Background:To predict the behavior of biological systems, mathematical models of biological systems have been shown to be useful. In particular, mathematical models of tumor-immune system interactions have demonstrated promising results in prediction of different behaviors of tumor against the immune system.Methods:This study aimed at the introduction of a new model of tumor-immune system interaction, which includes tumor and immune cells as well as myeloid-derived suppressor cells (MDSCs). MDSCs are immune suppressor cells that help the tumor cells to escape the immune system. The structure of this model is agent-based which makes possible to investigate each component as a separate agent. Moreover, in this model, the effect of low dose 5-fluorouracil (5-FU) on MDSCs depletion was considered.Results:Based on the findings of this study, MDSCs had suppressive effect on increment of immune cell number which consequently result in tumor cells escape the immune cells. It has also been demonstrated that low-dose 5-FU could help immune system eliminate the tumor cells through MDSCs depletion.Conclusion:Using this new agent-based model, multiple injection of low-dose 5-FU could eliminate MDSCs and therefore might have the potential to be considered in treatment of cancers.

This study is designed to present an agent-based model (ABM) to simulate the interactions between tumor cells and the immune system in the melanoma model. The Myeloid-derived Suppressor Cells (MDSCs) and dendritic cells (DCs) are considered in this model as immunosuppressive and antigen-presenting agents respectively.The animal experiment was performed on 68 B16F10 melanoma tumor-bearing C57BL/6 female mice to collect dynamic data for ABM implementation and validation. Animals were divided into 4 groups; group 1 was control (no treatment) while groups 2 and 3 were treated with DC vaccine and low-dose 5- fluorouracil (5-FU) respectively and group 4 was treated with both DC Vaccine and low-dose of 5-FU. The tumor growth rate, number of MDSC, and presence of CD8+/CD107a+ T cells in the tumor microenvironment were evaluated in each group. Firstly, the tumor cells, the effector immune cells, DCs, and the MDSCs have been considered as the agents of the ABM model and their interaction methods have been extracted from the literatureand implemented in the model. Then, the model parameters were estimated by the dynamic data collected from animal experiments.To validate the ABM model, the simulation results were compared with the real data. The results show that the dynamics of the model agents can mimic the relations among considered immune system components to an emergent outcome compatible with real data. The simplicity of the proposed model can help to understand the results of the combinational therapy and make this model a useful tool for studying different scenarios and assessing the combinational results.Determining the role of each component helps to find critical times during tumor progression and change the tumor and immune system balance in favor of the immune system.

Background: There are many studies to investigate the effects of each interacting component of tumor-immune system interactions. In all these studies, the distinct effect of each component was investigated. As the interaction of tumor-immune system has feedback and is complex, the alternation of each component may affect other components indirectly. Objective: Because of the complexities of tumor-immune system interactions, it is important to determine the mutual behavior of such components. We need a careful observation to extract these mutual interactions. Achieving these observations using experiments is costly and time-consuming. Material and Methods: In this experimental and based on mathematical modeling study, to achieve these observations, we presented a fuzzy structured agent-based model of tumor-immune system interactions. In this study, we consider the confronting of the effector cells of the adaptive immune system in the presence of the cytokines of interleukin-2 (IL-2) and transforming growth factor-beta (TGF-β) as a fuzzy structured model. Using the experimental data of murine models of B16F10 cell line of melanoma cancer cells, we optimized the parameters of the model. Results: Using the output of this model, we determined the rules which could occur. As we optimized the parameters of the model using escape state of the tumor and then the rules which we obtained, are the rules of tumor escape. Conclusion: The results showed that using fuzzy structured agent-based model, we are able to show different output of the tumor-immune system interactions, which are caused by the stochastic behavior of each cell. But different output of the model just follow the predetermined behavior, and using this behavior, we can achieve the rules of interactions.