Pathway enrichment analysis has become a key tool for biomedical researchers to gain insight in the underlying biology of differentially expressed genes, proteins and metabolites. It reduces complexity and provides a systems-level view of changes in cellular activity in response to treatments and/or progression of disease states. Methods that use pathway topology information have been shown to outperform simpler methods based on over-representation analysis. However, despite significant progress in understanding the association among members of biological pathways, and expansion of new knowledge data bases, such as KEGG, Reactome, BioCarta, etc., the existing network information may be incomplete/inaccurate, and are not condition-specific. We propose a constrained network estimation framework that combines network estimation based on cell- and condition-specific omics data with interaction information from existing data bases. The resulting pathway topology information is subsequently used to provide a framework for simultaneous testing of differences in mean expression levels, as well as interaction mechanisms.