In silico optimization of heparin microislands in microporous annealed particle hydrogel for endothelial cell migration.
In silico optimization of heparin microislands in microporous annealed particle hydrogel for endothelial cell migration.
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DOI:
10.1016/j.actbio.2022.05.049
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发表时间:
2022-08
影响因子:
9.7
通讯作者:
Griffin, Donald R.
中科院分区:
文献类型:
--
作者:
Pruett, Lauren J.;Taing, Alex L.;Singh, Neharika S.;Peirce, Shayn M.;Griffin, Donald R.
Biomaterials capable of generating growth factor gradients have shown success in guiding tissue regeneration, as growth factor gradients are a physiologic driver of cell migration. Of particular importance, a focus on promoting endothelial cell migration is vital to angiogenesis and new tissue formation. Microporous Annealed Particle (MAP) scaffolds represent a unique niche in the field of regenerative biomaterials research as an injectable biomaterial with an open porosity that allows cells to freely migrate independent of material degradation. Recently, we have used the MAP platform to heterogeneously include spatially isolated heparin-modified microgels (heparin microislands) which can sequester growth factors and guide cell migration. In in vitro sprouting angiogenesis assays, we observed a parabolic relationship between the percentage of heparin microislands and cell migration, where 10% heparin microislands had more endothelial cell migration compared to 1% and 100%. Due to the low number of heparin microisland ratios tested, we hypothesize the spacing between microgels can be further optimized. Rather than use purely empirical methods, which are both expensive and time intensive, we believe this challenge represents an opportunity to use computational modeling. Here we present the first agent-based model of a MAP scaffold to optimize the ratio of heparin microislands. Specifically, we develop a two-dimensional model in Hybrid Automata Library (HAL) of endothelial cell migration within the unique MAP scaffold geometry. Finally, we present how our model can accurately predict cell migration trends in vitro, and these studies provide insight on how computational modeling can be used to design particle-based biomaterials.
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影响因子:
14
作者:
Mehdizadeh, Hamidreza;Sumo, Sami;Cinar, Ali
通讯作者:
Cinar, Ali
影响因子:
9.7
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DOI:
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发表时间:
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期刊:
Nature reviews. Materials
影响因子:
--
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通讯作者:
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影响因子:
9.7
作者:
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通讯作者:
Kiick, Kristi L.