What are the roadblocks?
Although computational systems neurobiology is still far ahead of other fields when it comes to multi-scale, multi-algorithms modelling, the coupling of signalling pathways, electrical dynamics and ionic diffusion is still infrequent . Even more serious is the fact that some crucial cellular functions or behaviours are barely considered at all when it come to quantitative modelling. Modifications of gene expression  and protein translation  have been largely studied in synaptic function and plasticity, or in the symptomatology of neuronal diseases . Due to the different time-scales involved, and the difficulty of building hybrid models able to provide continuous descriptions of electrical, metabolic and signalling events together with stochastic or even logical descriptions of gene regulatory networks , those aspects of neuronal physiology are mainly considered separately. Cell remodelling has also been generally ignored, whether at the level of the synapse, the spine or the neuronal process, despite an abundant literature showing its importance in neuronal function.
The recent availability of new types of quantitative data should help to expand the models in new directions. On the large-scale front, functional genomics approaches such as microarrays  or proteomics , but also systematic application of more classical approaches such as in situ hybridization  should make the models more accurately reflect brain function and dysfunction. Other cutting-edge technologies like single-particle tracking in living cells  will allow the development of more realistic models, and will enable the investigation of the role of micro-domains and supra-macromolecular complexes.