Simulation and computational analysis of multiscale graph agent-based tumor model

TL;DR

This paper introduces a graph-based agent modeling system for tumor growth analysis, enabling detailed simulations of tumor micro-environments and complex network behaviors to aid in predictive diagnostics and therapy development.

Contribution

It presents a novel graph agent-based modeling and simulation framework for tumor growth, integrating network analysis with multi-scale biological modeling.

Findings

Enabled large-scale simulations using Python for detailed tumor micro-environment analysis.

Applied network analysis to pathway and metabolism prediction.

Demonstrated potential for fast, detailed computational biology simulations in clinical settings.

Abstract

This paper deals with the cellular biological network analysis of the tumor-growth model, consisting of multiple spaces and time scales. In this paper, we present a model in graph simulation using ABM for tumor growth. In particular, we propose a graph agent-based modeling and simulation system in the format of tumor growth scenario for evolving analysis. To manage cellular biological network analysis, we developed a workflow that allows us to estimate the tumor model and the complexity of the evolving behavior in a principled manner. By developing the model using Python, which has enabled us to run the model multiple times (more than what is possible by conventional means) to generate a large amount of data, we have succeeded in getting deep in to the micro-environment of the tumor, employing network analysis. Combining agent-based modeling with graph-based modeling to simulate the structure, dynamics, and functions of complex networks is exclusively important for biological systems with a large number of open parameters, e.g., epidemic models of disease spreading or cancer. Extracting data from evolutionary directed graphs and a set of centrality algorithms helps us to tackle the problems of pathway analysis and to develop the ability to predict, control, and design the function of metabolisms. Reproducing and performing complex parametric simulations a known phenomenon at a sufficient level of detail for computational biology could be an impressive achievement for fast analysis purposes in clinics, both on the predictive diagnostic and therapeutic side.