Topology of gene expression networks as revealed by data mining and modeling.

Motivation: Interpretation of high-throughput gene expression profiling requires a knowledge of the design principles underlying the networks that sustain cellular machinery. Recently a novel approach based on the study of network topologies has been proposed. This methodology has proven to be useful for the analysis of a variety of biological systems, including metabolic networks, networks of protein-protein interactions, and gene networks that can be derived from gene expression data.

Comparison of gene expression patterns induced by treatment of human umbilical vein endothelial cells with IFN-alpha 2b vs. IFN-beta 1a: understanding the functional relationship between distinct type I interferons that act through a common receptor.

We analyzed whether interferon-alpha 2b (IFN-alpha 2b) and IFN-beta 1a engage their common receptor to generate activated receptor complexes possessing distinct signaling properties. Human vascular endothelial cells (HUVEC) are 100-1000-fold more sensitive to IFN-beta 1a than to IFN-alpha 2b in in vitro assays. An nonarray-based expression profiling (GeneCalling) technology was employed to compare the patterns and levels of gene expression induced by these IFN as the broadest means by which signaling events could be measured.