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Computational models to explore the complexity of the epithelial to mesenchymal transition in cancer

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Abstract Epithelial to mesenchymal transition (EMT) is a complex biological process that plays a key role in cancer progression and metastasis formation. Its activation results in epithelial cells losing adhesion and polarity and becoming capable of migrating from their site of origin. At this step the disease is generally considered incurable. As EMT execution involves several individual molecular components, connected by nontrivial relations, in vitro techniques are often inadequate to capture its complexity. Computational models can be used to complement experiments and provide additional knowledge difficult to build up in a wetlab. Indeed in silico analysis gives the user total control on the system, allowing to identify the contribution of each independent element. In the following, two kinds of approaches to the computational study of EMT will be presented. The first relies on signal transduction networks description and details how changes in gene expression could influence this process, both focusing on specific aspects of the EMT and providing a general frame for this phenomenon easily comparable with experimental data. The second integrates single cell and population level descriptions in a multiscale model that can be considered a more accurate representation of the EMT. The advantages and disadvantages of each approach will be highlighted, together with the importance of coupling computational and experimental results. Finally, the main challenges that need to be addressed to improve our knowledge of the role of EMT in the neoplastic disease and the scientific and translational value of computational models in this respect will be presented. This article is categorized under: Analytical and Computational Methods > Computational Methods
Representation of the elements of the canonical WNT pathway considered in Gasior et al. (). When the WNT pathway is off (WNT = 0) CDH1 is highly expressed and binds βCAT at the membrane. Any free βCAT protein is targeted for degradation. The activation of the WNT pathway (WNT = 1) leads to the inhibition (crossed line) of βCAT degradation by DVL. The accumulation and nuclear translocation of this protein leads to the activation of SLUG and inhibition of CDH1
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Gene network considered in Celia‐Terrassa et al. (). Here TGF‐β1 is considered as input and CDH1 as output. Figure recreated from Celia‐Terrassa et al. ()
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Gene circuit used in Bocci et al., to study the interconnection between EMT (blue: Zeb, Snail μ34, μ200), Notch pathway (green: NICD, Notch, Delta, Jagged), and Numb regulation (red). A and B represent single cells. Figure recreated from (Bocci et al. ()
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Schematic representation of the structure of a LGCA. This model can be used to describe cell migration as it comprises a 2D mesh representing the environment where virtual cells (gray dots) can move. Each element of the 2D mesh (gray square) comprises five main elements: a rest channel (orange cloud) and four velocity channels (blue arrows). These structures allow cells to stay in the current mesh element and move toward a neighboring one respectively. Each channel has a maximum capacity, that is the maximum number of cells that it can hold (six cells for rest channels and one for velocity channels). In this representation gray arrows show the prevalent direction of the biased random walk in that mesh element
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