Modern high throughput technologies in biological science often create lists of interesting molecules. The challenge is to reconstruct a descriptive model from these lists that reflects the underlying biological processes as accurately as possible. Once we have such a model or network, what can we learn from it? Specifically, given that we are interested in some biological process associated with the model, what new properties can we predict and subsequently test? Here, we describe, at an introductory level, a range of bioinformatics techniques that can be systematically applied to proteomic datasets. When combined, these methods give us a global overview of the network and the properties of the proteins and their interactions. These properties can then be used to predict functional pathways within the network and to examine substructure. To illustrate the application of these methods, we draw upon our own work concerning a complex of 186?proteins found in neuronal synapses in mammals. The techniques discussed are generally applicable and could be used to examine lists of proteins involved with the biological response to electric or magnetic fields.