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

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.