Excellence in Research: Convergent Physics-based Data-driven Bioprinting of Regenerative Tissues for Future Biomanufacturing

再生组织工程在用干细胞替代患病和功能障碍的器官方面有着巨大的希望。然而，干细胞工程中组织支架的制造取决于几个因素的相互作用，如生化信号，细胞排列和相关工艺参数。生物制造研究进展的主要障碍是缺乏正式的指导原则和实时过程监测，以及进行基于干细胞的生物打印实验所需的过高资源。这一关键屏障限制了控制多种细胞类型的生长行为以形成用于器官替代的活组织构建体的能力。为了解决这些问题，这项卓越研究奖将研究基于物理的模型，将传感器数据与机器学习算法和实验相结合，以创建生物打印过程的数字孪生模型。发现驱动的研究将产生一系列知识，通过再生组织工程的生物打印设计和制造规则的开源存储库来指导研究人员和工业用户。包括生物制造课程开发在内的教育工作将影响北卡罗来纳州农业和技术州立大学的代表性不足的学生，该大学是全国最大的历史黑人学院和大学之一。与维克森林再生医学研究所（WFIRM）的一个学者交流项目将在生物制造、数据分析和指导程序方面对学生进行培训。该项目的总体目标是在混合生物打印中建立一个基于物理学的数据驱动结构，以定制工程师基于干细胞的组织构建。具体目标包括（1）创建一个强大的框架，整合计算建模，实验结果和基于工业物联网的生物打印支架健康监测技术，（2）使用基于混合物理学的数据驱动模型理解生化线索和纳米级拓扑结构的吸附配置的组合效应，以及（3）研究相互作用的材料之间的关系，用于闭环控制的生物打印的工艺参数和微环境变量。该团队计划采用一种融合的方法，其中将增加计算建模数据、实验研究、实时原位传感器和诊断，以研究生物打印过程参数。机器学习算法将应用于合并的数据集，以解开地形，机械刺激和生化线索之间的潜在隐藏模式，以确定细胞的命运和功能。将开发混合预测模型，以实现对生物打印过程和材料配方的实时监测和控制。将在WFIRM进行细胞增殖、组织学染色和生化测定，以验证混合模型。输入输出关系映射将使集成的过程控制，监控和智能过程数据分析成为生物制造工业4.0。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

Regenerative tissue engineering holds great promise to replace diseased and dysfunctional organs with stem cells. The fabrication of tissue scaffolds in stem-cell engineering is, however, dependent on an interplay of several factors such as biochemical signaling, cellular arrangement and related process parameters. Key impediments in progressing biomanufacturing research are the lack of formal guiding principles and real-time process monitoring, in addition to the exorbitant resources required to conduct stem-cell based bioprinting experiments. This critical barrier has limited the ability to control the growth behavior of multiple cell types to form viable tissue constructs for organ replacement. To address these issues, this Excellent in Research award will investigate physics-based models that integrate sensor data with machine learning algorithms and experimentation to create a digital twin of bioprinting processes. The discovery-driven research will generate a body of knowledge to guide researchers and industrial users through an open-source repository of Bioprinting Design and Manufacturing rules for regenerative tissue engineering. The education efforts including the development of biomanufacturing coursework will impact underrepresented students at the North Carolina Agricultural and Technical State University, one of the nation’s largest historical black colleges and universities, and beyond. A scholar exchange program with the Wake Forest Institute for Regenerative Medicine (WFIRM) will train student cohorts in biomanufacturing, data-analytics and guiding procedures.The overall goal of this project is to establish a physics-based data-driven structure in hybrid bioprinting to custom engineer stem-cell based tissue constructs. The specific objectives include (1) creating a robust framework integrating computational modeling, experimental results and industrial internet of things based scaffold health monitoring techniques for bioprinting, (2) understanding the combinatorial effect of adsorption configurations of biochemical cues and nanoscale topologies using hybrid physics-based data-driven models, and (3) investigating relationships among interacting materials, process parameters and microenvironmental variables of bioprinting for closed-loop control. The team plans a convergent approach wherein, computational modeling data, experimental research, real-time in-situ sensors and diagnostics will be augmented to investigate bioprinting process parameters. Machine learning algorithms will be applied to the consolidated data sets to unravel the underlying hidden patterns between topography, mechanical stimuli and biochemical cues in determining cell fate and function. The hybrid predictive models will be developed to enable real-time monitoring and control of the bioprinting process and material formulations. Cell proliferation, histological staining, and biochemical assays will be performed at the WFIRM to validate the hybrid models. Input-output relationship mappings will enable integrated process control, monitoring and smart process data analytics towards a Biomanufacturing Industry 4.0.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.