生长组织中图案形成的随机模拟：一种多级方法
Stochastic simulation of pattern formation in growing tissue: a multilevel approach
摘要：
我们接受了设计大的相互作用的细胞群体的现实计算模型的挑战。我们的目标基本上是把吉莱斯皮著名的随机方法的水平上相互作用的细胞群体。具体而言，我们感兴趣的是如何的黄金标准的单细胞计算建模，这里采取的空间随机反应-扩散模型，可以有效地耦合在细胞群体水平的类似方法。具体地说，我们针对最近提出的一组涉及Notch-Delta信号传导机制的模式形成途径。这些涉及通过直接膜接触位点以及通过细胞突起介导的细胞间通讯。我们解释了如何使用多层次的方法来模拟生长组织的过程，我们讨论了相关的计算方法的未来发展的影响。
英文摘要：
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 as well as 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.
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