Tag: chemical and biomolecular engineering

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Melding AI and RNA: Penn’s $18 Million AIRFoundry to Revolutionize RNA Research

by Ian Scheffler

The NSF AIRFoundry will accelerate RNA research using the power of AI and educate the next generation of RNA researchers. (DesignCells via Getty Images)

In a typical foundry, raw materials like steel and copper are melted down and poured into molds to assume new shapes and functions. The U.S. National Science Foundation Artificial Intelligence-driven RNA Foundry (NSF AIRFoundry), led by the University of Pennsylvania and the University of Puerto Rico and supported by an $18-million, six-year grant, will serve much the same purpose, only instead of smithing metal, the “BioFoundry” will create molecules and nanoparticles.

NSF AIRFoundry is one of five newly created BioFoundries, each of which will have a different focus. Bringing together researchers from Penn Engineering, Penn Medicine’s Institute for RNA Innovation, the University of Puerto Rico–Mayagüez (UPR-M), Drexel University, the Children’s Hospital of Philadelphia (CHOP) and InfiniFluidics, the facility, which will be physically located in West Philadelphia and at UPR-M, will focus on ribonucleic acid (RNA), the tiny molecule essential to genetic expression and protein synthesis that played a key role in the COVID-19 vaccines and saved tens of millions of lives.

The facility will use AI to design, optimize and synthesize RNA and delivery vehicles by augmenting human expertise, enabling rapid iterative experimentation, and providing predictive models and automated workflows to accelerate discovery and innovation.

“With NSF AIRFoundry, we are creating a hub for innovation in RNA technology that will empower scientists to tackle some of the world’s biggest challenges, from health care to environmental sustainability,” says Daeyeon Lee, Russell Pearce and Elizabeth Crimian Heuer Professor in Chemical and Biomolecular Engineering in Penn Engineering and NSF AIRFoundry’s director.

“Our goal is to make cutting-edge RNA research accessible to a broad scientific community beyond the health care sector, accelerating basic research and discoveries that can lead to new treatments, improved crops and more resilient ecosystems,” adds Nobel laureate Drew Weissman, Roberts Family Professor in Vaccine Research in Penn Medicine, Director of the Penn Institute for RNA Innovation and NSF AIRFoundry’s senior associate director.

The facility will catalyze new innovations in the field by leveraging artificial intelligence (AI). AI has already shown great promise in drug discovery, poring over vast amounts of data to find hidden patterns. “By integrating artificial intelligence and advanced manufacturing techniques, the NSF AIRFoundry will revolutionize how we design and produce RNA-based solutions,” says David Issadore, Professor in Bioengineering and in Electrical and Systems Engineering at  Penn Engineering and the facility’s associate director of research coordination.

Read the full story on the Penn AI website.

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Mining the Microbiome: Uncovering New Antibiotics Inside the Human Gut

by Ian Scheffler

Penn Engineering and Stanford researchers leveraged AI to discover dozens of potential new antibiotics in the human gut microbiome. (ChrisChrisW via Getty Images)

The average human gut contains roughly 100 trillion microbes, many of which are constantly competing for limited resources. “It’s such a harsh environment,” says César de la Fuente, Presidential Assistant Professor in Bioengineering and in Chemical and Biomolecular Engineering within the School of Engineering and Applied Science, in Psychiatry and Microbiology within the Perelman School of Medicine, and in Chemistry within the School of Arts & Sciences. “You have all these bacteria coexisting, but also fighting each other. Such an environment may foster innovation.”

In that conflict, de la Fuente’s lab sees potential for new antibiotics, which may one day contribute to humanity’s own defensive stockpile against drug-resistant bacteria. After all, if the bacteria in the human gut have to develop new tools in the fight against one another to survive, why not use their own weapons against them?

In a new paper in Cell, the labs of de la Fuente and Ami S. Bhatt, Professor in Medicine (Hematology) and Genetics at Stanford, surveyed the gut microbiomes of nearly 2,000 people, discovering dozens of potential new antibiotics. “We think of biology as an information source,” says de la Fuente. “Everything is just code. And if we can come up with algorithms that can sort through that code, we can dramatically accelerate antibiotic discovery.”

