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Background: The epithelial-mesenchymal transition (EMT) is critically involved in tumorigenesis and tumor progression except its fundamental role to development and tissue remodeling. Although the full spectrum of signaling agents that contribute to EMTs of carcinoma cells remains unclear, the interactions and coordination of individual known molecules or pathways that contribute to EMT phenotype as a network are not well studied. The EMT signaling network is complicate in nature, as such, a systems mathematical modeling is especially valuable to help understand and characterize genes that are mediated individually or in combination in the context of relevant signaling pathways, and such models would help to predict feedback loop in the network and provide candidate strategies to control the signaling transduction furthermore guide us make clinic research available after the further validation. Experimental design and methods: siRNA-mediated knockdown of individual nine EMT-related genes in mesenchymal like cancer cell line (HMLE-Ras-Snail and U87), and epithelial cell lines (HMLE-Ras-vector and MCF10A) were imaged in a time-lapse manner at single cell resolution. Multiple cell phenotype features were extracted by image informatics analysis. Thirteen proteins including the 9 siRNA targeted genes as well as 4 epithelial and mesenchymal marker proteins were profiled by Western blot. Bioinformatics modeling was performed to create a gene EMT network and describe the genes or pathway interactions which are dominant for EMT or MET phenotypes. Results: Each individual siRNA knockdown indicated by western blot, concomitantly incucyte captured clear cell morphology changes due to RNA silence: silencing TJP1, PXN, FLNA, CCNE1switched cells to mesenchymal-like, silencing Gli3, CTNNB1, PSEN1 induced cells more Epithelial-like. However not every maker protein showed as significant changes as images parameters (cell width/length ratio, protrusion ect) did. Therefore by using traditional biochemical and imaging two approaches, we will identify the confident genes for EMT regulation. Conclusion: Our results demonstrate that high-content imaging is a reliable and convenient tool to help us to distinguish crucial metastasis genes and related pathway from other ambiguous, redundant and broad genes network that is valuable for us to interpret the intricate EMT mechanism. Citation Format: Xiaoping Zhu, Yin Zhen, Hong Zhao, Stephen Wong. High-content imaging facilities modeling of cancer epithelial-mesenchymal transition signaling network. [abstract]. In: Proceedings of the AACR Special Conference on Tumor Invasion and Metastasis; Jan 20-23, 2013; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2013;73(3 Suppl):Abstract nr C2.