00040
NETWORK INFORMATION FLOW REVEALS KEY GENES IN GLIOBLASTOMA PROGRESSION

Saturday, February 18, 2017
Exhibit Hall (Hynes Convention Center)
Yunpeng Liu, Massachusetts Institute of Technology, Cambridge, MA
Regulatory networks within the cell are organized into a multilayer architecture with feed-forward and feedback control motifs. Current efforts to understand how information is propagated in the cell are confined to a monolayered view or omit feedback loops that are key features of many regulatory networks. Here we present an information flow algorithm that simulates propagation of regulatory signals over a multilayer representation of cellular networks. Through integration of multiple data types and interaction rules inspired by Google’s webpage ranking algorithm, we show using the TCGA glioblastoma multiforme (GBM) mRNA / miRNA dataset as well as previously compiled regulatory relationships among TFs, miRNAs and target genes that this novel algorithm is able to uncover key regulators during cancer progression.