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Mathematical models in GnRH research

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 prov...

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Detalles Bibliográficos
Autores principales: Voliotis, Margaritis, Plain, Zoe, Li, Xiao Feng, McArdle, Craig A., O’Byrne, Kevin T., Tsaneva‐Atanasova, Krasimira
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9285519/
https://www.ncbi.nlm.nih.gov/pubmed/35080068
http://dx.doi.org/10.1111/jne.13085
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author Voliotis, Margaritis
Plain, Zoe
Li, Xiao Feng
McArdle, Craig A.
O’Byrne, Kevin T.
Tsaneva‐Atanasova, Krasimira
author_facet Voliotis, Margaritis
Plain, Zoe
Li, Xiao Feng
McArdle, Craig A.
O’Byrne, Kevin T.
Tsaneva‐Atanasova, Krasimira
author_sort Voliotis, Margaritis
collection PubMed
description 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.
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spelling pubmed-92855192022-07-18 Mathematical models in GnRH research Voliotis, Margaritis Plain, Zoe Li, Xiao Feng McArdle, Craig A. O’Byrne, Kevin T. Tsaneva‐Atanasova, Krasimira J Neuroendocrinol Invited Review 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. John Wiley and Sons Inc. 2022-01-25 2022-05 /pmc/articles/PMC9285519/ /pubmed/35080068 http://dx.doi.org/10.1111/jne.13085 Text en © 2021 The Authors. Journal of Neuroendocrinology published by John Wiley & Sons Ltd on behalf of British Society for Neuroendocrinology. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Invited Review
Voliotis, Margaritis
Plain, Zoe
Li, Xiao Feng
McArdle, Craig A.
O’Byrne, Kevin T.
Tsaneva‐Atanasova, Krasimira
Mathematical models in GnRH research
title Mathematical models in GnRH research
title_full Mathematical models in GnRH research
title_fullStr Mathematical models in GnRH research
title_full_unstemmed Mathematical models in GnRH research
title_short Mathematical models in GnRH research
title_sort mathematical models in gnrh research
topic Invited Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9285519/
https://www.ncbi.nlm.nih.gov/pubmed/35080068
http://dx.doi.org/10.1111/jne.13085
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