1000 100 10 1 1 10 100 1000 k0 BMC Genomics 2005, 6:131 http://www.biomedcentral.com/1471-2164/6/131 FDiIgPubraese3d protein interaction network using matrix model DIP based protein interaction network using matrix model. Average value of k1 as a function of k0 displayed on a log.- log. scale for the global network (blue) and essential network (red); generated by applying the matrix model to determine pair- wise interactions from protein complex data. For both networks, the previously reported negative correlation reverts to a positive correlation, and the previously observed difference between the topologies of the two networks appears to have been lost. that allows us to define a set of pair-wise interactions for a protein complex whose topology is completely unknown. Given our results on the topology of the network, it is hard to believe that changes in the strength and polarity of the correlations observed, stem from some underlying biological process [14,15]. Rather, they are presumably the result of biases introduced either through experimen- tal methodologies or the choice of analysis technique. As an example, data from Y2H experiments show a signifi- cant asymmetry between the connectivities of baits and preys (i.e., the average connectivity of baits with at least one interaction is almost double the same quantity meas- ured for preys) [14]. This and other factors such as auto- activation and the presence of "sticky-proteins" [20], if not taken into account during network construction and analysis, could create the false impression of a negative correlation. In a similar way, it is obvious (from the vol- ume of published literature) that yeast essential genes have been subject to a greater degree of investigation than non-essential genes, and are therefore liable to have more documented interacting partners. It is possible that this bias is responsible for the appearance of the topologically distinct "essential" sub-network (first identified by Pereira-Leal and co-workers [15], and shown here in Fig- ure 1) because a more complete and "accurate" dataset is available for analysis. This is supported by the fact that this topology is only evident when multiple datasets (which, if taken individually, often show an apparent neg- ative correlation) are combined (Figure 4). Furthermore, we have shown that, by changing the modelling method- ology employed to derive pair-wise protein interactions, it is relatively easy to change the overall topology of the observed networks. Although the application of one model seems to indicate the global and essential networks Page 5 of 8 (page number not for citation purposes)