The prognosis of Glioblastoma remains barely unchanged since the establishment of the Stupp protocol in 2005. This cancer represents a paradigm of tumor heterogeneity, which initiates at the scale of the single cell and manifests in different manners at the macroscopic scale. Most mathematical models focus on either of these scales, and combining both remains challenging due to the complexity and computational cost of high-dimensional models.
MOLAB researchers have developed a mesoscopic model that aims to deal with this complexity while keeping an affordable simulation time. The model considers a three-dimensional discretization of space in voxels, and compartmentalizes the tumor cell population into different clones, all of which can coexist in a given region of space. These subpopulations evolve by means of four basic processes: reproduction, migration, mutation, and death. Cells perform these processes at different rates depending on the subpopulation they belong to, thus bringing heterogeneity to in-silico tumors, as subpopulation distribution depends on the time they appear and on the spot they do so. This emergent heterogeneity allows for the study of the growth and diversification of the population up to the macroscopic scale, which in turn permits its comparison with imaging data and whole tumor heterogeneity measures.
The model was recently published in PLoS Computational Biology, an interdisciplinary open-access journal, placed in Q1 (6/59) in the category 'Mathematical and Computational Biology’.
A mesoscopic simulator to uncover heterogeneity and evolutionary dynamics in tumors
Juan Jiménez-Sánchez, Álvaro Martínez-Rubio, Anton Popov, Julián Pérez-Beteta, Youness Azimzade, David Molina-García, Juan Belmonte-Beitia, Gabriel F Calvo, Víctor M Pérez-García
PLOS Computational Biology,17(2), e1008266 (2021)
If you are interested in digging into the model, you can find the code at this public repository: https://github.com/JuanJS117/MesoscopicModel