Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks

Nat Genet. 2008 Jul;40(7):854-61. doi: 10.1038/ng.167. Epub 2008 Jun 15.


A key goal of biology is to construct networks that predict complex system behavior. We combine multiple types of molecular data, including genotypic, expression, transcription factor binding site (TFBS), and protein-protein interaction (PPI) data previously generated from a number of yeast experiments, in order to reconstruct causal gene networks. Networks based on different types of data are compared using metrics devised to assess the predictive power of a network. We show that a network reconstructed by integrating genotypic, TFBS and PPI data is the most predictive. This network is used to predict causal regulators responsible for hot spots of gene expression activity in a segregating yeast population. We also show that the network can elucidate the mechanisms by which causal regulators give rise to larger-scale changes in gene expression activity. We then prospectively validate predictions, providing direct experimental evidence that predictive networks can be constructed by integrating multiple, appropriate data types.

Publication types

  • Research Support, N.I.H., Extramural
  • Validation Study

MeSH terms

  • Bayes Theorem
  • Cluster Analysis
  • Crosses, Genetic
  • Gene Expression Regulation, Fungal*
  • Gene Regulatory Networks / physiology*
  • Genome, Fungal*
  • Genomics / methods
  • Models, Statistical
  • Organisms, Genetically Modified
  • Quantitative Trait Loci
  • Saccharomyces cerevisiae / genetics*
  • Transcription Factors / genetics
  • Transcription Factors / physiology


  • Transcription Factors

Associated data

  • GEO/GSE11111
  • GEO/GSE1990