A University of Maryland-led project received a $17.3 million National Science Foundation award to create an AI-powered and remotely accessible laboratory for biomanufacturing research. The four-year initiative, called the Collaborative for the Realization of Autonomous Biomanufacturing Lab, or CRAB Lab, will combine artificial intelligence, robotics, automation, and real-time biological measurements.
The laboratory will be housed at the Institute for Bioscience and Biotechnology Research in Rockville, Maryland, while allowing researchers elsewhere in the U.S. to remotely access its capabilities.
The project is led by William Bentley, Robert E. Fischell Distinguished Professor and Director of the Robert E. Fischell Institute for Biomedical Devices.
University of Maryland Department of Computer Science Assistant Professor Alan Zaoxing Liu is a co-principal investigator alongside Gregory Payne, Martha Wang, and Hussain Dahodwala.
Ang Li of UMD’s Department of Electrical and Computer Engineering is co-leading the project’s AI efforts.
The CRAB Lab is one of 20 Programmable Cloud Laboratory Node sites supported by an NSF initiative focused on developing AI-enabled infrastructure for automated science and engineering.
Researchers will be able to program workflows that design, conduct, and analyze biomanufacturing experiments remotely.
Automated laboratory instruments and AI systems will collect information during experiments and help determine what actions should be taken next.
Liu’s research group is responsible for developing the data and AI infrastructure supporting the laboratory.
This work includes managing large volumes of measurement data, coordinating experiments from beginning to end, and developing models capable of identifying what Liu describes as biological vital signs.
Those signals could help determine whether a biomanufacturing process is operating as intended or beginning to deviate from expected conditions.
The project will also use bioelectronic measurement technologies developed through University of Maryland partnerships with the National Institute of Standards and Technology and the U.S. Food and Drug Administration.
The technologies are designed to generate high-volume and high-speed biological measurements that can be used to train AI models.
Those models could identify combinations of measurements linked to product quality and other characteristics while experiments are still underway.
The CRAB Lab is expected to initially serve 30 biopharmaceutical companies through the Advanced Mammalian Biomanufacturing Innovation Center.
Remote access is also intended to make sophisticated automated laboratory infrastructure available to researchers at institutions that may not have comparable equipment of their own.
The initiative could ultimately help researchers accelerate experimentation related to medicines including vaccines and cancer therapies while improving reproducibility and reducing the time required to move from an idea to experimental results.
KEY QUOTES:
“This is a tremendously exciting moment. I’ve been building the systems that make AI fast and reliable, and this award is a chance to apply that work to something as tangible as making medicines.”
Alan Zaoxing Liu, Assistant Professor in the University of Maryland Department of Computer Science
“Almost everything in modern medicine, from vaccines to cancer therapies, depends on biomanufacturing, but the experiments behind it are slow, expensive and often hard to reproduce. We’re building AI agents that can design an experiment, hand it to the robots, monitor the data in real time and decide what to try next, so a scientist can log in from anywhere and have results the next morning.”
Alan Zaoxing Liu, Assistant Professor in the University of Maryland Department of Computer Science
“That shortens the path from idea to result by months. Just as important, a graduate student at a small school will have the same AI-driven lab at their fingertips as a large company, and that kind of access changes who gets to do the science.”
Alan Zaoxing Liu, Assistant Professor in the University of Maryland Department of Computer Science
