Nonlinear feedback loops inherent to neuroendocrine systems are among the most important information flows at the organism level. They support high sensitivity and responsiveness of the living beings to external perturbations. Moreover, nonlinear feedback loops enable efficient control over dynamic physiological states. Often, they can be recognized through emergence of various dynamic phenomena, such as biological rhythmicity. Typical examples of such neuroendocrine systems are the hypothalamic-pituitary-adrenal (HPA) axis and the hypothalamic–pituitary–thyroid (HPT) axis. They are characterized by rhythmic dynamics with two characteristic periods, circadian (~ 24 h) and ultradian (20 min – 120 min), which allows living organisms to quickly adjust their neuroendocrine activity to fluctuations in their surroundings and/or their internal physiology.
We focus our research on mechanistic modelling of biochemical transformations that underlay complex neuroendocrine networks. Thus far, we have developed several variants of low-dimensional and extended models for the HPA axis, as well as, one medium scale model of the HPT axis. Both of them are assembled by combinations of the pseudo-reaction steps, describing in essence the information flow through the network of chemical transformations. Their role in physiological system is to maintain basal levels of hormone concentrations, and enable their functionally reasonable change when some need emerges. Our models enable one to emulate in numerical simulations changes in blood level of relevant hormones that constitute the HPA or HPT axis (Jelić et al. 2005, Marković et al. 2011, Čupić et al. 2017, Kolar-Anić et al. 2023).
The high predictive value of our models paves the way for their use in medical diagnostics of neuroendocrine diseases and for more efficient corticosteroid treatment that is applied in various illnesses, by harnessing the power of the underlying nonlinear feedback loops to the dosage of corticosteroid drugs could be significantly decreased, while preserving their efficacy. We pay special attention to the Stoichiometric Network Analysis of reaction network models to identify conditions ensuring the existence of unstable steady states, and in particular, Hopf bifurcation as a most plausible path leading to the oscillatory dynamics. The simplest way to use this template is to replace the text in this file with your own words using the styles provided as far as possible
