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

再生组织工程学很有希望用干细胞取代疾病和功能失调的器官。然而，干细胞工程中组织支架的制造依赖于几个因素的相互作用，如生化信号、细胞排列和相关的工艺参数。生物制造研究进展的关键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。这一关键障碍限制了控制多种细胞类型的生长行为以形成可用于器官替代的组织结构的能力。为了解决这些问题，这个优秀的研究奖将调查基于物理的模型，这些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兄弟。这项以发现为导向的研究将产生一系列知识，指导研究人员和工业用户通过再生组织工程的Biopprint设计和制造规则的开源存储库。包括开发生物制造课程在内的教育努力将影响北卡罗来纳农业和技术州立大学的学生人数不足，该大学是美国最大的历史上最大的黑人学院和大学之一，甚至更多。与维克森林再生医学研究所(WFIRM)的一项学者交流计划将培训学生在生物制造、数据分析和指导程序方面的队列。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的数据驱动结构，以定制设计基于干细胞的组织结构。具体目标包括(1)创建一个集成计算建模、实验结果和基于工业物联网的支架健康监测技术的稳健框架，(2)使用基于混合物理的数据驱动模型了解生物化学线索和纳米级拓扑的吸附构型的组合效应，以及(3)研究生物打印相互作用的材料、过程参数和微环境变量之间的关系以实现闭环控制。该团队计划采取一种融合的方法，其中将增加计算建模数据、实验研究、实时现场传感器和诊断技术，以研究生物打印过程参数。机器学习算法将被应用于合并的数据集，以揭开地形、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，以实现对生物印刷过程和材料配方的实时监测和控制。WFIRM将进行细胞增殖、组织学染色和生化分析，以验证混合模型。输入-输出关系映射将为生物制造行业4.0提供集成的过程控制、监控和智能过程数据分析。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.