CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题:Dynamics aspects and bifurcations of a tumor-immune system interaction under stationary immunotherapy.
Dynamics aspects and bifurcations of a tumor-immune system interaction under stationary immunotherapy.
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我们考虑一个三维数学模型,用于描述患者的效应细胞、肿瘤细胞和细胞因子(IL-2)之间的相互作用。这被称为 Kirschner-Panetta 模型。
我们的目标是解释肿瘤大小的振荡以及长期肿瘤复发。随后,我们探讨过继性细胞免疫治疗对该模型的影响,并描述在何种情况下肿瘤可以被消除,或在受控方式下长期存在。给出了免疫原性肿瘤的非线性动力学,例如:我们证明相关系统的轨迹是有界的,并且对所有正时间都有定义;存在一些不变子集;存在参数的开子集,使得第一卦限中的系统至多有五个平衡解,其中一个是无肿瘤的,其他是共存的。
我们能够证明从无肿瘤平衡点存在跨临界分岔和叉式分岔。固定一个平衡点并引入小扰动,我们能够证明 Hopf 周期轨道的存在,显示种群之间的循环行为,其中亲本异常生长细胞种群具有强烈优势。上述信息揭示了参数的影响。在我们的研究中,我们观察到我们的数学模型表现出非常丰富的动力学行为,并且参数 μ̃(效应细胞的死亡率)和 p̃ 1(由细胞因子 IL-2 刺激的效应细胞的产生率)起着重要作用。更准确地说,在我们的方法中,不等式 μ̃ 2 >p̃ 1 非常重要,也就是说,效应细胞的死亡率大于由细胞因子 IL-2 刺激的效应细胞的产生率。最后,还给出了医学意义以及一组支持数学结果的数值模拟。
We consider a three-dimensional mathematical model that describes the interaction between the effector cells, tumor cells, and the cytokine (IL-2) of a patient. This is called the Kirschner-Panetta model.
Our objective is to explain the tumor oscillations in tumor sizes as well as long-term tumor relapse.
We then explore the effects of adoptive cellular immunotherapy on the model and describe under what circumstances the tumor can be eliminated or can remain over time but in a controlled manner.
Nonlinear dynamics of immunogenic tumors are given, for example: we prove that the trajectories of the associated system are bounded and defined for all positive time; there are some invariant subsets; there are open subsets of parameters, such that the system in the first octant has at most five equilibrium solutions, one of them is tumor-free and the others are of co-existence.
We are able to prove the existence of transcritical and pitchfork bifurcations from the tumor-free equilibrium point. Fixing an equilibrium and introducing a small perturbation, we are able to show the existence of a Hopf periodic orbit, showing a cyclic behavior among the population, with a strong dominance of the parental anomalous growth cell population. The previous information reveals the effects of the parameters.
In our study, we observe that our mathematical model exhibits a very rich dynamic behavior and the parameter μ̃ (death rate of the effector cells) and p̃ 1 (production rate of the effector cell stimulated by the cytokine IL-2) plays an important role. More precisely, in our approach the inequality μ̃ 2 >p̃ 1 is very important, that is, the death rate of the effector cells is greater than the production rate of the effector cell stimulated by the cytokine IL-2.
Finally, medical implications and a set of numerical simulations supporting the mathematical results are also presented.
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