一种模拟神经嵴细胞集体趋化的有偏随机漫步方法

Viktoria Freingruber, Kevin J. Painter, Mariya Ptashnyk, Linus Schumacher
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引用次数: 0

摘要

集体细胞迁移是一种多细胞现象,出现在各种生物环境中,包括癌症和胚胎发育。“集体性”可以通过细胞间的相互作用来促进,如asco-attraction和运动的接触抑制。这些机制通过影响小gtp酶(如Rac1)的细胞内动力学,作用于细胞极性,这对定向细胞运动至关重要。为了模拟这些动态,我们引入了一个有偏差的随机游走模型,其中偏差取决于Rac1的内部状态,而Rac1的状态受细胞间相互作用和化学吸引线索的影响。在广泛的模拟研究中,我们演示并解释了在各种场景中引入的模型的范围和适用性。使用有偏随机游走模型可以推导出相应的细胞密度偏微分方程,同时仍然保持一定水平的细胞内细节。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A biased random walk approach for modeling the collective chemotaxis of neural crest cells
Collective cell migration is a multicellular phenomenon that arises in various biological contexts, including cancer and embryo development. "Collectiveness" can be promoted by cell-cell interactions such as co-attraction and contact inhibition of locomotion. These mechanisms act on cell polarity, pivotal for directed cell motility, through influencing the intracellular dynamics of small GTPases such as Rac1. To model these dynamics we introduce a biased random walk model, where the bias depends on the internal state of Rac1, and the Rac1 state is influenced by cell-cell interactions and chemoattractive cues. In an extensive simulation study we demonstrate and explain the scope and applicability of the introduced model in various scenarios. The use of a biased random walk model allows for the derivation of a corresponding partial differential equation for the cell density while still maintaining a certain level of intracellular detail from the individual based setting.
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