Multiscale mathematical modeling of the hypothalamo-pituitary-gonadal axis

Abstract

Although the fields of systems and integrative biology are in full expansion, few teams are involved worldwide into the study of reproductive function from the mathematical modeling viewpoint. This may be due to the fact that the reproductive function is not compulsory for individual organism survival, even if it is for species survival. Alternatively, the complexity of reproductive physiology may be discouraging. Indeed, the hypothalamo-pituitary-gonadal (HPG) axis involves not only several organs and tissues but also intricate time (from the neuronal millisecond timescale to circannual rhythmicity) and space (from molecules to organs) scales. Yet, mathematical modeling, and especially multiscale modeling, can renew our approaches of the molecular, cellular, and physiological processes underlying the control of reproductive functions. In turn, the remarkable dynamic features exhibited by the HPG axis raise intriguing and challenging questions to modelers and applied mathematicians. In this article, we draw a panoramic review of some mathematical models designed in the framework of the female HPG, with a special focus on the gonadal and central control of follicular development. On the gonadal side, the modeling of follicular development calls to the generic formalism of structured cell populations, that allows one to make mechanistic links between the control of cell fate (proliferation, differentiation, or apoptosis) and that of the follicle fate (ovulation or degeneration) or to investigate how the functional interactions between the oocyte and its surrounding cells shape the follicle morphogenesis. On the central, mainly hypothalamic side, models based on dynamical systems with multiple timescales allow one to represent within a single framework both the pulsatile and surge patterns of the neurohormone GnRH. Beyond their interest in basic research investigations, mathematical models can also be at the source of useful tools to study the encoding and decoding of the (neuro-) hormonal signals at play within the HPG axis and detect complex, possibly hidden rhythms, in experimental time series.

Transcriptomic analyses reveal tissue-specific selection on genes related to apoptotic processes in the subterranean rodent,Ctenomys sociabilis

“…The hypothalamus has been implicated in multiple critical signaling pathways, such as the Hypothalamic-Pituitary-Adrenal (stress) and Hypothalamic-Pituitary-Gonadal (reproductive) axes in vertebrates (Hall et al, 2012; Clément, 2016). Transcripts that were uniquely abundant in the hypothalamus tended to be directly involved in downstream signaling of activities such as feeding and parental or sexual behaviors (Insulin Receptor Substrate 4: IRS4, TPM= 31.63) as well as formation of the diencephalon and prethalamic brain region (FEZ Family Zinc Finger 1: FEZF1, TPM= 24.65) (Numan & Russell, 1999; Shimizu & Hibi, 2009).…”

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

“…From the HPG viewpoint, the selection of the ovulatory follicle(s) is one of -if not the -most integrative process (Clément, 2016): (i) it is the endpoint of the several-month process of follicle morphogenesis, (ii) it involves a tightly coordinated endocrine sequence : drop in FSH due to the inhibin and estradiol mediated feedback onto the pituitary gland, increase in GnRH frequency and triggering of the GnRH surge due to estradiol feedback onto the hypothalamus and estradiol priming on the pituitary level.…”

Bridging the Data Gap From in vitro Toxicity Testing to Chemical Safety Assessment Through Computational Modeling

“…Bois et al has recently linked ToxCast aromatase inhibition data to a menstrual cycle model to predict cycle length changes in women for chemical mixtures ( 114 ). Therefore, for the risk assessment of endocrine disruptors, we should aim to model endocrine systems by integrating different biological scales of the relevant AOPs and utilize these models to predict in vivo effects by taking in vitro toxicity pathway perturbation data ( 115 ). Developing these quantitative AOP models capable of risk prediction will aid regulatory decisions ( 116 ).…”