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Stochastic Simulation of Pattern Formation in Growing Tissue: A Multilevel Approach

机译:生长组织中模式形成的随机模拟:一种多层次方法

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摘要

We take up the challenge of designing realistic computational models of large interacting cell populations. The goal is essentially to bring Gillespie’s celebrated stochastic methodology to the level of an interacting population of cells. Specifically, we are interested in how the gold standard of single-cell computational modeling, here taken to be spatial stochastic reaction–diffusion models, may be efficiently coupled with a similar approach at the cell population level. Concretely, we target a recently proposed set of pathways for pattern formation involving Notch–Delta signaling mechanisms. These involve cell-to-cell communication as mediated both via direct membrane contact sites and via cellular protrusions. We explain how to simulate the process in growing tissue using a multilevel approach and we discuss implications for future development of the associated computational methods.
机译:我们接受了设计大型相互作用细胞群体的现实计算模型的挑战。目的实质上是将吉莱斯皮著名的随机方法学带到相互作用的细胞群体的水平。特别是,我们对单细胞计算建模的金标准(这里被认为是空间随机反应扩散模型)感兴趣,如何将其与细胞群体水平上的类似方法有效地结合在一起。具体来说,我们针对的是一组最近提出的涉及Notch-Delta信号传导机制的模式形成途径。这些涉及通过直接的膜接触部位和通过细胞突起介导的细胞间通信。我们解释了如何使用多级方法来模拟组织生长过程,并讨论了相关计算方法的未来发展意义。

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