- BIMSA 计算数学讨论班

Mathematical model of tumor-macrophage interactions: Elucidating the tumor-driven macrophage phenotype reprogramming

组织者

塔赫蕾·埃夫特哈里 , 胡丕丕 , 梁鑫 , 马志婷 , 哈米德·莫菲迪 , 苏春梅 , 阿克塞尔·特恩奎斯特 , 王丽 , 熊繁升 , 杨朔 , 杨武岳

演讲者

张海峰

时间

2026年06月17日 13:30 至 14:30

地点

Online

线上

Zoom 518 868 7656 (BIMSA)

摘要

The interplay between tumor cells and macrophages plays a central regulatory role in cancer progression. In this study, we developed a mathematical model that incorporates tumor cells, M1-type macrophages, M2-type macrophages, and an M3-type macrophage population characterized by dual phenotypic features. First, we analyzed the fundamental mathematical properties of the model and derived the conditions under which the system attains a tumor‑free equilibrium or a coexistence state of tumor and immune cells. Second, global sensitivity analysis revealed that key parameters governing macrophage polarization and intercellular communication vary dynamically during tumor development. Bifurcation analysis further identified the polarization rate of M1‑type macrophages and the baseline level of resting macrophages as critical determinants of the system's dynamical behavior. Notably, using approximate Bayesian computation for parameter inference and dynamic simulations, the model successfully recapitulated the evolutionary trajectories of eight tumor samples. The results demonstrate that lower tumor burden is significantly associated with higher M1‑type macrophage infiltration and delayed peak time of M3‑type macrophage activation. Moreover, survival analysis indicated that both enhanced M1‑type macrophage infiltration and delayed peak time of M3‑type macrophage activation are correlated with longer survival time. In summary, this study provides a theoretical framework for understanding the dynamic mechanisms underlying tumor-macrophage interactions and proposes two model-derived parameters as candidate prognostic markers: the level of M1-type macrophage infiltration and the peak time of M3-type macrophage activation. These predictions, while grounded in the model, require further experimental and clinical validation.

演讲者介绍

Haifeng Zhang obtained Ph.D. degree from Department of Mathematical Sciences at Tsinghua University, China in 2023. Currently, he is a lecturer in School of Mathematical Sciences, Jiangsu University. His current research interests include mathematical biology, differential equations, and control theory.