Almost all complex human diseases are context-dependent entities to which molecular components make a necessary, but only partial, contribution. This is particularly evident in psychiatric conditions such as schizophrenia and major depressive disorders. Here, classical analytical approaches based on reductionism lead to profound misconceptions of the actual nature of the problem. Consequently, a systems perspective may be the optimal method for approaching complex psychiatric diseases. However, attempting to productively apply systems principles to complex medical conditions is much more difficult than hitherto anticipated. Living systems are integrative and non-linear by nature and embody higher level functional principles that are not reducible to the molecular level. Furthermore, whereas systems biology functions on the basis of large data sets arising from highly targeted investigations upon homogeneous experimental material, systems medicine must proceed on the basis of existing, highly heterogeneous data. The challenge is therefore to assimilate a large, and often conflicting corpus of data to build and inform a systems-level model of the physiological alterations underlying the disorders while reaching beyond somatism (bottom-up approaches), which is provably largely insufficient to functionally explain multicellular living systems to a degree enabling informed therapeutic intervention. This paper factually documents how a modelling approach based on a combination of heuristics (top-down) and algorithmic (bottom-up) modelling strategies, together with the active participation of clinician networks can provide an effective roadmap to productively address psychiatric disorders at large, and schizophrenia in particular.
© Georg Thieme Verlag KG Stuttgart · New York.