Analysis of Gene Regulatory Network Based on Functional Clustering of Gene Expression Dynamics

• Activity Number: 71
• Type: Contributed
• Date/Time: Sunday, August 3, 2014 : 4:00 PM to 5:50 PM
• Sponsor: Biometrics Section
• Author(s): Yaqun Wang*+ and Ningtao Wang and Han Hao and Rongling Wu
• Companies: Penn State and Penn State and Penn State and Penn State
• Keywords: Gene Regulatory Network ; Functional clustering ; Arterial occlusive disease ; Vein bypass grafting ; Non-parametric

Abstract

Organisms usually stimulate their regulatory system through adjusting gene transcription rates to best adapt to the environment. Identifying the difference in the pattern of gene expression trajectories and analysis the interaction between gene subsets can help us have a comprehensive map of gene-gene and gene-environment relationships. We describe a method for regulatory network analysis of gene clusters based on functional clustering which fits to the expression data measured at a set of discrete time points. The mean vectors for each clusters are modeled as functions of time by non-parametric polynomials then the approach for reconstructing gene regulatory network takes the advantage of these highly informative functions. Simulation studies illustrated that the tool has favorable statistical properties. This model has been used to analyze a data set of microarray genes associated with response to vein bypass grafting designed to treat arterial occlusive disease. It provides a useful tool for our understanding of gene functions and networks.