The epithelial-to-mesenchymal transition (EMT) initiates the invasive and metastatic behavior of many epithelial cancers. Mechanisms underlying EMT are not fully known. Surprisal analysis of mRNA time course data from lung and pancreatic cancer cells stimulated to undergo TGF-β1-induced EMT identifies two phenotypes. Examination of the time course for these phenotypes reveals that EMT reprogramming is a multistep process characterized by initiation, maturation, and stabilization stages that correlate with changes in cell metabolism. Surprisal analysis characterizes the free energy time course of the expression levels throughout the transition in terms of two state variables. The landscape of the free energy changes during the EMT for the lung cancer cells shows a stable intermediate state.Existing data suggest this is the previously proposed maturation stage. Using a single-cell ATP assay, we demonstrate that the TGF-β1-induced EMT for lung cancer cells, particularly during the maturation stage, coincides with a metabolic shift resulting in increased cytosolic ATP levels. Surprisal analysis also characterizes the absolute expression levels of the mRNAs and thereby examines the homeostasis of the transcription system during EMT.free energy landscape | transcriptions expression profile | maximal entropy | cellular thermodynamics | microarray T he epithelial-to-mesenchymal transition (EMT) is a cellular transition critical for several normal biological events, including embryonic development and wound healing. EMT has been investigated most exhaustively in cancer progression. An evoked EMT in epithelial cancer cells induces gene expression changes that result in loss of adhesive properties and acquisition of mesenchymal cell traits associated with tumor progression and metastasis, e.g., increased cellular motility, migration, and invasion (1, 2).Cultured epithelial tumor cells may be induced by several alternative stimuli to undergo an EMT, leading to acquisition of mesenchymal properties (3). Transforming growth factors (TGFs) are the most widely used EMT inducers. Microarray expression profiling enables identification of TGF-β1 EMT-induced molecular alterations and mechanisms (4).Gene expression transcriptional profiling is a major tool in analyzing induced changes in cells, yet interpretation of microarray experiments is faced with challenges (5-7). Here we use an alternative approach, surprisal analysis (8), to better understand, characterize, and represent gene expression differences critical to the EMT (4, 9).Surprisal analysis assumes that if a system can decrease its free energy, it will do so spontaneously unless constrained. If a system does not attain its minimal free energy state, surprisal analysis seeks to recognize the constraints that prevent a reduction in energy; surprisal analysis identifies the main constraints on a system that has the thermodynamic potential to change spontaneously but is restrained from doing so (10).As in physics and chemistry (11,12), surprisal analysis in biology (13-16) ar...

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