Quantitative prediction of vasopressin secretion using a computational population model of rat magnocellular neurons

Abstract

The goal of this study was to create a realistic and quantitative simulation of vasopressin (AVP) secretion under iso-osmotic and short-term challenged plasma osmolality. The relationship between AVP concentration ([AVP]) and plasma osmolality was computed using a sophisticated and integrated model that chronologically simulates (1) the overall firing rate of the hypothalamus' magnocellular neuronal (MCN) population, (2) the propagation of the spike activity down the axons, (3) the fatigue and facilitation mechanisms of AVP release at the axon terminals and (4) the [AVP] pharmacodynamics based on the trains of AVP release. This global simulation predicted that the differential MCN sensitivity to dynorphin would be the most critical mechanism underlying the individual variability of MCN firing behaviors (silence, irregular, phasic and continuous firing patterns). However, at the level of the MCN population, the simulation predicted that the dynorphin factor must be combined with the distribution of the resting membrane potentials among the MCNs to obtain a realistic overall firing rate in response to a change in osmolality. Moreover, taking advantage of the integrated model, the simulation predicted that the selective removal of the frequency-dependent facilitation of AVP secretion has a major impact on the overall [AVP]-to-osmolality relationship (mean absolute change of 2.59 pg/ml); the action potential propagation failure, while critical, has a smaller quantitative impact on the overall [AVP] (0.58 pg/ml). The present integrated model (from a single MCN to a quantitative plasma [AVP]) improves our knowledge of the mechanisms underlying overall MCN firing and AVP excitation-secretion coupling.

Spike Triggered Hormone Secretion in Vasopressin Cells; a Model Investigation of Mechanism and Heterogeneous Population Function

“…We are aware of only one previous published model of the secretion response in vasopressin neurons [39]. This used an ad hoc representation, based simply on a piecewise linear fit to experimental data [12], and it does not produce a response which is sensitive to spike patterning (differentiating phasic from regular stimulation), which is one of the key features of vasopressin cells.…”

Phasic Firing in Vasopressin Cells: Understanding Its Functional Significance through Computational Models

“…Nadeau et al [34] have recently extended their spiking model to include the vasopressin secretion response, and have used their model to explore what properties of the spiking mechanism might underlie the heterogeneity observed in the cell population. The core of their secretion model is based on an interpolation of in vitro data which demonstrated the frequency facilitation effect [9] , assuming that this is sufficient to also represent the fatigue effect on secretion.…”

Mathematical modelling of the oxytocin and vasopressin secretory system

“…Another earlier modelling attempt [ 14 ], instead of basing on the underlying mechanisms, directly simulates the frequency response profile, and the rate of propagation failure, as a function of spike interval. It matches vasopressin concentrations in set conditions but lacks flexibility and a close quantitative match to the experimental data.…”