肿瘤巨噬细胞极化延迟相互作用的数学建模与Hopf分岔分析。

IF 1.8 4区 数学 Q3 ECOLOGY
Journal of Biological Dynamics Pub Date : 2025-12-01 Epub Date: 2025-05-26 DOI:10.1080/17513758.2025.2508240
Jianping Li, Nan Liu, Danni Wang, Hongli Yang
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引用次数: 0

摘要

巨噬细胞具有抗肿瘤和促肿瘤双重作用。时间延迟在现实系统中是常见的,但其对肿瘤-巨噬细胞动力学的影响尚不清楚。本文建立了一种新的肿瘤-巨噬细胞时滞模型。该模型描述了肿瘤细胞(T)、经典活化的巨噬细胞(M1)、交替活化的巨噬细胞(M2)和失活的巨噬细胞(M0)之间的相互作用。计算了系统的解,分析了系统的平衡稳定性。随后建立了Hopf分岔的存在性。在内部平衡附近存在分岔的周期解,表明肿瘤细胞和巨噬细胞可以长期共存,也存在肿瘤复发的可能。此外,利用范式定理和中心流形定理研究了Hopf分岔的性质。敏感性分析强调了参数对肿瘤种群动态的影响。数值模拟结果验证了该理论的正确性,为肿瘤系统分析提供了一个有用的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mathematical modeling and Hopf bifurcation analysis of tumor macrophage interaction with polarization delay.

Macrophages have both anti-tumor and pro-tumor effects. Time delay is commonly observed in real systems, yet its impact on tumor-macrophage dynamics remains unclear. This paper develops a new tumor-macrophage model with time delay. The model describes the interactions between tumor cells (T), the classically activated macrophages (M1), the alternatively activated macrophages (M2), and the inactive macrophages (M0). The system's solution is computed, and equilibrium stability is analyzed. The existence of Hopf bifurcation is subsequently established. There exists bifurcating periodic solutions near the internal equilibrium, showing tumor cells and macrophages can coexist in the long term, as well as the potential for tumor relapse. Furthermore, the normal form and center manifold theorem are utilized to study the nature of Hopf bifurcation. Sensitivity analysis highlights the effect of parameters on tumor population dynamics. Numerical simulations validate the theory, elaborating the model can serve as a useful tool for tumor system analysis.

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来源期刊
Journal of Biological Dynamics
Journal of Biological Dynamics ECOLOGY-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
4.90
自引率
3.60%
发文量
28
审稿时长
33 weeks
期刊介绍: Journal of Biological Dynamics, an open access journal, publishes state of the art papers dealing with the analysis of dynamic models that arise from biological processes. The Journal focuses on dynamic phenomena at scales ranging from the level of individual organisms to that of populations, communities, and ecosystems in the fields of ecology and evolutionary biology, population dynamics, epidemiology, immunology, neuroscience, environmental science, and animal behavior. Papers in other areas are acceptable at the editors’ discretion. In addition to papers that analyze original mathematical models and develop new theories and analytic methods, the Journal welcomes papers that connect mathematical modeling and analysis to experimental and observational data. The Journal also publishes short notes, expository and review articles, book reviews and a section on open problems.
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