Towards Using Probabilities and Logic to Model Regulatory Networks

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.