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Statistical inference of the mechanisms driving collective cell movement

机译:推动集体细胞运动的机制的统计推断

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摘要

Numerous biological processes, many impacting on human health, rely on collective celludmovement. We develop nine candidate models, based on advection-diffusion partial differential equations, to describe various alternative mechanisms that may drive cell movement. The parameters of these models were inferred from one-dimensional projections of laboratory observations of Dictyostelium discoideum cells by sampling from the posterior distribution using the delayed rejection adaptive Metropolis algorithm (DRAM). The best model was selected using the Widely Applicable Information Criterion (WAIC). We conclude that cell movement in our study system was driven both by a self-generated gradient in an attractant that the cells could deplete locally, and by chemical interactions between the cells.
机译:许多影响人类健康的生物过程都依赖集体细胞的移动。我们基于对流扩散偏微分方程,开发了九种候选模型,以描述可能驱动细胞运动的各种替代机制。这些模型的参数是通过使用延迟拒绝自适应Metropolis算法(DRAM)从后分布采样来从盘状网柄菌的实验室观察结果的一维投影中推论得出的。使用广泛适用的信息标准(WAIC)选择了最佳模型。我们得出结论,我们研究系统中的细胞运动既受细胞可能局部耗尽的引诱剂中自身产生的梯度的驱动,又受细胞之间化学相互作用的驱动。

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