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

Nat Genet. 2008 Jul;40(7):854-61. Epub 2008 Jun 15.

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

Zhu J, Zhang B, Smith EN, Drees B, Brem RB, Kruglyak L, Bumgarner RE, Schadt EE.

Rosetta Inpharmatics, LLC, Seattle, Washington 98109, USA.

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.


PMCID: PMC2573859
PMID: 18552845 [PubMed - indexed for MEDLINE]