Network-based Statistical Models and Methods for Identification of Cellular Mechanisms of Action
Department of Mathematics and Statistics
Johnson Center G19 - Gold Room
4400 University Drive, Fairfax, VA 22030
Time: 11:00 A.M. - 12:00 P.M.
Date: Friday, Apr 5, 2013
Identifying biological mechanisms of action (e.g. biological pathways) that control disease states, drug response, and altered cellular function is a multifaceted problem involving a dynamic system of biological variables that culminate in an altered cellular state. The challenge is in deciphering the factors that play key roles in determining the cell's fate. In this talk I will describe some of the efforts by our group to develop statistical models and methods for identification of cellular mechanisms of action. More specifically, we assume gene expression data and treat the problem of determining mechanisms of action under perturbation (e.g., drug treatment, gene knockout, etc.) as a type of inverse problem. I will describe three approaches to solving this inverse problem. The first attempts to use only the gene expression data and to `filter' that data by an inferred network of gene regulatory interactions. The other two -- one testing-based and the other regression-based -- use gene expression data in conjunction with information from biological databases. More specifically, gene expression is modeled as deriving from a perturbed latent network of pathways, where the inter-connections among pathways is informed by shared biological function. Illustrations are given in the context of yeast experiments and human cancer.