Dataset Information

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

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.

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.

Availability 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.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

SUBMITTER: Cesaro G

PROVIDER: S-EPMC9744439 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

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

Cesaro Giulia G Milia Mikele M Baruzzo Giacomo G Finco Giovanni G Morandini Francesco F Lazzarini Alessio A Alotto Piergiorgio P da Cunha Carvalho de Miranda Noel Filipe NF Trajanoski Zlatko Z Finotello Francesca F Di Camillo Barbara B

Bioinformatics advances 20221205 1

<h4>Motivation</h4>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.<h4>Results</h4>We present MAST, a hybrid Multi-Agent Spatio-Temporal model which can be informed using a data-driven approach t ...[more]

PMID: 36699399

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Dataset Information

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

Motivation

Results

Availability and implementation

Supplementary information

Publications

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

Similar Datasets

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