Inside NSF AIRFoundry, Senator McCormick gets a look at the future of RNA discovery
A visit to Penn’s Artificial Intelligence-driven RNA BioFoundry gave U.S. Senator Dave McCormick a firsthand look at how federal investment in science translates into shared research infrastructure, student training, and regional economic development.
Senator Dave McCormick (second from left) and Andrew Hanna (left) observing Hanna’s robotic system for rapidly formulating large numbers of lipid nanoparticles.
(Image: Sylvia Zhang)
“Each droplet is a different candidate for drug delivery,” said Hanna, pointing to a rectangular plastic plate dotted with tiny wells. “Conventionally, creating a single one of these would take a scientist 20 to 30 minutes.”
A robot that Hanna developed whirred, carrying the plate back and forth as it collected formulations mixed by tubes pushing fluid through a tiny chip, at speeds an order of magnitude faster than researchers could achieve by hand.
“So you’re creating a huge data set,” said McCormick. “Where does the data go next?”
The exchange set the tone for McCormick’s first visit to the NSF AIRFoundry on May 15, where he met with Daeyeon Lee, Russell Pearce, and Elizabeth Crimian Heuer Professor in Chemical and Biomolecular Engineering; the facility’s director, George J. Pappas, UPS Foundation Professor of Transportation in Electrical and Systems Engineering and Penn Engineering’s associate dean for research; and David F. Meaney, Solomon R. Pollack Professor in Bioengineering and Penn’s vice provost for research.
That acceleration is the premise behind the NSF AIRFoundry, an $18-million effort led by Penn Engineering to combine AI, automation, and RNA science.
Opened earlier this spring, the facility brings together Penn Engineering, Penn Medicine’s Institute for RNA Innovation, the University of Puerto Rico–Mayagüez, Drexel University, Children’s Hospital of Philadelphia, and startup InfiniFluidics to make RNA research faster, more scalable, and more accessible.
During the tour, McCormick saw how that mission depends on more than any single piece of equipment. Researchers showcased systems for creating lipid nanoparticles, producing RNA and automating experiments that would otherwise require painstaking manual work. Each piece of equipment feeds into a larger cycle: experiments generate data, data trains AI models, and AI models help researchers decide which experiments to try next.
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