通过数学建模探索免疫反应对肿瘤异质性的影响

Diksha Gautam, Sanjeev Kumar, Rashmi Sharma, Deepshikha Dixit
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摘要

目的:本文对包括肿瘤细胞在内的各种细胞群体增长数学模型及其动态进行了研究:我们将模型分为五类:指数模型、逻辑模型、时间检验模型、异质模型和免疫学模型。通过数学建模,我们可以了解肿瘤随着时间的推移是如何发展的,以及肿瘤的增殖率是如何变得更加危险的。为了探索免疫反应对肿瘤异质性的影响,我们建立了一个肿瘤生长的反应-扩散模型,其中包含肿瘤-免疫相互作用以及肿瘤突变和克隆扩张机制。我们利用数值模拟来研究免疫反应的变化如何影响肿瘤的异质性:我们的研究结果表明,免疫反应越强,肿瘤群体的同质性越高,这表明增强免疫反应可以降低肿瘤异质性,改善治疗效果:这些结果对开发针对免疫系统的治疗策略以对抗肿瘤异质性具有重要意义。
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Exploring the impact of immune response on tumor heterogeneity through mathematical modeling
Aim: This article presents an investigation into various mathematical models for cell population growth, including tumor cells, and their dynamics. Methods: We classify the models into five categories: exponential, logistic, time-tested, heterogeneous, and immunology. Mathematical modeling provides insights into the development of tumors over time and how their proliferation rate becomes more dangerous. To explore the impact of immune response on tumor heterogeneity, we develop a reaction-diffusion model of tumor growth that incorporates tumor-immune interactions and a mechanism for tumor mutation and clonal expansion. We use numerical simulations to investigate how variation in immune response affects tumor heterogeneity. Results: Our findings show that a stronger immune response leads to greater homogeneity in the tumor population, which suggests that enhancing immune response could reduce tumor heterogeneity and improve treatment outcomes. Conclusions: These results have important implications for the development of therapeutic strategies targeting the immune system to combat tumor heterogeneity.
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