Discovering novel regulatory controls of budding yeast cell cycle by Reverse Engineering and Bayesian Network Modeling

Yan Sun1, Pawan Dhar2, Bioinformatics Institute, 21 Heng Mui Keng Terrace, Singapore;, Bioinformatics Institute, 21 Heng Mui Keng Terrace, Singapore

The Cell cycle represents a fundamental driving force for differentiation and evolution in eukaryotes. The successful completion of DNA synthesis and replication is strongly dependent on its state of division and cell physiology. Due to its vital role, therefore, a vast repertoire of genes, proteins and small molecules are employed to keep cell cycle under a constant state of surveillance. The big challenge therefore is to understand the logic of these fundamental interactions and discover new regulatory feedback loops. Genetic networks are frequently modeled as forward processes, wherein the topology is known, or reverse processes wherein a functional relationship among genes is inferred from global expression profiles. In this study, a new Reverse Engineering and Bayesian Network Modeling (REBNM) approach has been developed to construct cell cycle regulatory network from high-throughput gene expression data. The REBNM analyzer comprises of two major components: the support vector machine (SVM) and Bayesian Modeling method. The SVM is heavily based on prior biological knowledge and uses supervised classification scheme to categorize genes into different functional groups to identify cell cycle regulated genes. The Bayesian network modeling technique is used for learning and inferring the causal interactions among various regulatory controls. In this study we have used the publicly available Budding Yeast genomic data, from Stanford University ( Based on this data the interaction between Cyclins, Cyclin Dependent Kinases, Anaphase Promoting Complexes and other proteolytic enzymes were modeled and simulated. Our aim is to coalesce reverse engineered networks into forward modeling approach to create an accurate cell cycle model. The next step would be to predict novel functions of proteins and study evolution of cell cycle at various stages of complexities.