推导和模拟活跃细胞群的计算模型:避免重叠、可变形性、细胞-细胞连接和细胞骨架力如何影响排列。

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS PLoS Computational Biology Pub Date : 2024-07-29 eCollection Date: 2024-07-01 DOI:10.1371/journal.pcbi.1011879
Vivienne Leech, Fiona N Kenny, Stefania Marcotti, Tanya J Shaw, Brian M Stramer, Angelika Manhart
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引用次数: 0

摘要

细胞群的集体排列是生物学中经常观察到的现象。一个重要的例子是健康或疤痕组织中的成纤维细胞排列。在这项工作中,我们推导并模拟了一个基于代理的机理模型,该模型描述了活跃运动和相互作用的细胞的集体行为,重点是理解集体排列。推导策略基于能量最小化。该模型的基本要素来自于不同成纤维细胞群排列行为的数据,包括自我推进、避免重叠、可变形性、细胞-细胞连接和细胞骨架力。我们发现,自推进速度与避免重叠之间存在一个最佳比例,可最大限度地实现集体排列。此外,我们还发现可变形性有助于排列,而细胞-细胞连接本身会阻碍排列。然而,如果细胞骨架力通过细胞-细胞连接传递,我们就能观察到大空间尺度上强烈的集体排列。
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Derivation and simulation of a computational model of active cell populations: How overlap avoidance, deformability, cell-cell junctions and cytoskeletal forces affect alignment.

Collective alignment of cell populations is a commonly observed phenomena in biology. An important example are aligning fibroblasts in healthy or scar tissue. In this work we derive and simulate a mechanistic agent-based model of the collective behaviour of actively moving and interacting cells, with a focus on understanding collective alignment. The derivation strategy is based on energy minimisation. The model ingredients are motivated by data on the behaviour of different populations of aligning fibroblasts and include: Self-propulsion, overlap avoidance, deformability, cell-cell junctions and cytoskeletal forces. We find that there is an optimal ratio of self-propulsion speed and overlap avoidance that maximises collective alignment. Further we find that deformability aids alignment, and that cell-cell junctions by themselves hinder alignment. However, if cytoskeletal forces are transmitted via cell-cell junctions we observe strong collective alignment over large spatial scales.

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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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