Evidence map›Paper›PMID 35080068›Full record

ReviewJournal of neuroendocrinology2022

Mathematical models in GnRH research.

Margaritis Voliotis, Zoe Plain, Xiao Feng Li, Craig A McArdle, Kevin T O'Byrne, Krasimira Tsaneva-Atanasova

Abstract readReview
In one paragraph

Review in Journal of neuroendocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Systems bioengineering approaches for developmental toxicology.Computational and structural biotechnology journal · 2023
    Review
  2. Mathematical models in GnRH research.Journal of neuroendocrinology · 2022
    Review
  3. Review
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

6 authors.

Margaritis VoliotisDepartment of Mathematics and Living Systems Institute, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK.ORCID 0000-0001-6488-7198
Zoe PlainDepartment of Mathematics and Living Systems Institute, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK.
Xiao Feng LiDepartment of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK.
Craig A McArdleLaboratories for Integrative Neuroscience and Endocrinology, School of Clinical Sciences, University of Bristol, Bristol, UK.ORCID 0000-0003-4836-5351
Kevin T O'ByrneDepartment of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK.
Krasimira Tsaneva-AtanasovaDepartment of Mathematics and Living Systems Institute, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mathematical modelling is an indispensable tool in modern biosciences, enabling quantitative analysis and integration of biological data, transparent formulation of our understanding of complex biological systems, and efficient experimental design based on model predictions. This review article provides an overview of the impact that mathematical models had on GnRH research. Indeed, over the last 20 years mathematical modelling has been used to describe and explore the physiology of the GnRH neuron, the mechanisms underlying GnRH pulsatile secretion, and GnRH signalling to the pituitary. Importantly, these models have contributed to GnRH research via novel hypotheses and predictions regarding the bursting behaviour of the GnRH neuron, the role of kisspeptin neurons in the emergence of pulsatile GnRH dynamics, and the decoding of GnRH signals by biochemical signalling networks. We envisage that with the advent of novel experimental technologies, mathematical modelling will have an even greater role to play in our endeavour to understand the complex spatiotemporal dynamics underlying the reproductive neuroendocrine system.

Indexed as

Gonadotropin-Releasing HormoneKisspeptinsModels, TheoreticalNeuronsReproductionGonadotropin-Releasing HormoneKisspeptinsbiophysical modellingGnRHmathematical modelling

Identifiers

PMID35080068
PMCPMC9285519

What OpenQuestion holds

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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.