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失调的器官方面具有很大的前景。然而，干细胞工程中组织支架的制备依赖于生化信号、细胞排列和相关工艺参数等多种因素的相互作用。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控，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生长行为的能力，无法形成用于器官替代的有活力的组织结构。为了解决这些问题，这项杰出研究奖将研究基于物理的模型，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。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生物打印设计和制造规则的开源存储库。包括生物制造课程开发在内的教育努力将影响北卡罗来纳农业和技术州立大学（North Carolina Agricultural and Technical State University）的代表性不足的学生，该大学是美国历史上最大的黑人学院和大学之一。与维克森林再生医学研究所（WFIRM）的学者交流项目将在生物制造、数据分析和指导程序方面培训学生群体。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，以定制工程干细胞为基础的组织结构。具体目标包括(1)创建一个强大的框架，将计算建模、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集成在一起；(2)使用基于混合物理的数据驱动模型理解生化线索的吸附构型和纳米级拓扑结构的组合效应；(3)研究相互作用材料之间的关系。闭环控制的生物打印工艺参数和微环境变量。该团队计划采用一种融合的方法，其中计算建模数据、实验研究、实时原位传感器和诊断将被增强，以研究生物打印过程参数。机器学习算法将应用于整合的数据集，以揭示地形、机械刺激和生物化学线索之间潜在的隐藏模式，这些模式决定了细胞的命运和功能。将开发混合预测模型，以便实时监测和控制生物打印过程和材料配方。细胞增殖、组织学染色和生化分析将在WFIRM进行，以验证杂交模型。投入产出关系映射将实现集成过程控制、监控和智能过程数据分析，实现生物制造工业4.0。该奖项反映了美国国家科学基金会的法定使命，并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，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.