MAST: a hybrid Multi-Agent Spatio-Temporal model of tumor microenvironment informed using a data-driven approach.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36699399", "kind": "science-record", "revision": 1, "content_hash": "sha256:b96b65e5fad58c1449ac0c20b0b45faae7da944aa98607805123ada55208c373", "created_at": "2026-11-01T19:01:15Z", "updated_at": "2026-11-01T19:01:15Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "MAST: a hybrid Multi-Agent Spatio-Temporal model of tumor microenvironment informed using a data-driven approach.", "doi": "10.1093/bioadv/vbac092", "pmid": "36699399", "authors": ["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"], "journal": "Bioinformatics advances", "year": 2022, "abstract": "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.\nResults: 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.\nAvailability and implementation: 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. The submitted software version and test data are available in Zenodo at https://dx.doi.org/10.5281/zenodo.7267745.\nSupplementary information: Supplementary data are available at Bioinformatics Advances online.", "claims": [{"id": "claim-data-access", "statement": "The model described in this article is implemented in Python language and it is freely available with an open-source license through GitLab (https://gitlab.com/sysbiobig/mast). MAST is also released through the Docker Registry (https://registry.gitlab.com/sysbiobig/mast/) to facilitate its distribution. 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