Read the full story in Penn Engineering Today.

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A 3D-printed Band-Aid for the Heart?

Biomaterials 3D-printed with the new method can be used inside the body and could even serve as bandages on a beating human heart. (Photo by Casey A. Cass/University of Colorado)

In the quest to develop life-like materials to replace and repair human body parts, scientists face a formidable challenge: Real tissues are often both strong and stretchable and vary in shape and size.

A CU Boulder-led team, in collaboration with researchers at the University of Pennsylvania, has taken a critical step toward cracking that code. They’ve developed a new way to 3D print material that is at once elastic enough to withstand a heart’s persistent beating, tough enough to endure the crushing load placed on joints, and easily shapeable to fit a patient’s unique defects.

Their breakthrough, described in the Aug. 2 edition of the journal Science, helps pave the way toward a new generation of biomaterials, from internal bandages that deliver drugs directly to the heart to cartilage patches and needle-free sutures.

“This is a simple 3D processing method that people could ultimately use in their own academic labs as well as in industry to improve the mechanical properties of materials for a wide variety of applications,” says first author Abhishek Dhand, a researcher in the Burdick Lab and doctoral candidate in the Department of Bioengineering at the University of Pennsylvania. “It solves a big problem for 3D printing.”

Read the full story by Lisa Marshall and Nicholas Goda on CU Boulder’s website

Jason Burdick is Bowman Endowed Professor in Chemical and Biological Engineering at the University of Colorado Boulder and Adjunct Professor in Bioengineering at Penn Engineering.

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Unlocking Nature’s Secrets: Sherry Gao Pushes the Boundaries of Genetic Engineering

by Ian Scheffler

Sherry (Xue) Gao, Presidental Penn Compact Associate Professor in Chemical and Biomolecular Engineering and in Bioengineering

Sherry (Xue) Gao, Presidential Penn Compact Associate Professor in Chemical and Biomolecular Engineering (CBE), always knew she had a future in the lab. “I grew up in China, and when I was little, maybe six or seven,” she recalls, “my teacher asked me, ‘What do you want to be when you grow up?’ I said, ‘I want to be a scientist!’”

Neither of her parents had studied beyond high school; when Gao finished her training as a chemical engineer, she became the first person in her family to graduate from college. “One of my greatest motivations is to help first-generation college students,” Gao says.

Now, as the newest faculty member in CBE, Gao is prepared to do just that: support the next generation of chemical engineers, while also conducting groundbreaking research in the development of small molecules to edit genes, pushing the boundaries of precision medicine.

The Presidential Penn Compact Professorships were created by former Penn President Amy Gutmann specifically to recruit and support faculty like Gao: transformative leaders working at the intersection of multiple fields with “a yen for collaboration,” as Gutmann told the Penn Gazette in 2021.

Gao joins Penn Engineering from Rice University, where she collected numerous accolades, including the 2024 BMES-CMBE Rising Star Award, a 2022 NSF CAREER Award, the 2022 Outstanding Young Faculty at Rice School of Engineering Award and the 2020 NIH MIRA R35 Award.

As a member of the Center for Precision Engineering for Health (CPE4H), Gao will partner with colleagues from across the School to develop technologies that bridge disciplines, all in the interest of advancing health care. “We are very excited to have Sherry as a new member of the Center,” says Daniel Hammer, Alfred G. and Meta A. Ennis Professor and inaugural Director of CPE4H. “Gene editing is an important new tool that can precisely alter cell behavior by deleting or redirecting cell pathways, as well as enhancing and suppressing gene expression. She will have significant interactions with other members of the Center, such as Mike Mitchell and myself, as well as the broader Penn community, especially with the CAR therapists.”

Read the full story in Penn Engineering Today.

Since this story was originally published in March 2024, Sherry Gao now holds a secondary appointment in Bioengineering, effective July 1, 2024.

Daniel Hammer and Michael Mitchell both hold primary appointments in Bioengineering.

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Looking to AI to Solve Antibiotic Resistance

by Nathi Magubane

Cesar de la Fuente (left), Fangping Wan (center), and Marcelo der Torossian Torres (right). Fangping holds a 3D model of a unique ATP synthase fragment, identified by their lab’s deep learning model, APEX, as having potent antibiotic properties.

