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Combining in silico and in vitro models to inform cell seeding strategies in tissue engineering

机译:组合在硅和体外模型中以通知组织工程中的细胞播种策略

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摘要

The seeding density of therapeutic cells in engineered tissue impacts both cell survival and vascularization. Excessively high seeded cell densities can result in increased death and thus waste of valuable cells, whereas lower seeded cell densities may not provide sufficient support for the tissue in vivo, reducing efficacy. Additionally, the production of growth factors by therapeutic cells in low oxygen environments offers a way of generating growth factor gradients, which are important for vascularization, but hypoxia can also induce unwanted levels of cell death. This is a complex problem that lends itself to a combination of computational modelling and experimentation. Here, we present a spatio-temporal mathematical model parametrized using in vitro data capable of simulating the interactions between a therapeutic cell population, oxygen concentrations and vascular endothelial growth factor (VEGF) concentrations in engineered tissues. Simulations of collagen nerve repair constructs suggest that specific seeded cell densities and non-uniform spatial distributions of seeded cells could enhance cell survival and the generation of VEGF gradients. These predictions can now be tested using targeted experiments.
机译:工程组织中治疗细胞的播种密度会影响细胞存活和血管化。过高的播种细胞密度可以导致死亡增加,从而浪费有价值的细胞,而较低的种子细胞密度可能无法为体内组织提供足够的载体,降低功效。另外,低氧环境中治疗细胞的生长因子的产生提供了一种产生生长因子梯度的方法,这对血管化是重要的,但缺氧也可以诱导不需要的细胞死亡水平。这是一个复杂的问题,可以自身归因于计算建模和实验的组合。在这里,我们使用能够模拟工程组织中治疗细胞群,氧气浓度和血管内皮生长因子(VEGF)浓度之间的相互作用的体外数据来介绍一种时空数学模型参数化。胶原神经修复构建体的模拟表明,种子细胞的特定种子细胞密度和不均匀的空间分布可以增强细胞存活和VEGF梯度的产生。现在可以使用目标实验测试这些预测。

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