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arXiv 1802.00655math.APcs.NAmath.NA
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肿瘤生长模型与营养成分的分析与计算:从细胞密度模型到自由边界动力学

Analysis and computation of some tumor growth models with nutrient: from cell density models to free boundary dynamics

Duke University(杜克大学);School of Mathematics and Institute of Natural Sciences, MOE-LSC, Shanghai JiaoTong University(上海交通大学数学学院与自然科学研究院);Department of Mathematics, Computational and Data-Enabled Science and Engineering Program, State University of New York at Buffalo(纽约州立大学布法罗分校数学系、计算与数据驱动科学与工程计划);Beijing International Center for Mathematical Research, Peking University(北京大学北京国际数学研究中心)

Jian-Guo Liu, Min Tang, Li Wang, Zhennan Zhou

AI总结:

本文研究肿瘤生长模型与营养成分模型,揭示细胞密度模型在γ→∞时趋近于海勒-沙夫流模型,推导解析解并验证数值方案与自由边界动力学的联系。

摘要:

在本文中,我们研究了肿瘤生长方程以及各种营养成分模型,包括体外模型和体内模型。在细胞密度层面,肿瘤密度n的空间可用性由达西定律通过压力p(n)=n^γ控制。对于有限的γ,我们证明了肿瘤生长模型的一些先验估计,例如营养密度的有界性,以及肿瘤密度的非负性和增长估计。当γ→∞时,细胞密度模型形式上收敛到海勒-沙夫流模型,这些模型确定了肿瘤组织在不可压缩极限下的自由边界动力学。我们推导了几种海勒-沙夫流模型的解析解,这些解作为肿瘤前沿传播几何运动的基准解。最后,我们应用一种守恒且保持正性的数值方案到细胞密度模型中,数值结果验证了细胞密度模型与自由边界动力学模型之间的联系。

英文摘要:

In this paper, we study the tumor growth equation along with various models for the nutrient component, including the \emph{in vitro} model and the \emph{in vivo} model. At the cell density level, the spatial availability of the tumor density $n$ is governed by the Darcy law via the pressure $p(n)=n^γ$. For finite $γ$, we prove some a priori estimates of the tumor growth model, such as boundedness of the nutrient density, and non-negativity and growth estimate of the tumor density. As $γ\rightarrow \infty$, the cell density models formally converge to Hele-Shaw flow models, which determine the free boundary dynamics of the tumor tissue in the incompressible limit. We derive several analytical solutions to the Hele-Shaw flow models, which serve as benchmark solutions to the geometric motion of tumor front propagation. Finally, we apply a conservative and positivity preserving numerical scheme to the cell density models, with numerical results verifying the link between cell density models and the free boundary dynamical models.

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。

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