Integrated data-driven modeling and experimental optimization of granular hydrogel matrices
Integrated data-driven modeling and experimental optimization of granular hydrogel matrices
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DOI：
10.1016/j.matt.2023.01.011
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发表时间：
2023-01
期刊：
影响因子：
18.9
通讯作者：
C. Verheyen;Sébastien Uzel;Armand Kurum;E. Roche;J. Lewis
中科院分区：
文献类型：
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作者：
C. Verheyen;Sébastien Uzel;Armand Kurum;E. Roche;J. Lewis
颗粒水凝胶基质已成为细胞封装、生物打印和组织工程的有希望的候选者。然而，鉴于这些材料广泛的成分和加工参数空间，设计和优化这些材料仍然具有挑战性。在这里，我们结合实验和计算来创建由基于藻酸盐的生物块组成的颗粒基质，其具有受控的结构、流变特性和可注射性。在每个实验阶段之后应用自定义机器学习管道，以自动将多维输入输出模式映射到压缩数据驱动模型。这些模型用于评估通用的可预测性并定义高级设计规则以指导后续的开发和表征阶段。我们的集成模块化方法为理解和控制复杂软材料的行为开辟了新途径。
Granular hydrogel matrices have emerged as promising candidates for cell encapsulation, bioprinting, and tissue engineering. However, it remains challenging to design and optimize these materials given their broad compositional and processing parameter space. Here, we combine experimentation and computation to create granular matrices composed of alginate-based bioblocks with controlled structure, rheological properties, and injectability profiles. A custom machine learning pipeline is applied after each phase of experimentation to automatically map the multidimensional input-output patterns into condensed data-driven models. These models are used to assess generalizable predictability and define high-level design rules to guide subsequent phases of development and characterization. Our integrated, modular approach opens new avenues to understanding and controlling the behavior of complex soft materials.