Agent-based model for tumour-analysis using Python+Mesa

TL;DR

This paper presents a Python-based agent model for tumor analysis that facilitates running multiple simulations to study tumor microenvironments through network analysis, aiding in understanding complex biological dynamics.

Contribution

It introduces a new simplified agent-based model using Python and Mesa for tumor analysis, enabling extensive simulation and data collection for microenvironment study.

Findings

Model allows extensive simulation with varied parameters.

Enables detailed network analysis of tumor microenvironment.

Facilitates in-depth biological insights through simulation data.

Abstract

The potential power provided and possibilities presented by computation graphs has steered most of the available modeling techniques to re-implementing, utilization and including the complex nature of System Biology (SB). To model the dynamics of cellular population, we need to study a plethora of scenarios ranging from cell differentiation to tumor growth and etcetera. Test and verification of a model in research means running the model multiple times with different or in some cases identical parameters, to see how the model interacts and if some of the outputs would change regarding different parameters. In this paper, we will describe the development and implementation of a new agent-based model using Python. The model can be executed using a development environment (based on Mesa, and extremely simplified for convenience) with different parameters. The result is collecting large sets of data, which will allow an in-depth analysis in the microenvironment of the tumor by the means of network analysis.