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. Areas covered: The authors provide an overview of the historical and ongoing issues in modeling and data analysis related to gonadotropin secretion in the field of both physiology and neuroendocrinology. They mention the different mathematical formalisms involved, their interest and limits. They also discuss open statistical questions in signal analysis associated with key endocrine issues and review recent advances in the modeling of the intracellular pathways activated by gonadotropins, which yields promising development for innovative approaches in drug discovery. Expert opinion: 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.

Genetic variants of G‐protein coupled receptors associated with pubertal disorders

“…Conceptual modeling approaches and multicellular systems biology may be useful for addressing these issues. 82 Lastly, not only pathogenic variants, but also common single‐nucleotide polymorphisms (SNPs) of the six GPCR genes may be of biological importance. For example, common SNPs in KISS1R or KISS1 were linked to the risk of early puberty.…”

“…Moreover, the signal network involving these GPCRs and other puberty‐associated molecules such as FGFR1, ANOS1, and MKRN3, needs to be clarified in the future. Conceptual modeling approaches and multicellular systems biology may be useful for addressing these issues 82 . Lastly, not only pathogenic variants, but also common single‐nucleotide polymorphisms (SNPs) of the six GPCR genes may be of biological importance.…”

Mathematical modeling approaches of cellular endocrinology within the hypothalamo-pituitary-gonadal axis

“…We will pay most of our attention to some dynamic features specific to the HPG axis, namely: the pulse-and-surge pattern of GnRH secretion, the decoding of GnRH pulse frequency by its target cells, FSH and LH signaling in gonadal cells, and the dynamics of gonadotropin-responsive cell populations in the gonads. We refer the reader to a former review (Yvinec et al, 2018) for other interesting biomathematical issues raised by the HPG, such as (i) the detection and possible reconstruction of pulsatile secretion events, and (ii) the modeling of hormonal blood levels in males or females (ovarian cycle). We also mention recent reviews dealing with related topics: mathematical neuroendocrinology (Bertram, 2015;Leng and MacGregor, 2018), electrophysiology in pituitary cells (Fletcher et al, 2018), and general modeling issues in endocrine systems (Zavala et al, 2019).…”

“…A step forward in that direction has been performed in (Margolskee and Selgrade, 2013). The authors have extended their substantive modeling framework for the ovarian cycle (see (Yvinec et al, 2018) for a review on ovarian cycle modeling) to account for long term effects of the exhaustion of early growing follicles. The extended model can reproduce the continual drop in AMH, the decrease in inhibin B at mid-reproductive age, and the subsequent rise in FSH.…”

Mathematical modeling of ovarian follicle development: A population dynamics viewpoint

“…We will highlight how historical and recent modeling approaches have tackled these issues, what insights have been gained or can be expected in the future from the models, and which experimental data are or will be critical to ensure a mutual benefit from modeling and experimental studies. We refer the interested reader to [1,2] for a broader overview on mathematical models dedicated to the HPG axis.…”