Epithelial to Mesenchymal transition (EMT) is an exceedingly complex biological process that plays a key role in cancer progression and metastases formation. To unravel this complexity and isolate specific genetic markers important for this transition, we have developed a computational model recapitulating both single cell and population behaviours. The former was represented with a boolean network describing the major pathways involved in EMT, while the latter consisted in a Markov chain modeling the temporal evolution of the phenotypes (i.e. different gene expression patterns) throughout the transformation. This model was shown to have a good agreement with experimental results and to be able to identify a signature of genes with a key role EMT execution and progression.
The source code used for this work can be downloaded here while the paper’s full text is freely available at https://www.frontiersin.org/articles/10.3389/fgene.2019.00575/full
This work is part of the Research Topic: “Single-Cell Data Analysis: Resources, Challenges and Perspectives” of Frontiers in Genetics. Read the other works in this collection at https://www.frontiersin.org/research-topics/8103/single-cell-data-analysis-resources-challenges-and-perspectives#articles
