“…As a result, a limitation of our model is that the circadian dynamics of CRH and ACTH are outside the dynamic range that is physiologically observed for these hormones. While such a limitation has been observed for a number of other published mathematical models 33,34 of the HPA axis, we expect that the key qualitative relationships described by our subsequent simulations will be maintained, should future models more quantitatively capture the dynamics of the other HPA axis mediators.Figure 1Schematic depiction of the modeled HPA axis network. The feedforward adrenal sensitivity, (k p3 ; green) and the hypothalamic negative feedback (K p1 ; red) and pituitary negative feedback (K p2 ; red) account for regulatory variability in the system.…”
Section: Methodsmentioning
confidence: 92%
“…Glucocorticoids have also been shown to have complex feedback effects on both peripheral clocks (including the adrenal peripheral clock) and central circadian clocks, thus adding further complexity to the circadian dynamics of glucocorticoids 85,86 . Nonetheless, it has been shown that many of the essential properties of glucocorticoid rhythms such as the observed temporal dependence of the stress response, amplitude behavior and entrainment properties can be explained using limit cycle oscillators as model systems 34,35,87,88 . In this sense, we suggest that many of the features of our simulation results might still be preserved even after separately accounting for these influences.…”
Section: Discussionmentioning
confidence: 99%
“…We aimed to characterize these putative regulatory adaptations of the system upon habituation to chronic stress exposure. Experiments show that chronic stress can have complex effects on the properties of the HPA axis 34 . We simplify the effects of chronic stress on the HPA axis by lumping its influence in a single term, the zero-order rate constant for CRH synthesis, , representative of the central activation of the HPA axis.…”
The hypothalamic-pituitary-adrenal (HPA) axis orchestrates the physiological response to unpredictable acute stressors. Moreover, the HPA axis exhibits prominent circadian activity and synchronizes peripheral circadian clocks to daily environmental cycles, thereby promoting homeostasis. Persistent disruption of homeostatic glucocorticoid circadian rhythmicity due to chronic stress exposure is correlated with the incidence of various pathological conditions including depression, diabetes and cancer. Allostatic habituation of the HPA axis, such that glucocorticoid levels retain homeostatic levels upon chronic exposure to stress, can therefore confer fitness advantages by preventing the sustained dysregulation of glucocorticoid-responsive signaling pathways. However, such allostatic adaptation results in a physiological cost (allostatic load) that might impair the homeostatic stress-responsive and synchronizing functions of the HPA axis. We use mathematical modeling to characterize specific chronic stress-induced allostatic adaptations in the HPA network. We predict the existence of multiple individualized regulatory strategies enabling the maintenance of homeostatic glucocorticoid rhythms, while allowing for flexible HPA response characteristics. We show that this regulatory variability produces a trade-off between the stress-responsive and time-keeping properties of the HPA axis. Finally, allostatic regulatory adaptations are predicted to cause a time-of-day dependent sensitization of the acute stress response and impair the entrainability of the HPA axis.
