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Advances in computational modeling approaches of pituitary gonadotropin signaling

Abstract : Pituitary gonadotropins play an essential and pivotal role in the control of human and animal reproduction within the hypothalamic-pituitary-gonadal (HPG) axis. The computational modeling of pituitary gonadotropin signaling encompasses phenomena of different natures such as the dynamic encoding of gonadotropin secretion, and the intracellular cascades triggered by gonadotropin binding to their cognate receptors, resulting in a variety of biological outcomes. We overview historical and ongoing issues in modeling and data analysis related to gonadotropin secretion in the field of both physiology and neuro-endocrinology. We mention the different mathematical formalisms involved, their interest and limits. We discuss open statistical questions in signal analysis associated with key endocrine issues. We also review recent advances in the modeling of the intracellular pathways activated by gonadotropins, which yields promising development for innovative approaches in drug discovery. The greatest challenge to be tackled in computational modeling of pituitary gonadotropin signaling is the embedding of gonadotropin signaling within its natural multi-scale environment, from the single cell level, to the organic and whole HPG level. The development of modeling approaches of G protein-coupled receptor signaling, together with multicellular systems biology may lead to unexampled mechanistic understanding with critical expected fallouts in the therapeutic management of reproduction.
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Déposant : Romain Yvinec <>
Soumis le : mercredi 7 novembre 2018 - 09:40:56
Dernière modification le : jeudi 24 septembre 2020 - 16:00:37
Archivage à long terme le : : vendredi 8 février 2019 - 12:32:44



Romain Yvinec, Pascale Crepieux, Eric Reiter, Anne Poupon, Frédérique Clément. Advances in computational modeling approaches of pituitary gonadotropin signaling. Expert Opinion on Drug Discovery, Informa Healthcare, 2018, 13 (9), pp.799-813. ⟨10.1080/17460441.2018.1501025⟩. ⟨hal-01913612⟩



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