title: Estimating coarse gene network structure from large-scale gene perturbation data.
abstract: Large scale gene perturbation experiments generate information about the number of genes whose activity is directly or indirectly affected by a gene perturbation. From this information, one can numerically estimate coarse structural network features such as the total number of direct regulatory interactions and the number of isolated subnetworks in a transcriptional regulation network. Applied to the results of a large-scale gene knockout experiment in the yeast Saccharomyces cerevisiae, the results suggest that the yeast transcriptional regulatory network is very sparse, containing no more direct regulatory interactions than genes. The network comprises >100 independent subnetworks.
authors.affiliation: University of New Mexico and The Santa Fe Institute, University of New Mexico, Department of Biology, Albuquerque, New Mexico 87131-1091, USA. wagnera@unm.edu
authors.forename: Andreas
authors.initials: A
authors.lastname: Wagner
mesh_terms: D017353:Gene Deletion; D020869:Gene Expression Profiling / Q000379:methods* / Q000706:statistics & numerical data; D005800:Genes, Fungal / Q000235:genetics; D005809:Genes, Regulator / Q000235:genetics*; D012441:Saccharomyces cerevisiae / Q000235:genetics