A mesoscopic simulator to uncover heterogeneity and evolutionary dynamics in tumors.

ABSTRACT: Increasingly complex in silico modeling approaches offer a way to simultaneously access cancerous processes at different spatio-temporal scales. High-level models, such as those based on partial differential equations, are computationally affordable and allow large tumor sizes and long temporal windows to be studied, but miss the discrete nature of many key underlying cellular processes. Individual-based approaches provide a much more detailed description of tumors, but have difficulties when trying to handle full-sized real cancers. Thus, there exists a trade-off between the integration of macroscopic and microscopic information, now widely available, and the ability to attain clinical tumor sizes. In this paper we put forward a stochastic mesoscopic simulation framework that incorporates

SUBMITTER: Jimenez-Sanchez J 

PROVIDER: S-EPMC7901744 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature