Our group is involved in an ongoing effort to model, understand and eventually reverse enginner genetic networks based on high-throughput data. Initial work on expansion of a known genetic network based on gene expression data proved promising and validated our modeling assumption. We developed a biclustering algorithm to detect tight regulatory modules, which allowed annotations of numerous unknown yeast genes at high specificity, and experimentally validated the suggested functionality of some genes.
We have carried out a genome-wide in-silico determination of transcriptional regulation modules controlling cell cycle in human cells. We are also studying and validating experimentally fitness function to evaluate putative regulatory functions and studying the properties of biologically motivated regulation functions.
The work described is taking place in Ron Shamir's group for Computational Genomics, School of Computer Science, Tel Aviv University. Participating group members are Amos Tanay, Roded Sharan, Rani Elkon, Chaim Linhart and Irit Gat-Viks. Some of the research is done in collaborations with Martin Kupiec (Life Sciences, TAU) and Yossi Shiloh (Medicine, TAU).
