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

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.

Computational systems‐biology approaches for modeling gene networks driving epithelial–mesenchymal transitions

“…Despite its success in modeling EMT GRN, the bottom-up approach is typically limited by the following factors: (1) literature synthesis can be quite tedious and time consuming; (2) because of the involvement of significant manual curation, it is not straightforward to reproduce literaturebased GRNs; (3) there may not be sufficient literature data to investigate the EMT process in a particular biological context. Additional information on the bottomup approach can also be found in some recent reviews [14,[57][58][59].…”

Non-destructive monitoring of 3D cell cultures: new technologies and applications

“…6 computational modelling is also associate to the evaluation of a single variable. This is not entirely true, as specialized models can be used to simulate all the quantities considered within this work ( Cortesi et al, 2020b ; Cortesi et al, 2021a ; Cortesi et al, 2020a ; Kim et al, 2018b ; Sun & Hu, 2018 ; M Cortesi & E Giorda, 2022, unpublished data), but this analysis hasn’t been included, as measurement techniques were preferred whenever available.…”

Design of a custom-made device for real-time optical measurement of differential mineral concentrations in three-dimensional scaffolds for bone tissue engineering

“…Monitoring maturation of bone TEed constructs in these settings is important to ensure the quality of applied protocols. In this respect, in-silico models, aiming at improving the characterization of three-dimensional cell cultures [ 6 , 7 ] have also been proposed. However, mainly histochemical, destructive methods are currently used, requiring the sacrifice of the investigated samples.…”