Affective disorders as complex dynamic diseases--a perspective from systems biology

• Published in: Pharmacopsychiatry Vol. 44 Suppl 1; p. S2
• Main Authors: Tretter, F, Gebicke-Haerter, P J, an der Heiden, U, Rujescu, D, Mewes, H W, Turck, C W
• Format: Journal Article
• Language: English
• Published: Germany 01.05.2011
• Subjects: Brain - anatomy & histology; Brain - pathology; Brain - physiopathology; Computer Simulation; Depression - physiopathology; Depressive Disorder - pathology; Depressive Disorder - physiopathology; Disease; Humans; Hydrocortisone - metabolism; Mental Disorders - pathology; Mental Disorders - physiopathology; Models, Biological; Mood Disorders - metabolism; Mood Disorders - pathology; Mood Disorders - physiopathology; Neurobiology; Neuropsychiatry; Nonlinear Dynamics; Signal Transduction; Systems Biology
• ISSN: 1439-0795
• DOI: 10.1055/s-0031-1275278

Understanding mental disorders and their neurobiological basis encompasses the conceptual management of "complexity" and "dynamics". For example, affective disorders exhibit several fluctuating state variables on psychological and biological levels and data collected of these systems levels suggest quasi-chaotic periodicity leading to use concepts and tools of the mathematics of nonlinear dynamic systems. Regarding this, we demonstrate that the concept of "Dynamic Diseases" could be a fruitful way for theory and empirical research in neuropsychiatry. In a first step, as an example, we focus on the analysis of dynamic cortisol regulation that is important for understanding depressive disorders. In this case, our message is that extremely complex phenomena of a disease may be explained as resulting from perplexingly simple nonlinear interactions of a very small number of variables. Additionally, we propose that and how widely used complex circuit diagrams representing the macroanatomic structures and connectivities of the brain involved in major depression or other mental disorders may be "animated" by quantification, even by using expert-based estimations (dummy variables). This method of modeling allows to develop exploratory computer-based numerical models that encompass the option to explore the system by computer simulations (in-silico experiments). Also inter- and intracellular molecular networks involved in affective disorders could be modeled by this procedure. We want to stimulate future research in this theoretical context.