Binny Rachelle N, James Alex, Plank Michael J
School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand.
Te Pūnaha Matatini, A New Zealand Centre of Research Excellence, Auckland, New Zealand.
Bull Math Biol. 2016 Nov;78(11):2277-2301. doi: 10.1007/s11538-016-0222-9. Epub 2016 Oct 19.
Collective cell migration and proliferation are integral to tissue repair, embryonic development, the immune response and cancer. Central to collective cell migration and proliferation are interactions among neighbouring cells, such as volume exclusion, contact inhibition and adhesion. These individual-level processes can have important effects on population-level outcomes, such as growth rate and equilibrium density. We develop an individual-based model of cell migration and proliferation that includes these interactions. This is an extension of a previous model with neighbour-dependent directional bias to incorporate neighbour-dependent proliferation and death. A deterministic approximation to this individual-based model is derived using a spatial moment dynamics approach, which retains information about the spatial structure of the cell population. We show that the individual-based model and spatial moment model match well across a range of parameter values. The spatial moment model allows insight into the two-way interaction between spatial structure and population dynamics that cannot be captured by traditional mean-field models.
集体细胞迁移和增殖对于组织修复、胚胎发育、免疫反应及癌症而言不可或缺。相邻细胞间的相互作用,如体积排斥、接触抑制和黏附,是集体细胞迁移和增殖的核心要素。这些个体层面的过程会对群体层面的结果产生重要影响,例如生长速率和平衡密度。我们构建了一个包含这些相互作用的基于个体的细胞迁移和增殖模型。这是对先前具有邻域依赖方向偏差模型的扩展,以纳入邻域依赖的增殖和死亡。使用空间矩动力学方法推导出该基于个体模型的确定性近似,它保留了关于细胞群体空间结构的信息。我们表明,基于个体的模型和空间矩模型在一系列参数值范围内匹配良好。空间矩模型能够洞察空间结构与群体动态之间的双向相互作用,而这是传统平均场模型无法捕捉到的。