Learning Regulatory Network Models that Represent Regulator States and Roles

Abstract. We present an approach to inferring probabilistic models of generegulatory networks that is intended to provide a more mechanistic representation of transcriptional regulation than previous methods. Our approach involves learning Bayesian network models using both gene-expression and genomic-sequence data. One key aspect of our approach is that our models represent states of regulators in addition to their expression levels. For example, the state of a transcription factor may be determined by whether a particular small molecule is bound to it or not. Our models represent these states using hidden nodes in the Bayesian networks. A second key aspect of our approach is that we use known and predicted transcription start sites to determine whether a given transcription factor is more likely to act as an activator or a repressor for a given gene. We refer to this distinction as the role of a regulator with respect to a gene. Determining the roles of a regulator provides a helpful...

• Type: Conference
• Year: 2004
• Where: RECOMB
• Authors: Keith Noto, Mark Craven
• Keywords: Computational Biology, Key Aspect, Particular Small Molecule, RECOMB 2004, Transcription Factor