首页> 外文期刊>Journal of Immunological Methods >Combining cellular automata and Lattice Boltzmann method to model multiscale avascular tumor growth coupled with nutrient diffusion and immune competition.
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Combining cellular automata and Lattice Boltzmann method to model multiscale avascular tumor growth coupled with nutrient diffusion and immune competition.

机译:结合细胞自动机和Lattice Boltzmann方法来模拟多尺度无血管肿瘤生长以及营养物扩散和免疫竞争。

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In the last decades the Lattice Boltzmann method (LB) has been successfully used to simulate a variety of processes. The LB model describes the microscopic processes occurring at the cellular level and the macroscopic processes occurring at the continuum level with a unique function, the probability distribution function. Recently, it has been tried to couple deterministic approaches with probabilistic cellular automata (probabilistic CA) methods with the aim to model temporal evolution of tumor growths and three dimensional spatial evolution, obtaining hybrid methodologies. Despite the good results attained by CA-PDE methods, there is one important issue which has not been completely solved: the intrinsic stochastic nature of the interactions at the interface between cellular (microscopic) and continuum (macroscopic) level. CA methods are able to cope with the stochastic phenomena because of their probabilistic nature, while PDE methods are fully deterministic. Even if the coupling is mathematically correct, there could be important statistical effects that could be missed by the PDE approach. For such a reason, to be able to develop and manage a model that takes into account all these three level of complexity (cellular, molecular and continuum), we believe that PDE should be replaced with a statistic and stochastic model based on the numerical discretization of the Boltzmann equation: The Lattice Boltzmann (LB) method. In this work we introduce a new hybrid method to simulate tumor growth and immune system, by applying Cellular Automata Lattice Boltzmann (CA-LB) approach.
机译:在过去的几十年中,格子Boltzmann方法(LB)已成功用于模拟各种过程。 LB模型以独特的功能(概率分布函数)描述了发生在细胞水平的微观过程和发生在连续谱水平的宏观过程。最近,已经尝试将确定性方法与概率细胞自动机(概率CA)方法相结合,目的是对肿瘤生长的时间演变和三维空间演变进行建模,从而获得混合方法。尽管CA-PDE方法取得了良好的结果,但仍有一个尚未完全解决的重要问题:细胞(微观)和连续谱(宏观)之间的界面相互作用的内在随机性。 CA方法由于其概率性而能够应付随机现象,而PDE方法则是完全确定性的。即使耦合在数学上是正确的,PDE方法也可能遗漏重要的统计效果。因此,为了能够开发和管理一个考虑到所有这三个复杂度(细胞,分子和连续性)的模型,我们认为应该将PDE替换为基于数值离散化的统计和随机模型。玻尔兹曼方程的公式:格子波尔兹曼(LB)方法。在这项工作中,我们采用细胞自动机格子Boltzmann(CA-LB)方法,引入了一种新的混合方法来模拟肿瘤生长和免疫系统。

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