A gene regulatory network (GRN) extracted from microarray data has the potential to give us a concise and precise way of understanding how genes interact under the experimental conditions studied [1, 2]. Learning such networks, and unravelling the knowledge hidden within them is important for drug targets and to understand the basis of disease. In this paper, we analyse microarray gene expression data fromSaccharomyces cerevisiae, to extract Bayesian belief networks (BBNs) which mirror the cell cycle GRN. This is achieved through the use of a novel structure learning algorithm of Taboo search and a novel knowledge extraction technique, target node (TN) analysis. We also show how quantitative and qualitative information captured within the BBN can be used to simulate the nature of interaction between genes. The GRN extracted was validated against literature and genomic databases, and found to be in excellent agreement.