BMC Genomics 2005, 6:131 http://www.biomedcentral.com/1471-2164/6/131 scale-free property of these networks is thought to be of particular biological significance as it confers robustness to random node loss, allowing the network to maintain its overall integrity even when a significant number of nodes are removed [13]. The concept of network-mediated robustness appears to be reinforced by the presence of a correlation between the connectivity of neighbouring nodes within the network (a feature not observed in ran- dom networks) [14]. In the yeast protein-interaction net- work, the observed negative correlation between the connectivity of a protein and the average connectivity of its binding partners has been seen as a possible adaptation which allows the network to be resilient to the propaga- tion of deleterious perturbations [14]. Recently, Pereira- Leal and co-workers showed that this correlation is valid only for the yeast protein-interaction network as a whole, and that the network formed by the proteins essential for yeast growth has its own unique topological properties, including a very high degree of connectivity (97% of the proteins form a single distinct sub-network), which they postulate may have some implications for our under- standing of the network's evolution [15]. Protein interaction networks are generally described using a graph theoretical approach, in which proteins within the graph (nodes) are connected by undirected links (edges) if they are found to interact. While creating a representa- tion of the network is relatively straight forward, deciding what should be represented is often more difficult. Typi- cally, networks are generated using interactions derived from a plurality of different experimental types, which may include protein interactions identified in both indi- vidual small-scale studies and larger systematic genome- scale screens – such as those from yeast two-hybrid (Y2H) and affinity-purification experiments. More often than not, less thought than appropriate is given to how the interactions derived from these different systems have been, or should be, combined and the possible implica- tions that different methodologies for achieving this might have on the outcome of analyses. The issue of data handling is of particular importance in the study of protein interactions derived from purified protein complexes. For any given purified complex that results from a FLAG or TAP tag-based experiment, it is very unlikely that every "prey" protein identified within the complex interacts directly with the "bait" protein. Other proteins or molecules (such as RNA) present within the mixture may act as scaffolds or bridges between the pro- tein constituents. Consequently, we are unable to deter- mine the true topology of the complex. In order to integrate this type of data with those from other experi- mental sources, we must first derive a set of hypothetical pair-wise protein interactions using either a "spoke", or "matrix" model [16]. The spoke model assumes that the bait protein physically interacts with each of the prey pro- teins in the complex but does not acknowledge any type of association between the preys. In contrast, the matrix model assumes that any two proteins within the "com- plex" are connected. Here, we investigate the effect that the choice of datasets, and modelling methodology (matrix or spoke), has on the topological properties of the yeast protein interaction network and discuss our results with respect to the notion of a negative correlation between nodes within the net- work (in some studies, this is referred to as an "anticorre- lation"). We go on to investigate the notion of a highly connected essential sub-network and, finally, we discuss the nature of the term "interaction" and how the interpre- tation of that term might affect research within the field. Results Data choice The topology of the protein interaction network created using data derived from the yeast-subset of the DIP data- base (15,129 protein interactions involving 4,738 unique proteins) [17] reveals that the nodes within the network obey a power-law degree distribution as previously described (data not shown) [10]. Analysis of node con- nectivities also reveals the previously reported negative correlation between the connectivity of a central reference node (k0) and the mean connectivity of its neighbouring nodes (<k1>). The previously reported difference between the topologies of the global network and the network of essential yeast proteins is also evident, with the essential network displaying a markedly weaker negative correla- tion than the global network. For k0 ≤ 60, the global net- work has a correlation coefficient, rk0:k1 = -0.83 and slope, αk0:k1 = -0.25; for the network of essential proteins, rk0:k1 = -0.39, αk0:k1 = -0.08 between log (<k1>) and log (k0) (Figure 1) [14,15]. The yeast subset of the DIP database consists of interac- tions derived from a range of different studies that employed a variety of different experimental methods. It is possible that biases within one or more of these individ- ual datasets are having a measurable effect on the out- come of the topological analysis. By performing analyses on portions of the data extracted from the database, we were able to begin to identify some of these biases. Figure 2 shows the topology of the network created from protein interactions identified in protein interaction studies (totalling 3,191 protein interactions involving 1,623 pro- teins) that may be characterised as "small-scale", defined as an experiment described in a published article listing no more than 100 protein-protein interactions [18]. Again, we observed the previously identified power-law degree distribution for nodes within the network (data not shown). However, in this case, the differences Page 2 of 8 (page number not for citation purposes)