“Make sure you finish your antibiotics course, even if you start feeling better’ is a medical mantra many hear but ignore,” says Cesar de la Fuente of the University of Pennsylvania.

He explains that this phrase is, however, crucial as noncompliance could hamper the efficacy of a key 20th century discovery, antibiotics. “And in recent decades, this has led to the rise of drug-resistant bacteria, a growing global health crisis causing approximately 4.95 million deaths per year and threatens to make even common infections deadly,” he says.

De la Fuente, a Presidential Assistant Professor, and a team of interdisciplinary researchers have been working on biomedical innovations tackling this looming threat. In a new study, published in Nature Biomedical Engineering, they developed an artificial intelligence tool to mine the vast and largely unexplored biological data—more than 10 million molecules of both modern and extinct organisms— to discover new candidates for antibiotics.

“With traditional methods, it takes around six years to develop new preclinical drug candidates to treat infections and the process is incredibly painstaking and expensive,” de la Fuente says. “Our deep learning approach can dramatically reduce that time, driving down costs as we identified thousands of candidates in just a few hours, and many of them have preclinical potential, as tested in our animal models, signaling a new era in antibiotic discovery.” César de la Fuente holds a 3D model of a unique ATP synthase fragment, identified by his lab’s deep learning model, APEX, as having potent antibiotic properties. This molecular structure, resurrected from ancient genetic data, represents a promising lead in the fight against antibiotic-resistant bacteria.

These latest findings build on methods de la Fuente has been working on since his arrival at Penn in 2019. The team asked a fundamental question: Can machines be used to accelerate antibiotic discovery by mining the world’s biological information? He explains that this idea is based on the notion that biology, at its most basic level, is an information source, which could theoretically be explored with AI to find new useful molecules.

Read the full story in Penn Today.

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Largest-Ever Antibiotic Discovery Effort Uses AI to Uncover Potential Cures in Microbial Dark Matter

by Eric Horvath

Credit: Georgina Joyce

Almost a century ago, the discovery of antibiotics like penicillin revolutionized medicine by harnessing the natural bacteria-killing abilities of microbes. Today, a new study co-led by researchers at the Perelman School of Medicine at the University of Pennsylvania suggests that natural-product antibiotic discovery is about to accelerate into a new era, powered by artificial intelligence (AI).

The study, published in Cell, the researchers used a form of AI called machine learning to search for antibiotics in a vast dataset containing the recorded genomes of tens of thousands of bacteria and other primitive organisms. This unprecedented effort yielded nearly one million potential antibiotic compounds, with dozens showing promising activity in initial tests against disease-causing bacteria.

“AI in antibiotic discovery is now a reality and has significantly accelerated our ability to discover new candidate drugs. What once took years can now be achieved in hours using computers” said study co-senior author César de la Fuente, PhD, a Presidential Assistant Professor in Psychiatry, Microbiology, Chemistry, Chemical and Biomolecular Engineering, and Bioengineering.

Nature has always been a good place to look for new medicines, especially antibiotics. Bacteria, ubiquitous on our planet, have evolved numerous antibacterial defenses, often in the form of short proteins (“peptides”) that can disrupt bacterial cell membranes and other critical structures. While the discovery of penicillin and other natural-product-derived antibiotics revolutionized medicine, the growing threat of antibiotic resistance has underscored the urgent need for new antimicrobial compounds.

In recent years, de la Fuente and colleagues have pioneered AI-powered searches for antimicrobials. They have identified preclinical candidates in the genomes of contemporary humans, extinct Neanderthals and Denisovans, woolly mammoths, and hundreds of other organisms. One of the lab’s primary goals is to mine the world’s biological information for useful molecules, including antibiotics.

Read the full story in Penn Medicine News.

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Artificial Intelligence to Accelerate Antibiotic Discovery

Using AI for discovery of new antibiotics.

The growing threat of antimicrobial resistance demands innovative solutions in drug discovery. Scientists are turning to artificial intelligence (AI) and machine learning (ML) to accelerate the discovery and development of antimicrobial peptides (AMPs). These short strings of amino acids are promising for combating bacterial infections, yet transitioning them into clinical use has been challenging. Leveraging novel AI-driven models, researchers aim to overcome these obstacles, heralding a new era in antimicrobial therapy.

