Elucidating the HPA axis stress response via computational inverse analysis
by
James Lu
Johann Radon Institute for Computational and Applied Mathematics (RICAM)
Coauthors: Clemens Zarzer, RICAM; Rainer Machne, Theoretical Biochemistry Group, University of Vienna; Gottfried Koehler, Max F. Perutz Laboratories, University of Vienna

The hypothalamic pituitary adrenal (HPA) axis represents a feedback system that plays an important role in maintaining the body homeostasis in response to various stresses. When stress is encountered, the hypothalamus releases the corticotropin releasing hormone (CRH) as a central neuro-transmitter in the HPA axis. There exist diverse differential equation models, which account for induction of ACTH synthesis in the pituitary by CRH, leading to adrenal activation and release of cortisol, which in turn inhibits the synthesis of ACTH.

Starting from these basic models, several additional feedback mechanisms could be included. One is the incorporation of an additional membrane bound glucocorticoid receptor (GR) in the inhibition of the ACTH release in the pituitary, responsible for fast feedback effects. Including such model extensions, computational inverse analysis is crucial in identifying the possible dynamical behaviors, such as oscillations,modulated by circadian rhythms and switching between multiple steady states.

To identify factors controlling the qualitative nature of the stress responses, we apply the method of inverse bifurcation analysis, using a

hierarchical identification strategy based upon a sparse-promoting regularization method. In particular, diseased phenotypes as represented

mathematically by the respective bifurcation diagrams are computationally mapped to the underlying regulation mechanisms. For instance,

the identified mechanisms underlying the delayed activation of the stress response include the degradation rate of GR as well as the rate of

up-regulation in the GR synthesis via its dimer. In addition to mapping diseased phenotypes to possible underlying mechanisms, inverse analysis can also point to mechanistic details of the model that should

be elucidated via experimental studies.

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Date received: May 14, 2008