Abstract:

The dissection of complex biological systems is a challenging task, made
difficult by the size of the underlying genetic network, the
heterogeneous nature of the control mechanisms involved, and the noise
inherent in the experimental data generated on such systems.  In this
talk I will present three strategies towards uncovering the organization
of the yeast genetic network.  The strategies employ novel algorithmic
methods for the integrated analysis of diverse genome-wide data sources,
and are readily extendable to novel experimental techniques and higher
organisms.

- We applied a novel biclustering algorithm to identify groups of genes
with statistically significant correlated behavior across diverse
experiments. The discovered biclusters revealed a hierarchical
organization of the yeast network and were used to predict the function
of over 800 uncharacterized genes.

- We developed a statistical framework for identifying associations
between sequence motifs that are involved in regulating gene activity.
We applied this framework to sequence data from five yeast species to
discover co-occurring motifs and their characteristic sequence patterns.

- We also performed a comparative study of the protein interaction
networks of yeast and bacteria to identify conserved sub-networks. Our
analysis was based on a detailed  probabilistic model for the data,
which was used to recast the question of finding conserved structures as
a problem of searching for heavy subgraphs in an edge- and node-weighted
graph. The discovered sub-networks shed light on evolutionary
relationships between the two species.