Inference of radio-responsive gene regulatory networks using the graphical lasso algorithm

Abstract

Background: Inference of gene regulatory networks (GRNs) from gene microarray expression data is of great interest and remains a challenging task in systems biology. Despite many efforts to develop efficient computational methods, the successful modeling of GRNs thus far has been quite limited. To tackle this problem, we propose a novel framework to reconstruct radio-responsive GRNs based on the graphical lasso algorithm. In our attempt to study radiosensitivity, we reviewed the literature and analyzed two publicly available gene microarray datasets. The graphical lasso algorithm was applied to expression measurements for genes commonly found to be significant in these different analyses. Results: Assuming that a protein-protein interaction network obtained from a reliable pathway database is a goldstandard network, a comparison between the networks estimated by the graphical lasso algorithm and the goldstandard network was performed. Statistically significant p-values were achieved when comparing the goldstandard network with networks estimated from one microarray dataset and when comparing the networks estimated from two microarray datasets. Conclusion: Our results show the potential to identify new interactions between genes that are not present in a curated database and GRNs using microarray datasets via the graphical lasso algorithm.