Algorithmic approaches for analyzing genetic networks
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.
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.
Our combined framework is readily extendable to novel experimental techniques and higher organisms.