A new article in Nature Reviews Bioengineering illuminates the promises and challenges of using AI for antibiotic discovery. Cesar de la Fuente, Presidential Assistant Professor in Microbiology and Psychiatry in the Perelman School of Medicine, in Bioengineering and Chemical and Biomolecular Engineering in the School of Engineering and Applied Science, and Adjunct Assistant Professor in Chemistry in the School of Arts and Sciences, collaborated with James J. Collins, Termeer Professor of Medical Engineering and Science at MIT, to provide an introduction to this emerging field, outlining both its current limitations and its massive potential.

In the past five years, groundbreaking work in the de la Fuente Lab has dramatically accelerated the discovery of new antibiotics, reducing the timeline from years to mere hours. AI-driven approaches employed in his laboratory have already yielded numerous preclinical candidates, showcasing the transformative potential of AI in antimicrobial research and offering new potential solutions against currently untreatable infections.

Recent advancements in AI and ML are revolutionizing drug discovery by enabling the precise prediction of biomolecular properties and structures. By training ML models on high-quality datasets, researchers can accurately forecast the efficacy, toxicity and other crucial attributes of novel peptides. This predictive power expedites the screening process, identifying promising candidates for further evaluation in a fraction of the time required by conventional methods.

Traditional approaches to AMP development have encountered hurdles such as toxicity and poor stability. AI models help overcome these challenges by designing peptides with enhanced properties, improving stability, efficacy and safety profiles, and fast-tracking the peptides’ clinical application.

While AI-driven drug discovery has made significant strides, challenges remain. The availability of high-quality data is a critical bottleneck, necessitating collaborative efforts to curate comprehensive datasets to train ML models. Furthermore, ensuring the interpretability and transparency of AI-generated results is essential for fostering trust and wider adoption in clinical settings. However, the future is promising, with AI set to revolutionize antimicrobial therapy development and address drug resistance.

Integrating AI and ML into antimicrobial peptide development marks a paradigm shift in drug discovery. By harnessing these cutting-edge technologies, researchers can address longstanding challenges and accelerate the discovery of novel antimicrobial therapies. Continuous innovation in AI-driven approaches is likely to spearhead a new era of precision medicine, augmenting our arsenal against infectious diseases.

Read “Machine learning for antimicrobial peptide identification and design” in Nature Reviews Bioengineering.

The de la Fuente Lab uses use the power of machines to accelerate discoveries in biology and medicine. The lab’s current projects include using AI for antibiotic discovery, molecular de-extinction, reprogramming venom-derived peptides to discover new antibiotics, and developing low-cost diagnostics for bacterial and viral infections. Read more posts featuring de la Fuente’s work in the BE Blog.

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Episode 4 of Innovation & Impact: Exploring AI in Engineering

by Melissa Pappas

Susan Davidson, Cesar de la Fuente, Surbhi Goel and Chris Callison-Burch speak on AI in Engineering in episode 4 of the Innovation & Impact podcast.

With AI technologies finding their way into every industry, important questions must be considered by the research community: How can deep learning help identify new drugs? How can large language models disseminate information? Where and how are researchers using AI in their own work? And, how are humans anticipating and defending against potential harmful consequences of this powerful technology?

In this episode of Innovation & Impact, host Susan Davidson, Weiss Professor in Computer and Information Science (CIS), speaks with three Penn Engineering experts about leveraging AI to advance scientific discovery and methods to protect its users. Panelists include:

Chris Callison-Burch, Associate Professor in CIS, who researches the applications of large language models and AI tools in current and future real-world problems with a keen eye towards safety and ethical use of AI;  

Surbhi Goel, Magerman Term Assistant Professor in CIS, who works at the intersection of theoretical computer science and machine learning. Her focus on developing theoretical foundations for modern machine learning paradigms expands the possibilities of deep learning; and

Cesar de la Fuente, Presidential Assistant Professor in Bioengineering, Psychiatry and Microbiology with a secondary appointment in Chemical and Biomolecular Engineering, who leads research on technology in the medical field, using computers to find antibiotics in extinct organisms and identify pre-clinical candidates to advance drug discovery. 

