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Stochastic differential equation modelling of cancer cell migration and tissue invasion

机译:癌细胞迁移和组织侵袭的随机微分方程建模

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摘要

Abstract Invasion of the surrounding tissue is a key aspect of cancer growth and spread involving a coordinated effort between cell migration and matrix degradation, and has been the subject of mathematical modelling for almost 30 years. In this current paper we address a long-standing question in the field of cancer cell migration modelling. Namely, identify the migratory pattern and spread of individual cancer cells, or small clusters of cancer cells, when the macroscopic evolution of the cancer cell colony is dictated by a specific partial differential equation (PDE). We show that the usual heuristic understanding of the diffusion and advection terms of the PDE being one-to-one responsible for the random and biased motion of the solitary cancer cells, respectively, is not precise. On the contrary, we show that the drift term of the correct stochastic differential equation scheme that dictates the individual cancer cell migration, should account also for the divergence of the diffusion of the PDE. We support our claims with a number of numerical experiments and computational simulations.
机译:摘要 周围组织的侵袭是癌症生长和扩散的一个关键方面,涉及细胞迁移和基质降解之间的协调努力,并且近 30 年来一直是数学建模的主题。在这篇论文中,我们解决了癌细胞迁移建模领域一个长期存在的问题。也就是说,当癌细胞集落的宏观进化由特定的偏微分方程 (PDE) 决定时,确定单个癌细胞或小癌细胞簇的迁移模式和扩散。我们表明,对偏微分方程的扩散和平流项的通常启发式理解是一对一的,分别负责孤立癌细胞的随机和偏置运动,并不精确。相反,我们表明,决定单个癌细胞迁移的正确随机微分方程方案的漂移项也应该解释偏微分方程扩散的发散。我们通过大量的数值实验和计算模拟来支持我们的主张。

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