Discovering Differentially Expressed Genes in Yeast Stress Data

Abstract

Transcriptional regulation plays an important role in every cellular decision. Gaining an understanding of the dynamics that govern how a cell will respond to diverse environmental cues is difficult using intuition alone. We try to discover how genes interact when submitted to stress by exploring techniques of gene expression data analysis. We use several types of data, including high-throughput data. These results will help us recreate plausible regulatory networks by using a probabilistic logical model. Hence, network hypotheses can be generated from existing gene expression data for use by experimental biologists.