Molecular systems biology models of the post synaptic density.

Information processing in the nervous system takes place at the synapses between neurons and in particular is mediated by some of the largest protein complexes described in biology. We have applied systems biology approaches to the glutamatergic post synaptic density (PSD) which is clearly associated with cognitive processes and human brain diseases. We have initially focused on exploiting protein-protein interaction data within static interaction models. These scale well and can capture the organisation of the 1000s of different proteins that can be found in synapses. However, to gain a more realistic understanding of such large complexes and of their influence on biology one must model their dynamics, their interactions with the cellular environment, as well the side effect of activity on their structure, state, and subsequent responses (e.g. through local translational control).

We next applied a stochastic calculus of domain binding provided by the rule-based modelling approach (Kappa) to formalize the highly combinatorial signalling pathway in PSD and performed numerical analysis of the relative distribution of protein complexes and their sizes at steady state. We find that this approach allows us to model, in a much more biologically plausible manner, the molecular interactions at synapses. This modelling approach allows us to study the effect of different perturbations (mutated polypeptides, protein splice variants, etc) on structure and relative stability of multi-protein complexes. Analysis of the basic topological properties of the protein networks obtained in simulation with respect to relevant physiological phenotypes provides a direct link between them. For example we can use these models to predict the impact of genetic disruption on the availability of transmitter receptors - in other words we can use this approach to develop predictive models that link from molecular genetics through to physiological properties of synapses.