nova Science Publishers, Inc., pp. 225-245.
  4. Miller, W. L. & Chrousos, G. P. (2001) The adrenal cortex. Endocrinology & Metabolism (P. Felig & L. Frohman eds). New York: McGraw-Hill, pp. 387-524.

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Authors
Marković, V. M., Čupić, Ž., Maćešić, S., Stanojević, A., Vukojević, V., & Kolar-Anić, L.
Affiliations
  • University of Belgrade (RS)
  • Karolinska Institutet (SE)
Corresponding author
Marković, Vladimir M.
Venue
Mathematical Medicine and Biology
Published
2014-10-20
Volume
33
Issue
1
Pages
1-28
ISSN
  • 1477-8599 (ISSN-L)
  • 1477-8602
OpenAlex
W2307321386
Cited by
31
References
74
Citations by year
2025: 5 · 2023: 4 · 2022: 2 · 2021: 1 · 2019: 2 · 2018: 4 · 2017: 5 · 2016: 8
FWCI
2.13
Type
journal article
Language
en
Open access
Locations
  • Mathematical Medicine and Biology A Journal of the IMA (publishedVersion)
  • PubMed (publishedVersion)
  • CER (University of Belgrade, Institute of Chemistry, Technology and Metallurgy) (submittedVersion, other-oa)
  • HighWire Press Open Archive (submittedVersion)
Funders
  • Karolinska Institutet
  • Knut och Alice Wallenbergs Stiftelse
  • Vetenskapsrådet
  • Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja (172015, 45001, grants 172015 and 45001, 172015 and 45001)
Topics
  • Life Sciences › Neuroscience › Behavioral Neuroscience › Stress Responses and Cortisol
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Adrenal Hormones and Disorders
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Keywords
hypothalamic-pituitary-adrenal axis, cholesterol, steroid hormones, ultradian oscillations, circadian rhythm, bifurcation, dynamical transition, stoichiometric network analysis, mathematical model
MeSH terms
Ultradian Rhythm, Cholesterol, Circadian Rhythm, Humans, Hypothalamo-Hypophyseal System, Models, Theoretical, Pituitary-Adrenal System

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