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Stability and mean residence times for hybrid epithelial/mesenchymal phenotype

Biswas, K and Jolly, MK and Ghosh, A (2019) Stability and mean residence times for hybrid epithelial/mesenchymal phenotype. In: Physical Biology, 16 (2).

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Official URL: https://doi.org/10.1088/1478-3975/aaf7b7


Cancer metastasis and drug resistance remain unsolved clinical challenges. A phenotypic transition that is often implicated in both these processes is epithelial-mesenchymal transition (EMT) during which epithelial cells weaken their cell-cell adhesion and gain traits of migration and invasion, typical of mesenchymal cells. However, recent studies indicate that apart from these two states, cells can also exist in one or more hybrid E/M state(s), which plays an aggressive role in progression of the disease. Furthermore, computational and experimental studies have identified a variety of phenotypic stability factors (PSFs) that stabilize the hybrid E/M state(s) and can increase disease aggressiveness. In this work, we study EMT regulatory networks, in the presence of different PSFs, as dynamical systems subjected to random fluctuations. The cells thus explore different stable E, M, E/M states in the potential landscape and our aim is to quantify the residence time in each of these states. Our stochastic simulations indicate an universal feature that the mean residence time (MRT) in the hybrid E/M state is enhanced in the presence of PSFs. We demonstrate that the feature is consistent for a variety of PSFs, namely, GRHL2, ,OVOL, ΔNp63α, miR-145/OCT4, participating in the core EMT regulatory network. Our results reveal potential targets for pushing cells out of a hybrid E/M state and thus halting metastatic progression.

Item Type: Journal Article
Publication: Physical Biology
Publisher: Institute of Physics Publishing
Additional Information: The copyright for this article belongs to Institute of Physics Publishing.
Keywords: biological model; biology; epithelial mesenchymal transition; epithelium cell; gene expression; gene regulatory network; Markov chain; mesenchymal stem cell; metabolism; phenotype; physiology, Computational Biology; Epithelial Cells; Epithelial-Mesenchymal Transition; Gene Expression; Gene Regulatory Networks; Mesenchymal Stem Cells; Models, Genetic; Phenotype; Stochastic Processes
Department/Centre: Division of Interdisciplinary Sciences > Centre for Biosystems Science and Engineering
Date Deposited: 29 Nov 2022 06:24
Last Modified: 29 Nov 2022 06:24
URI: https://eprints.iisc.ac.in/id/eprint/78078

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