Inference of Dynamic Activity of Transcriptional Modules

• Activity Number: 426
• Type: Contributed
• Date/Time: Wednesday, August 9, 2006 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Ron Yu*+ and Jie Liu and Wei Wang
• Companies: University of California, San Diego and University of California, San Diego and University of California, San Diego
• Keywords: transcription factors ; regulatory network ; gene expression regulation ; motif binding ; Saccharomyces cerevisiae ; system biology

Abstract

One of the major challenges in the post-genome era is to identify all the functional modules and decipher the regulatory network. Given a set of microarray experiments, we have developed a probabilistic model to integrate sequence motif, gene expression and ChIP-chip data to infer the activity and direct targets of transcription factors (TFs) simultaneously. The probabilistic model provides a flexible framework for data integration from different sources and removes arbitrariness commonly seen with target gene selections. As a validation, our model was applied to several yeast dataset and the results were promising. Special attention was paid to the robustness of the algorithm. The model is being applied to study other higher organisms including fly and human.