Each episode of Penn Engineering’s Innovation & Impact podcast shares insight from leading experts at Penn and Penn Engineering on science, technology and medicine. 

Subscribe to the Innovation & Impact podcast on Apple Music, Spotify or your favorite listening platforms or find all the episodes on our Penn Engineering YouTube channel.

This story originally appeared in Penn Engineering Today.

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The Heart and Soul of Innovation: Noor Momin Harnesses the Immune System to Treat Heart Disease

by Ian Scheffler

Noor Momin, Stephenson Foundation Term Assistant Professor of Innovation

While growing up, Noor Momin, who joined the Department of Bioengineering in January as the Stephenson Foundation Term Assistant Professor of Innovation, imagined becoming a physician. Becoming a doctor seemed like a tangible way for someone interested in science to make a difference. Not until college did she realize the impact she could have as a bioengineer instead.

“I was taping microscope slides together,” Momin recalls of her initial experience as an undergraduate researcher at the University of Texas at Austin. “I didn’t even know what a Ph.D. was.”

It wasn’t until co-authoring her first paper, which explores how lipids, the water-repelling molecules that make up cell membranes (and also fats and oils), can switch between more fluid and less fluid arrangements, that Momin understood the degree to which bioengineering can influence medicine. “Someone could potentially use that paper for drug design,” Momin says.

Today, Momin’s research applies her molecular expertise to heart disease, which despite numerous advances in treatment — from coronary artery bypass surgery to cholesterol-lowering statins — remains the primary cause of mortality worldwide.

As Momin sees it, the conventional wisdom of treating the heart like a mechanical pump, whose pipes can be replaced or whose throughput can be treated to prevent clogging in the first place, overshadows the immune system’s critical role in the development of heart disease.

Read the full story in Penn Engineering Today.

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Penn Scientists Reflect on One Year of ChatGPT

by Erica Moser

René Vidal, at the podium, introduces the event “ChatGPT turns one: How is generative AI reshaping science?” Bhuvnesh Jain, left at the table, moderated the discussion with Sudeep Bhatia, Konrad Kording, Andrew Zahrt, and Nick Pangakis.

As a neuroscientist surveying the landscape of generative AI—artificial intelligence capable of generating text, images, or other media—Konrad Kording cites two potential directions forward: One is the “weird future” of political use and manipulation, and the other is the “power tool direction,” where people use ChatGPT to get information as they would use a drill to build furniture.

“I’m not sure which of those two directions we’re going but I think a lot of the AI people are working to move us into the power tool direction,” says Kording, a Penn Integrates Knowledge (PIK) University professor with appointments in the Perelman School of Medicine and School of Engineering and Applied Science. Reflecting on how generative AI is shifting the paradigm of science as a discipline, Kording said he thinks “it will push science as a whole into a much more collaborative direction,” though he has concerns about ChatGPT’s blind spots.

Kording joined three University of Pennsylvania researchers from the chemistry, political science, and psychology departments sharing their perspectives in the recent panel “ChatGPT turns one: How is generative AI reshaping science?” PIK Professor René Vidal opened the event, which was hosted by the School of Arts & Sciences’ Data Driven Discovery Initiative (DDDI), and Bhuvnesh Jain, physics and astronomy professor and co-faculty director of DDDI, moderated the discussion.

“Generative AI is moving so rapidly that even if it’s a snapshot, it will be very interesting for all of us to get that snapshot from these wonderful experts,” Jain said. OpenAI launched ChatGPT, a large language model (LLM)-based chatbot, on Nov. 30, 2022, and it rapidly ascended to ubiquity in news reports, faculty discussions, and research papers. Colin Twomey, interim executive director of DDDI, told Penn Today that it’s an open question as to how it will change the landscape of scientific research, and the` idea of the event was to solicit colleagues’ opinions on interesting directions in their fields.

Read the full story in Penn Today.

Konrad Paul Kording is Nathan Francis Mossell University Professor in Bioengineering and Computer and Information Science in Penn Engineering and in Neuroscience in the Perelman School of Medicine.