February 22, 2007 - Christina Leslie

Title
Learning predictive models of gene regulation

Abstract
Studying the behavior of gene regulatory networks by learning from
high-throughput genomic data has become one of the central problems in
computational systems biology. Most work in this area has focused on
learning structure from data -- e.g. finding clusters or modules of
potentially co-regulated genes, or building a graph of putative
regulatory "edges" between genes -- and has been successful at
generating qualitative hypotheses about regulatory networks.

Instead of adopting the structure learning viewpoint, our focus is to
build predictive models of gene regulation that allow us both to make
accurate quantitative predictions on new or held-out experiments (test
data) and to capture mechanistic information about transcriptional
regulation. Our algorithm, called MEDUSA, integrates promoter
sequence, mRNA expression, and transcription factor occupancy data to
learn gene regulatory programs that predict the differential
expression of target genes. Instead of using clustering or
correlation of expression profiles to infer regulatory relationships,
the algorithm learns to predict up/down expression of target genes by
identifying condition-specific regulators and discovering regulatory
motifs that may mediate their regulation of targets. We use boosting,
a technique from statistical learning, to help avoid overfitting as
the algorithm searches through the high dimensional space of potential
regulators and sequence motifs. We will report computational results
on the yeast environmental stress response, where MEDUSA achieves high
prediction accuracy on held-out experiments and retrieves key
stress-related transcriptional regulators, signal transducers, and
transcription factor binding sites. We will also describe recent
results on the hypoxic response in yeast, where we used MEDUSA to
propose the first global model of the oxygen sensing and regulatory
network, including new putative context-specific regulators. Through
our experimental collaborator on this project, the Zhang Lab at
Columbia University, we are in the process of validating our
computational predictions with wet lab experiments.