A Predictive Approach for the Tumor-Immune System Interactions Based on an Agent Based Modeling

Abstract

Purpose: The goal of this study is to introduce a quantified feature for investigating the quality manner and interaction between the immune system and tumor cell. Methods: For this purpose, we introduced an agent based model which uses two agents consisting tumor cell and CD8+ cells and the environment which consists IL-2 and TGF-β cytokines. This model works using a variety of ratios. The most important ratio of this model is the tumor’s proliferation ratio. Results: We investigated this ratio in three states of tumor-immune system interaction consisting elimination, equilibrium and escape using a raw model, then this ratio was investigated using models which were optimized by experimental data. Conclusion: The results showed that if the model is leaning to the elimination state, this ratio falls faster and if is leaning to the escape state, this ratio will reduce slowly. The result was proved by models which used experimental data for optimizing. Therefore, using this ratio we can compare different manners of tumor-immune system interactions.

Keywords

• Tumor-immune system
• Quantified feature
• Agent based model
• Prediction