TABLE 1 

N denotes operons, not genes.

This result was derived (statistically) by partial correlation analysis on microarrays and we suspect that this method is not precise. A pilot study with just 2000 genes found a network with N=820 and n=828, which gives c=0.00123 and K1.0. However, this is likely much too sparse. Moreover, although this sample should be representative for larger N, and thus K should be the same for large N, their partial correlation analysis shows a K=2.75. This method seems to give a large number of false negatives for smaller N, and according to our analysis, a large number of false positives for the larger networks. Nevertheless, a value for K where 1K2.75 will still result is a network that is quite sparse.

The number of network interactions for a subset of an organism's genes, which were reported in various studies and databases, is shown. Interactions: the number of interactions n reported for the N genes; genes: the number of genes N that had reported interactions; column c: the connectivity density (c=n/N²); column K: the average number of transcriptional regulators per gene (K=cN); secondary source: the source that reported the values; primary source: where the secondary source derived the values from. References for secondary and primary sources are shown at the bottom of the table.

Sources: (i) Serov VN, Spirov AV, Samsonova MG (1998). Graphical interface to the genetic network database GeNet. Bioinfomatics14: 546–547; (ii) GeNet (http://www.bionet.nsc.ru/bgrs/thesis/17/index.html); (iii) Rosenfeld N and Alon U (2003). Response delays and the structure of transcription networks. J Mol Biol329: 645–654; (iv) Davidson EH et al (2002). A genomic regulatory network for development. Science295: 1669–1678; (v) Costanzo MC et al (2001). YPD^TM, PombePD^TM and WormPD^TM: model organism volumes of the BioKnowledge^TM Library, an integrated resource for protein information. Nucleic Acids Res29: 75–79; (vi) Lee TI et al (2002). Transcriptional regulatory networks in Saccharomyces cerevisiae. Science298: 799–804; (vii) Kauffman S, Peterson C, Samuelsson B, Troein C (2003). Random Boolean network models and the yeast transcriptional network. Proc Natl Acad Sci USA100: 14796–14799; (viii) http://web.wi.mit.edu/young/regulatory_network; (ix) Shen-Orr SS, Milo R, Mangan S, Alon U (2002). Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genetics31: 64–68; (x) Ma S, Gong Q, Bohnert HJ (2007). An Arabidopsis gene network based on the graphical Gaussian model. Genome Res17: 1614–1625.