“…As a result, a limitation of our model is that the circadian dynamics of CRH and ACTH are outside the dynamic range that is physiologically observed for these hormones. While such a limitation has been observed for a number of other published mathematical models 33,34 of the HPA axis, we expect that the key qualitative relationships described by our subsequent simulations will be maintained, should future models more quantitatively capture the dynamics of the other HPA axis mediators.Figure 1Schematic depiction of the modeled HPA axis network. The feedforward adrenal sensitivity, (k p3 ; green) and the hypothalamic negative feedback (K p1 ; red) and pituitary negative feedback (K p2 ; red) account for regulatory variability in the system.…”
Section: Methodsmentioning
confidence: 92%
“…Glucocorticoids have also been shown to have complex feedback effects on both peripheral clocks (including the adrenal peripheral clock) and central circadian clocks, thus adding further complexity to the circadian dynamics of glucocorticoids 85,86 . Nonetheless, it has been shown that many of the essential properties of glucocorticoid rhythms such as the observed temporal dependence of the stress response, amplitude behavior and entrainment properties can be explained using limit cycle oscillators as model systems 34,35,87,88 . In this sense, we suggest that many of the features of our simulation results might still be preserved even after separately accounting for these influences.…”
Section: Discussionmentioning
confidence: 99%
“…We aimed to characterize these putative regulatory adaptations of the system upon habituation to chronic stress exposure. Experiments show that chronic stress can have complex effects on the properties of the HPA axis 34 . We simplify the effects of chronic stress on the HPA axis by lumping its influence in a single term, the zero-order rate constant for CRH synthesis, , representative of the central activation of the HPA axis.…”
The hypothalamic-pituitary-adrenal (HPA) axis orchestrates the physiological response to unpredictable acute stressors. Moreover, the HPA axis exhibits prominent circadian activity and synchronizes peripheral circadian clocks to daily environmental cycles, thereby promoting homeostasis. Persistent disruption of homeostatic glucocorticoid circadian rhythmicity due to chronic stress exposure is correlated with the incidence of various pathological conditions including depression, diabetes and cancer. Allostatic habituation of the HPA axis, such that glucocorticoid levels retain homeostatic levels upon chronic exposure to stress, can therefore confer fitness advantages by preventing the sustained dysregulation of glucocorticoid-responsive signaling pathways. However, such allostatic adaptation results in a physiological cost (allostatic load) that might impair the homeostatic stress-responsive and synchronizing functions of the HPA axis. We use mathematical modeling to characterize specific chronic stress-induced allostatic adaptations in the HPA network. We predict the existence of multiple individualized regulatory strategies enabling the maintenance of homeostatic glucocorticoid rhythms, while allowing for flexible HPA response characteristics. We show that this regulatory variability produces a trade-off between the stress-responsive and time-keeping properties of the HPA axis. Finally, allostatic regulatory adaptations are predicted to cause a time-of-day dependent sensitization of the acute stress response and impair the entrainability of the HPA axis.
“…We suggest that insights gathered from models of cellular stress could be usefully applied to other biological systems, including animals with complex nervous systems. For example, fairly sophisticated mathematical models of the HPA axis in humans and rodents have been developed and refined (Stanojevi c et al 2018). These models can be used to simulate the effect of challenges of variable intensity and that of repeated challenges over time, butto our knowledge-have never been employed to explore the dynamics of conditioning.…”
Section: Hormesis and Conditioningmentioning
confidence: 99%
“…Of course, the limitations of pure feedback control can be partially overcome by adding feedforward components to the system (Csete and Doyle 2002); however, this entails new points of fragility (e.g., sensitivity to prediction errors), as well as the additional costs of building and maintaining a more complex system. Interestingly, mathematical treatments of the HPA axis have dealt extensively with issues of dynamic stability (Savi c 2008;Stanojevi c et al 2018), but have not explicitly considered the role of robustness-fragility trade-offs in the design of the system. Fig.…”
The term "stress" is used to capture important phenomena at multiple levels of biological organization, but finding a general and rigorous definition of the concept has proven challenging. Current models in the behavioral literature emphasize the cognitive aspects of stress, which is said to occur when threats to the organism are perceived as uncontrollable and/or unpredictable. Here we adopt the perspective of systems biology and take a step toward a general definition of stress by unpacking the concept in light of control theory. Our goal is to clarify the concept so as to facilitate integrative research and formal analysis. We argue that stress occurs when a biological control system detects a failure to control a fitness-critical variable, which may be either internal or external to the organism. Biological control systems typically include both feedback (reactive, compensatory) and feedforward (predictive, anticipatory) components; their interplay accounts for the complex phenomenology of stress in living organisms. The simple and abstract definition we propose applies to animals, plants, and single cells, highlighting connections across levels of organization. In the final section of the paper we explore some extensions of our approach and suggest directions for future research. Specifically, we discuss the classic concepts of conditioning and hormesis and review relevant work on cellular stress responses; show how control theory suggests the existence of fundamental trade-offs in the design of stress responses; and point to potential insights into the effects of novel environmental conditions, including those resulting from anthropogenic change.
Recent studies have suggested that serotonin plays an essential role in many biological processes, such as increased locomotion [18] or blink rate, [19] in the parasympathetic division. Finally,
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