Gene network inference from time series expression data
Virginia Polytechnic Institute and State University
(April 28, 2008 10:30 AM - 11:30 AM)
The reverse engineering of biological network is a major focus of research in the post-omics era. Gene networks are conceptual representations of interactions between genes and may provide important information about the regulatory aspects of the biological system under study. Applications in biomedical engineering include the design of specific drug targets that could maximize the effect of its action across the network. A multitude of methods are available to infer gene networks from data, some of which have specific data requirements in order to satisfy their theoretical framework. I propose to present a new method to reverse engineer gene networks from time series data based on the estimation of gene interactions by least squares fitting. By iteratively selecting genes to be perturbed (i.e., to be knocked out), constraints can be imposed in the network, thereby helping in the inference process.