MAST, implemented in Python language, is freely available with an open-source license through GitLab (https://gitlab.com/sysbiobig/mast), and a Docker image is provided to ease its deployment.
Software Notes:
Motivation: Recently, several computational modeling approaches, such as agent-based models, have been applied to study the interaction dynamics between immune and tumor cells in human cancer. However, each tumor is characterized by a specific and unique tumor microenvironment, emphasizing the need for specialized and personalized studies of each cancer scenario.
Results: We present MAST, a hybrid Multi-Agent Spatio-Temporal model which can be informed using a data-driven approach to simulate unique tumor subtypes and tumor–immune dynamics starting from high-throughput sequencing data. It captures essential components of the tumor microenvironment by coupling a discrete agent-based model with a continuous partial differential equations-based model. The application to real data of human colorectal cancer tissue investigating the spatio-temporal evolution and emergent properties of four simulated human colorectal cancer subtypes, along with their agreement with current biological knowledge of tumors and clinical outcome endpoints in a patient cohort, endorse the validity of our approach.
Citation
If you use the software for your research, please refer to the original MAST paper with the citation below:
Giulia Cesaro, Mikele Milia, Giacomo Baruzzo, Giovanni Finco, Francesco Morandini, Alessio Lazzarini, Piergiorgio Alotto, Noel Filipe da Cunha Carvalho de Miranda, Zlatko Trajanoski, Francesca Finotello, Barbara Di Camillo, MAST: a hybrid Multi-Agent Spatio-Temporal model of tumor microenvironment informed using a data-driven approach, Bioinformatics Advances, Volume 2, Issue 1, 2022, vbac092, https://doi.org/10.1093/bioadv/vbac092.
Download
The last version of MAST is available at https://gitlab.com/sysbiobig/mast.
