Evolution of the Cognitive Proteome: From Static to Dynamic Network Models

Conference paper
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 736)


Integrative analysis of the neuronal synapse proteome has uncovered an evolutionarily conserved signalling complex that underpins the cognitive capabilities of the brain. Highly dynamic, cell type specific and intricately regulated, the synaptic proteome presents many challenges to systems biology approaches, yet this is likely to be the best route to unlock a new generation of neuroscience research and CNS drug development that society so urgently demands. Most systems biology approaches today have focussed on exploiting protein–protein interaction data to their fullest extent within static interaction models. These have revealed structure–function relationships within the protein network, uncovered new candidate genes for genetic studies and drug research and development and finally provided a means to study the evolution of the system. The rapid maturation of medium and high-throughput biochemical technologies means that dissecting the synapse proteome’s dynamic complexity is fast becoming a reality. Here we look at these new challenges and explore rule-based modelling as a basis for a new generation of synaptic models.


Neuronal Synapse Guanylate Kinase Interaction Logic Relative Steady State Protein Interaction Dataset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We acknowledge Anatoly Sorokin for help with simulation implementation. This work has made use of the resources provided by the Edinburgh Compute and Data Facility (ECDF). (http://www.ecdf.ed.ac.uk/). The ECDF is partially supported by the eDIKT initiative (http://www.edikt.org.uk). The research leading to these results has received funding from the European Union Seventh Framework Programme under grant agreement nos. HEALTH-F2–2009–241498 (“EUROSPIN” project) and HEALTH-F2–2009–242167 (“SynSys-project”).


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.School of InformaticsUniversity of EdinburghEdinburghUK

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