Evidence map›Paper›PMID 30016990›Full record

ArticleBMC systems biology2018

High-fidelity discrete modeling of the HPA axis: a study of regulatory plasticity in biology.

Hooman Sedghamiz, Matthew Morris, Travis J A Craddock, Darrell Whitley, Gordon Broderick

Abstract read
In one paragraph

Article in BMC systems biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Hooman SedghamizCenter for Clinical Systems Biology, Rochester General Hospital, 1425 Portland Ave, Rochester, 14621, US.
Matthew MorrisCenter for Clinical Systems Biology, Rochester General Hospital, 1425 Portland Ave, Rochester, 14621, US.
Travis J A CraddockInstitute for Neuro Immune Medicine, Nova Southeastern University, 8501 SW 124th Avenue, Davie, 33183, US.
Darrell WhitleySchool of Computer Science, Colorado State University, University Ave, Fort Collins, 80521, US.
Gordon BroderickCenter for Clinical Systems Biology, Rochester General Hospital, 1425 Portland Ave, Rochester, 14621, US. Gordon.Broderick@rochesterregional.org.ORCID 0000-0002-3588-8252

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe hypothalamic-pituitary-adrenal (HPA) axis is a central regulator of stress response and its dysfunction has been associated with a broad range of complex illnesses including Gulf War Illness (GWI) and Chronic Fatigue Syndrome (CFS). Though classical mathematical approaches have been used to model HPA function in isolation, its broad regulatory interactions with immune and central nervous function are such that the biological fidelity of simulations is undermined by the limited availability of reliable parameter estimates.

methodHere we introduce and apply a generalized discrete formalism to recover multiple stable regulatory programs of the HPA axis using little more than connectivity between physiological components. This simple discrete model captures cyclic attractors such as the circadian rhythm by applying generic constraints to a minimal parameter set; this is distinct from Ordinary Differential Equation (ODE) models, which require broad and precise parameter sets. Parameter tuning is accomplished by decomposition of the overall regulatory network into isolated sub-networks that support cyclic attractors. Network behavior is simulated using a novel asynchronous updating scheme that enforces priority with memory within and between physiological compartments.

resultsConsistent with much more complex conventional models of the HPA axis, this parsimonious framework supports two cyclic attractors, governed by higher and lower levels of cortisol respectively. Importantly, results suggest that stress may remodel the stability landscape of this system, favoring migration from one stable circadian cycle to the other. Access to each regime is dependent on HPA axis tone, captured here by the tunable parameters of the multi-valued logic. Likewise, an idealized glucocorticoid receptor blocker alters the regulatory topology such that maintenance of persistently low cortisol levels is rendered unstable, favoring a return to normal circadian oscillation in both cortisol and glucocorticoid receptor expression.

conclusionThese results emphasize the significance of regulatory connectivity alone and how regulatory plasticity may be explored using simple discrete logic and minimal data compared to conventional methods.

Indexed as

Models, BiologicalAdrenal GlandsHypothalamo-Hypophyseal SystemStochastic Processesbiological regulatory feedbackHPA axisHPA axis plasticityLogical ModelingMulti-valued discrete simulationRegulatory stability

Identifiers

PMID30016990
PMCPMC6050677

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.