Speaker: Amir Reza Alizad Rahvar
Ph.D Candidate,
Department of Electrical and Computer Engineering
University of Alberta
Local Organizer: Dr. Amir Masoud Rabiei
Title: Directed Network Inference of Gene Networks Through Computational Methods
Time: Sunday, August 28, 2011, 14:00-15:00 pm
Location: Shora Room, Department of Electrical and Computer Engineering, Tehran University, Tehran
Abstract:
One of the most important biological networks, with immediate applications in cancer prediction and drug development, are “gene regulatory networks†(GRNs). These networks are representing the regularity interactions of the genes, i.e., activation or suppression of other genes. A GRN aims to capture the interactions in gene regulation. It is often modeled as a graph composed of nodes (genes and proteins) and edges (genegene, protein–DNA, and protein–protein interactions).
Recently, a number of computational approaches have been proposed for inference of GRNs such as applying Boolean networks, Bayesian networks, differential equations, and information theory. Some methods are merely looking for associations and dependency between genes and ignore the cause-effect relationships. Hence, they only provide an undirected network. Moreover, most of them use Markovian (acyclic) models that exclude inherent loops in GRNs. In this talk, we briefly introduce the GRNs and advanced computational methods for network inference of GRNs, i.e., identifying molecular interactions. Our objective is to develop computational approaches that are able to capture the cause-effect relations (as a directed graph) and discover the inherent loops in GRNs. We develop an information theoretic framework based on the “Transfer Entropy†to infer the directed networks.