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

卓越的研究：基于融合物理的数据驱动的再生组织生物打印，用于未来的生物制造

基本信息

批准号：
2100739
负责人：
Salil Desai
金额：
$ 52.87万
依托单位：
North Carolina Agricultural & Technical State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2100739&HistoricalAwards=false
关键词：
Excellence Research Convergent Physics based

项目摘要

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.

再生组织工程有望用干细胞替代患病和功能失调的器官，然而，干细胞工程中组织支架的制造取决于多种因素的相互作用，例如生化信号、细胞排列和相关的关键过程参数。生物制造研究进展的障碍是缺乏正式的指导原则和实时过程监控，此外还需要进行基于干细胞的生物打印实验，这一关键障碍限制了控制生长行为的能力。为了解决这些问题，这项杰出研究奖将利用多种细胞类型来形成可行的组织结构，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双胞胎。驱动的研究将产生大量知识，通过再生组织工程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，包括开发生物制造课程在内的教育工作将影响北卡罗来纳州代表性不足的学生。州立农业技术大学是美国最大的历史悠久的黑人学院和大学之一，其与维克森林再生医学研究所 (WFIRM) 的学者交流计划将为学生群体提供生物制造、数据分析和指导程序方面的培训。该项目的总体目标是在混合生物打印中建立基于物理的数据驱动结构，以定制工程基于干细胞的组织结构，具体目标包括（1）创建一个集成计算模型、实验结果和工业互联网的强大框架。基于事物的用于生物打印的支架健康监测技术，（2）使用基于混合物理的数据驱动模型了解生化线索和纳米级拓扑的吸附配置的组合效应，以及（3）研究生物打印的相互作用材料、工艺参数和微环境变量之间的关系该团队计划采用收敛方法、计算建模数据、实验研究、实时原位传感器和诊断来研究生物打印过程参数。将开发混合预测模型，以揭示地形、机械刺激和生化线索之间的潜在隐藏模式，以实现对生物打印过程和材料配方的实时监测和控制。 WFIRM 将进行增殖、组织学染色和生化分析，以验证混合模型，从而实现生物制造行业的集成过程控制、监控和智能过程数据分析。 4.0. 该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。