Epithelial-to-Mesenchymal Transition (EMT); a driver of tumor resistance and metastasis, is a complex mechanism that arises through an intricate cross talk between highly robust biological networks. There is minimal information on the most central genes in the networks that drive EMT primarily due to the lack of proper computational tools. To address this unmet problem, two PIs (a computational biologist and a molecular biologist) have teamed together to identify the central genes that are differentially expressed between epithelial and mesenchymal subtypes in well recognized Weinberg's EMT cell models. While these models have been the subject of differentially expressed (DE) gene analyses using the t-test and the F-test, it is not sufficient to interrogate the entire EMT phenomena due to the presence of additional genes that do not meet the DE criteria. Existing models for network analysis, co-expression analysis, and gene clustering can only provide information about a group of genes with similar behavior. However, such analysis cannot extract EMT-specific characterization of mesenchymal pathway genes; i.e. identifying the distinguishing set of mesenchymal patterns in the entire co-expressed gene groups that may be specific to EMT only. Here, we propose a network-based differential analysis model for analyzing the topological differences between two gene networks constructed from the expression data. We hypothesize that for deeper understanding of EMT a differential network analysis coupled with biological validation of the EMT associated genes in the correct models is critical. To this end, we performed comparative genomic microarrays expression investigations on Weinberg's K-ras-HMLE (Epithelial) and K-ras-HMLE-SNAIL (Mesenchymal) 4 cell lines datasets. Our analyses revealed a significant global gene expression difference between parent K-ras-HMLE and HMLE-SNAIL cells. As they are SNAIL driven EMT models, we challenged these cells with a small molecule inhibitor (SMI) against SNAIL (GN-25). Our new computational approach utilizes differential network analysis in multiple EMT models in cell culture, and in animal tumor model (to verify the influence of tumor microenvironment on EMT in situ). This will be coupled with more robust biological validation in the presence of newer network targeted drugs. Therefore, our Specific Aims are 1) Identifying EMT central genes using differential network-based algorithms and 2) Biological validation and evaluation of targeted strategies against centralized genes in the EMT networks. The identified central gene will be validated at the mRNA and protein expression level and their cause-effect relationship will be evaluated using RNA interference in the paired EMT models. In a network-driven drug design, the EMT cells will be challenged with a repertoire of small molecule drugs (identified through our chemical library screening) as single agent or in combination and verify whether drug treatments could target the central genes. Additional validation using efficacy trial of the most potent SMI or its combination in animal tumor models will fortify the clinical application of our network derived EMT targeted drugs.