由交叉扩散诱导的癌症免疫治疗模型中的图灵模式
Turing pattern induced by cross-diffusion in cancer immunotherapy model
AI总结：
本文通过交叉扩散效应研究癌症免疫治疗模型中的图灵模式，分析了系统平衡点稳定性及空间结构形成机制。
摘要：
本文研究了一个数学模型，描述效应细胞（E）、癌细胞（T）和IL-2复合物（IL）之间的相互作用。所考虑的模型是对S.Suddin等人于2021年提出的空间癌症免疫治疗模型的推广，考虑了某些交叉扩散效应。这些修改使我们能够描述两种有生物学相关性的场景：接受过细胞免疫治疗（ACI）的患者和未接受任何治疗/治疗的患者。交叉扩散效应在肿瘤细胞和免疫系统之间的相互作用中尤其相关，事实上它们在免疫反应动态中起关键作用，不能被忽视。我们分析了均匀系统的平衡点，以及它们的稳定性和分支机制。此外，采用图灵方法分析反应扩散系统，研究了扩散驱动的不稳定性以及空间规则结构（时间稳定）的出现，即模式。最后，基于有限差分法（FDM）进行数值模拟，展示了上述两种场景。
英文摘要：
In this paper, we investigate a mathematical model describing the interactions between effector cells (E), cancer cells (T), and the IL-2 compound (IL). The model considered here is a generalization, taking into account some cross-diffusion effects, of a spatial cancer immunotherapy model proposed by S. Suddin et al in 2021. These modifications allow us to describe two biologically relevant scenarios: a patient treated with Adoptive Cell Immunotherapy (ACI) and a patient not receiving any treatment/therapy. Cross-diffusion effects are particularly relevant in the interactions between tumor cells and the immune system, in fact they play a key role in immune response dynamics and cannot be neglected. We analyze the equilibrium points of the homogeneous system, along with their stability and bifurcation mechanisms. Furthermore, adopting the Turing approach for reaction-diffusion systems, we investigate the diffusion-driven instability and the emergence of spatial regular structures (stationary in time), i.e. the patterns. Finally, numerical simulations based on the Finite Difference Method (FDM) are presented for the two previously mentioned scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
