Two grand challenges have been declared as premier goals of computational systems
biology. The first is the discovery of network motifs and design principles that help
us understand and rationalize why biological systems are organized in the manner we
encounter them rather than in a different fashion. The second goal is the development
of computational models supporting the investigation of complex systems, in particular,
as simulation platforms in personalized medicine and predictive health. Interestingly,
most published systems models in biology contain between a handful and a few dozen
variables. They are usually too complicated for systemic analyses of organizing principles,
but they are at the same time too coarse to allow reliable simulations of diseases.
While it may thus appear that the modeling efforts of the past have missed the declared
targets of systems biology, we argue in this article that midsized mesoscopic models are excellent starting points for pursuing both goals in computational systems
biology.
Key words
computational systems biology - design principle - dopamine - mesoscopic model - network
motif - operating principle - Parkinson’s disease - schizophrenia
