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模拟衰老传播的动态。

Modelling the dynamics of senescence spread.

机构信息

Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.

Centre for Regenerative Medicine, University of Edinburgh, Edinburgh, UK.

出版信息

Aging Cell. 2023 Aug;22(8):e13892. doi: 10.1111/acel.13892. Epub 2023 Jun 8.

Abstract

Cellular senescence is a cell surveillance mechanism that arrests the cell cycle in damaged cells. The senescent phenotype can spread from cell to cell through paracrine and juxtacrine signalling, but the dynamics of this process are not well understood. Although senescent cells are important in ageing, wound healing and cancer, it is unclear how the spread of senescence is contained in senescent lesions. In the absence of the immune system, senescence could theoretically spread infinitely from one cell to another, but this contradicts experimental evidence. To investigate this issue, we developed both a minimal mathematical model and a stochastic simulation of senescence spread. Our results suggest that differences in the number of signalling molecules secreted between subtypes of senescent cells can limit the spread of senescence. We found that dynamic, time-dependent paracrine signalling prevents the uncontrolled spread of senescence, and we demonstrate how model parameters can be determined using Bayesian inference in a proposed experiment.

摘要

细胞衰老(cellular senescence)是一种细胞监视机制,它会使受损细胞的细胞周期停滞。衰老表型可以通过旁分泌和接触抑制信号在细胞间传播,但这一过程的动态机制尚不清楚。尽管衰老细胞在衰老、伤口愈合和癌症中很重要,但尚不清楚衰老在衰老病变中是如何被控制的。在没有免疫系统的情况下,衰老理论上可以从一个细胞无限地传播到另一个细胞,但这与实验证据相矛盾。为了研究这个问题,我们开发了一个最小的数学模型和一个衰老传播的随机模拟。我们的结果表明,不同亚型衰老细胞分泌的信号分子数量的差异可以限制衰老的传播。我们发现,动态的、时变的旁分泌信号可以防止衰老的失控传播,并且我们展示了如何使用贝叶斯推断在一个拟议的实验中确定模型参数。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46be/10410058/4b3c2b16e25a/ACEL-22-e13892-g005.jpg

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