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.

Abstract
Transcriptional regulation plays an important role in every cellular decision. Unfortunately, understanding the dynamics that govern how a cell will respond to diverse environmental cues is difficult using intuition alone. We introduce logic-based regulation models based on state-of-the-art work on statistical relational learning, and validate our approach by using it to analyze time-series gene expression data of the Hog1 pathway. Our results show that plausible regulatory networks can be learned from time series gene expression data using a probabilistic logical model. Hence, network hypotheses can be generated from existing gene expression data for use by experimental biologists.