Endocrine Abstracts (2013) 31 APW1.1 | DOI: 10.1530/endoabs.31.APW1.1

Modelling neuroendocrine systems

Gareth Leng & Duncan MacGregor

University of Edinburgh, Edinburgh, UK.

In recent years, the increasing availability of massive computational capacity has reached what may be seen as a ‘tipping point’, bringing once unimaginable computational power into the lab. This is enabling models to be built, fit and refined during experiments, making predictive models that are powerful tools for hypothesis generation and testing. Neuroendocrine systems are at the forefront of these advances. Because of the exceptional opportunities that they offer for experimental intervention, they have long been prominent ‘model systems’ in neuroscience, now these model systems are the source of powerful computational models. The electrical activity of oxytocin and vasopressin cells of the hypothalamus can now be closely matched by computational models whose parameters are fit to the data by evolutionary algorithms as the data are collected. I will show how the synchronized bursting of oxytocin cells during suckling, which underlies pulsatile oxytocin secretion, can be understood through a network model of oxytocin cells, and how models can be used to explore the functional significance of the phasic firing patterns of vasopressin cells.


Rossoni E, Feng J, Tirozzi B, Brown D, Leng G & Moos F 2008 Emergent synchronous bursting of oxytocin neuronal network. PLoS Computl Biol 18;4 (7):e1000123.

1. Macgregor DJ & Leng G. 2012 Phasic firing in vasopressin cells: understanding its functional significance through computational models. PLoS Comput Biol. 8(10):e1002740. doi: 10.1371/journal.pcbi.1002740.

2. Tomaiuolo M, Bertram R, Leng G & Tabak J 2012 Models of electrical activity: calibration and prediction testing on the same cell. Biophysical Journal 103: 2021–2032

3. Leng G & Macgregor D 2008 Mathematical modelling in neuroendocrinology. J Neuroendocrinol 20: 713–718

Declaration of funding

This work was supported by The Wellcome Trust (grant number 083286/Z/07/A).

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