Rhythms are intrinsic to endocrine systems, and disruption of these hormone oscillations occurs at very early stages of the disease. Because adrenal hormones are secreted with both circadian and ultradian periods, conventional single–time point measurements provide limited information about rhythmicity and, crucially, do not provide information during sleep, when many hormones fluctuate from nadir to peak concentrations. If blood sampling is attempted overnight, then this necessitates admission to a clinical research unit, can be stressful, and disturbs sleep. To overcome this problem and to measure free hormones within their target tissues, we used microdialysis, an ambulatory fraction collector, and liquid chromatography–tandem mass spectrometry to obtain high-resolution profiles of tissue adrenal steroids over 24 hours in 214 healthy volunteers. For validation, we compared tissue against plasma measurements in a further seven healthy volunteers. Sample collection from subcutaneous tissue was safe, well tolerated, and allowed most normal activities to continue. In addition to cortisol, we identified daily and ultradian variation in free cortisone, corticosterone, 18-hydroxycortisol, aldosterone, tetrahydrocortisol and allo-tetrahydrocortisol, and the presence of dehydroepiandrosterone sulfate. We used mathematical and computational methods to quantify the interindividual variability of hormones at different times of the day and develop “dynamic markers” of normality in healthy individuals stratified by sex, age, and body mass index. Our results provide insight into the dynamics of adrenal steroids in tissue in real-world settings and may serve as a normative reference for biomarkers of endocrine disorders (ULTRADIAN, NCT02934399).

Hormone rhythms are ubiquitous and essential to sustain normal physiological functions. Combined mathematical modelling and experimental approaches have shown that these rhythms result from regulatory processes occurring at multiple levels of organisation and require continuous dynamic equilibration, particularly in response to stimuli. We review how such an interdisciplinary approach has been successfully applied to unravel complex regulatory mechanisms in the metabolic, stress, and reproductive axes. We discuss how this strategy is likely to be instrumental for making progress in emerging areas such as chronobiology and network physiology. Ultimately, we envisage that the insight provided by mathematical models could lead to novel experimental tools able to continuously adapt parameters to gradual physiological changes and the design of clinical interventions to restore normal endocrine function.

Significance: Our ability to respond to stress depends on a remarkably dynamic process of hormone secretion. The rapid release of glucocorticoid hormones from the adrenal glands is critical to mount such an efficient response to stress, particularly during inflammation. Although many key factors involved in this process have been studied, the way in which these factors interact dynamically with one another to regulate glucocorticoid secretion has not been investigated. Here, we develop a mathematical model of the regulatory network controlling glucocorticoid synthesis and, by combining this with in vivo experiments, show how this network governs changes in adrenal responsiveness under basal unstressed physiological conditions and under exposure to inflammatory stress.

Neuroendocrine axes display a remarkable diversity of dynamic signaling processes relaying information between the brain, endocrine glands, and peripheral target tissues. These dynamic processes include oscillations, elastic responses to perturbations, and plastic long term changes observed from the cellular to the systems level. While small transient dynamic changes can be considered physiological, larger and longer disruptions are common in pathological scenarios involving more than one neuroendocrine axes, suggesting that a robust control of hormone dynamics would require the coordination of multiple neuroendocrine clocks. The idea of apparently different axes being in fact exquisitely intertwined through neuroendocrine signals can be investigated in the regulation of stress and fertility. The stress response and the reproductive cycle are controlled by the Hypothalamic-Pituitary-Adrenal (HPA) axis and the Hypothalamic-Pituitary-Gonadal (HPG) axis, respectively. Despite the evidence surrounding the effects of stress on fertility, as well as of the reproductive cycle on stress hormone dynamics, there is a limited understanding on how perturbations in one neuroendocrine axis propagate to the other. We hypothesize that the links between stress and fertility can be better understood by considering the HPA and HPG axes as coupled systems. In this manuscript, we investigate neuroendocrine rhythms associated to the stress response and reproduction by mathematically modeling the HPA and HPG axes as a network of interlocked oscillators. We postulate a network architecture based on physiological data and use the model to predict responses to stress perturbations under different hormonal contexts: normal physiological, gonadectomy, hormone replacement with estradiol or corticosterone (CORT), and high excess CORT (hiCORT) similar to hypercortisolism in humans. We validate our model predictions against experiments in rodents, and show how the dynamic responses of these endocrine axes are consistent with our postulated network architecture. Importantly, our model also predicts the conditions that ensure robustness of fertility to stress perturbations, and how chronodisruptions in glucocorticoid hormones can affect the reproductive axis' ability to withstand stress. This insight is key to understand how chronodisruption leads to disease, and to design interventions to restore normal rhythmicity and health.

Stochastic models of reaction networks are widely used to depict gene expression dynamics. However, stochastic does not necessarily imply accurate, as subtle assumptions can yield erroneous results, masking key discrete effects. For instance, transcription and translation are not instantaneous processes—explicit delays separate their initiation from the appearance of their functional products. However, delays are often ignored in stochastic, single-gene expression models. By consequence, effects such as delay-induced stochastic oscillations at the single-cell level have remained relatively unexplored. Here, we present a systematic study of periodicity and multimodality in a simple gene circuit with negative feedback, analyzing the influence of negative feedback strength and transcriptional/translational delays on expression dynamics. We demonstrate that an oscillatory regime emerges through a Hopf bifurcation in both deterministic and stochastic frameworks. Of importance, a shift in the stochastic Hopf bifurcation evidences inaccuracies of the deterministic bifurcation analysis. Furthermore, noise fluctuations within stochastic oscillations decrease alongside increasing values of transcriptional delays and within a specific range of negative feedback strengths, whereas a strong feedback is associated with oscillations triggered by bursts. Finally, we demonstrate that explicitly accounting for delays increases the number of accessible states in the multimodal regime, and also introduces features typical of excitable systems