The vast majority (> 95%) of single-gene mutations in yeast affect not only the expression of the mutant gene, but also the expression of many other genes. These data suggest the presence of a previously uncharacterized ‘gene expression network’—a set of interactions between genes which dictate gene expression in the native cell environment. Here, we quantitatively analyze the gene expression network revealed by microarray expression data from 273 different yeast gene deletion mutants.(1) We find that gene expression interactions form a (...) robust, error-tolerant ‘scale-free’ network, similar to metabolic pathways(2) and artificial networks such as power grids and the internet.(3-5) Because the connectivity between genes in the gene expression network is unevenly distributed, a scale-free organization helps make organisms resistant to the deleterious effects of mutation, and is thus highly adaptive. The existence of a gene expression network poses practical considerations for the study of gene function, since most mutant phenotypes are the result of changes in the expression of many genes. Using principles of scale-free network topology, we propose that fragmenting the gene expression network via ‘genome-engineering’ may be a viable and practical approach to isolating gene function. BioEssays 24:267–274, 2002. © 2002 Wiley Periodicals, Inc.; DOI 10.1002/bies.10054. (shrink)

Studies on the conservation of the inferred transcriptional regulatory network of prokaryotes have suggested that specific transcription factors are less‐widely conserved in comparison to their target genes. This observation implied that, at large evolutionary distances, the turnover of specific transcription factors through loss and non‐orthologous displacement might be a major factor in the adaptive radiation of prokaryotes. However, the recent work of Hershberg and Margalit1 suggests that, at shorter phylogenetic scales, the evolutionary dynamics of the bacterial transcriptional regulatory network might (...) exhibit distinct patterns. The authors find previously unnoticed relationships between the regulatory mode (activation or repression), the number of regulatory interactions and their conservation patterns in γ‐proteobacteria. These relationships might be shaped by the differences in the adaptive value and mode of operation of different regulatory interactions. BioEssays 29:625–629, 2007. © 2007 Wiley Periodicals, Inc. (shrink)