BMC Genomics 2005, 6:131 http://www.biomedcentral.com/1471-2164/6/131 the highly connected nature of the sub-network of essen- tial proteins previously reported by Pereira-Leal and co- workers is primarily a consequence of the high-degree bias of its nodes, rather than a manifestation of some specific evolutionary process. Conclusion We conclude that, before embarking on these network- based analyses, we must first be clear as to what we mean when we use the term "interaction". Interactions derived from direct physical studies, such as Y2H experiments, are very different from those found in synthetic genetic screens, which (in turn) are different again from "associa- tions" between the proteins found within protein com- plexes. However, in several recent studies, many of these different interaction types have been lumped together as though they were equivalent and directly comparable. For instance, both Y2H data and synthetic lethal gene pairs count as 'interactions' in the GRID database [18], although the protein products of the latter rarely interact physically [21]. While graph theoretical analysis approaches have been successfully applied to a number of man-made and natu- rally occurring networks [22], these networks differ from the biological systems investigated in that every link between pairs of nodes within the network is of the same type and is generally independent of other factors. For example, analysis of the HTML pages that make up the content of the World Wide Web is relatively simple. In this network, both the nature of the relationships (hyperlinks) between nodes (pages) and the nodes themselves are usu- ally homogeneous and well-defined. Therefore, meaning- ful and representative visualizations and quantifications of the structure of the network and its properties are pos- sible. However, the "biological networks" we construct are not representative of the underlying system. Biological systems essentially comprise protein "machines" [23] and biological function is mediated through associations between proteins, either directly through physical contact, or indirectly within protein complexes, or as part of the same biological pathway. Although it is technically possi- ble to create an abstract representation of these associa- tions; in reality, heterogeneity and the spatial and temporal restrictions imposed upon the links mean that the resulting topology and parameters of the network need not convey biologically meaningful information. Methods Network analysis was performed by extracting all machine-readable, yeast-derived protein interactions from the DIP database (release 20050605). Node connec- tivities and network topology were investigated using cus- tom software written in the Perl programming language. Random networks were generated from a pool of non- essential proteins only. Construction of the random net- work continued until the appropriate number of proteins had been selected, whose degree distribution within the sample was similar to that actually observed in the essen- tial network. This was done using an algorithm that cre- ated a sample of nodes that, at each level of connectivity, matched as closely as possible (data-permitting) the observed node numbers in the essential network. In instances where an exact match was not possible, another node with a degree within the same range of the desired node was selected. The essential sub-network is defined by taking into consideration only interactions between essential genes, as defined by the Saccharomyces Gene Deletion Project [24]. Correlations between variables were determined by computing the Pearson's correlation coefficient, r. We also report the slope, α, of a linear fit to the data. Authors' contributions LH carried out all the analyses and wrote all the custom algorithms, with advice and help from DR, and involving discussions between all authors. LH wrote the initial draft of the manuscript, which was revised by DR and SGO (who conceived of the study). All authors read and approved the final manuscript. Acknowledgements We thank Dr. Simon Lovell and for insights and discussions. LH is sup- ported by a CASE Studentship from the Biotechnology & Biological Sci- ences Research Council (BBSRC) and AstraZeneca. Work on protein interactions is supported by grants from the BBSRC to SGO and DR, and from the Beacon initiative of the UK Department of Trade & Industry to SGO. References 1. Amoutzias GD, Robertson DL, Oliver SG, Bornberg-Bauer E: Con- vergent evolution of gene networks by single-gene duplica- tions in higher eukaryotes. EMBO Rep 2004, 5(3):274-279. 2. Jeong H, Tombor B, Albert R, Oltvai ZN, Barabasi AL: The large- scale organization of metabolic networks. 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