MAST: a Hybrid Multi-Agent Spatio-Temporal Model of Tumor Microenvironment Informed Using a Data-driven Approach

Overview

Journal Bioinform Adv

Publisher Oxford University Press

Specialty Biology

Date 2023 Jan 26

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

Affiliations

Soon will be listed here.

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 online.

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