Dynamical systems theory approach to neuropsychiatric diseases
Many common neuropsychiatric diseases, such as substance dependence/abuse, schizophrenia and manic depression, have similar dynamical profiles – they are characterized by a recurrent loss of normal behavioral patterns and appearance of episodes where involuntary and abnormal feelings and actions, such as alcohol/drug seeking and compulsory use, prevail. The underlying biochemical circuitry and time scales at which these changes occur are different for different diseases, but they may have in common the same type of qualitative deterministic changes in signaling dynamics, such as the transition through the same type of bifurcation [1-3].
Our research focuses on the hypothalamus-pituitary-adrenal (HPA) axis, a dynamical network that regulates the function of the neuroendocrine system. We construct stoichiometric models and use mathematical modeling to emulate changes in the concentration of steroid and peptide hormones that comprise the HPA axis [1-3]. Using approaches from dynamical systems theory, we study how qualitative changes in signaling dynamics occur via deterministic biochemical mechanisms. Our long-term goal is to use mathematical modeling as a tool for understanding the complex etiology of common neuropsychiatric diseases. Moreover, we would like to implement mathematical modeling for the development of individualized treatment protocols, where pharmacotherapy distribution is synchronized with the innate rhythmicity of HPA axis hormones in an individual to significantly shorten the duration of an acute episode in an individual and/or weaken its severity.
Predictive Modelling of the Hypothalamic-Pituitary-Adrenal (HPA) function. Dynamical Systems Theory Approach by Stoichiometric Network Analysis and Quenching Small Amplitude Oscillations Jelić S, Čupić Ž, Kolar-Anić Lj, Vukojević V. Int J Nonlin Sci Num, 2009, 10:1451-1472.