Functional regulation of endocrine secretory dynamics is known to be dependent on complex networks of physiological interactions. As such, reductionist methods of studying isolated, component elements involved in regulation of hormone secretion begin to encounter limitations when seeking an understanding of overall systems-level properties.

A program of computer-based modeling, in terms of complete networks of known interactions for a number of endocrine axes, has been developed to more directly and more deeply begin to probe the emergent properties and behavior exhibited in settings of dynamic endocrine secretory regulation.

The growth hormone axis is being studied by way of a deterministic network model of nonlinear ordinary differential equations in which interactions between hypothalamic growth hormone-releasing hormone, hypothalamic somatostatin, and pituitary growth hormone (as well as systemic insulin-like growth factor I) are accounted for in a series of positive and negative feedback and feedforward control loops. The objectives of this undertaking are to develop an understanding of how growth hormone secretory dynamics are regulated in male and female rat models, with extension to the male and female human.

Another endocrine axis to which attention is currently being directed is the human male luteinizing hormone-testosterone axis, particularly in relation to its functional decline with aging. The approach to this axis is to model in the context of stochastic differential equations, again accounting for the known physiological interactions in the model structure.

Faculty associated with this research include:

Michael L. Johnson

Leon S. Farhi

Boris P. Kovatchev

Michael O. Thorner