Tag: Perelman

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Who, What, Why: Lasya Sreepada on Decoding Alzheimer’s Disease

by Nathi Magubane

Lasya Sreepada, Ph.D. student in Bioengineering

Lasya Sreepada has always been fascinated by the brain and the underlying biology that shapes how people develop and age. “My curiosity traces back to observing differences between myself and my sister,” says Sreepada, a Ph.D. candidate in Bioengineering whose research unites efforts across Penn Medicine and Penn Engineering. “We grew up in the same environment but had remarkably different personalities, which led me to question what drove these differences and which brought me to the brain.”

Her academic journey began by applying medical imaging to understand how brain injuries sustained by professional athletes or military veterans impact their brain structure and chemistry over time. She became curious about how neurotrauma impacts aging and degeneration in the long term. Now, she leverages large, multimodal datasets to investigate neurodegenerative disease, with a particular focus on Alzheimer’s.

Read the full story in Penn Today.

Lasya Sreepada is a Bioengineering Ph.D. student at the Bioinformatics in Neurodegenerative Disease (BiND) Lab at Penn, advised by Corey McMillan and Dave Wolk, both Associate Professors in Neurology and members of the Bioengineering Graduate Group.

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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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How “Invitations” from Penn Medicine Restored Mammogram Completion Rates

by Frank Otto

The first few waves of COVID-19 slowed life across the United States, affecting everything from attending school to eating out for dinner and going on vacation. Segments of health care were also affected: Services that were not considered immediately crucial to fighting the virus were slowed or stopped during the pandemic’s first wave.  

But once Penn Medicine invited patients back to resume normal health care—including preventive care, like screenings for disease—there was some lag in numbers. 

“As we opened up to routine outpatient care, screening rates for situations when patients didn’t have symptoms were not returning back to normal,” said Mitchell Schnall, MD, PhD, FACR, a professor of Radiology, now the senior vice president for Data and Technology Solutions at Penn Medicine, and then the head of a team focused on the “resurgence” efforts to ease patients back into outpatient care. “Although a short delay in health screening is likely not going to cause long-term health problems, we were concerned whether screening rates would stay lower and lead to a long-term impact.”  

Read the full story in Penn Medicine News.

Mitchell Schnall is a member of the Penn Bioengineering Graduate Group.

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Different Brain Structures in Females Lead to More Severe Cognitive Deficits After Concussion Than Males

by Kelsey Geesler

Top: Axons in female and male subject brains Bottom: damaged axons in male and female brains after injury (Credit: Penn Medicine)

Important brain structures that are key for signaling in the brain are narrower and less dense in females, and more likely to be damaged by brain injuries, such as concussion. Long-term cognitive deficits occur when the signals between brain structures weaken due to the injury. The structural differences in male and female brains might explain why females are more prone to concussions and experience longer recovery from the injury than their male counterparts, according to a preclinical study led by the Perelman School of Medicine at the University of Pennsylvania, published this week in Acta Neuropathologica.

Each year, approximately 50 million individuals worldwide suffer a concussion, also referred to as mild traumatic brain injury (TBI). However, there is nothing “mild” about this condition for the more than 15 percent of individuals who suffer persisting cognitive dysfunction, which includes difficulty concentrating, learning and remembering new information, and making decisions.

Although males make up the majority of emergency department visits for concussion, this has been primarily attributed to their greater exposure to activities with a risk of head impacts compared to females. In contrast, it has recently been observed that female athletes have a higher rate of concussion and appear to have worse outcomes than their male counterparts participating in the same sport.

“Clinicians have observed for a long time that females suffer from concussion at higher rates than males in the same sports, and that they take longer to recover cognitive function, but couldn’t explain the underlying mechanisms of this phenomenon,” said senior author Douglas Smith, MD, a professor of Neurosurgery and director of Penn’s Center for Brain Injury and Repair. “The variances in brain structures of females and males not only illuminate why this disparity exists, but also exposes biomarkers, such as axon protein fragments, that can be measured in the blood to determine injury severity, monitor recovery, and eventually help identify and develop treatments that help patients repair these damaged structures and restore cognitive function.”

Read the full story in Penn Medicine News.

Douglas H. Smith is a member of the Penn Bioengineering Graduate Group.

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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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2024 Solomon R. Pollack Awards for Excellence in Graduate Bioengineering Research

The Solomon R. Pollack Award for Excellence in Graduate Bioengineering Research is given annually to the most deserving Bioengineering graduate students who have successfully completed research that is original and recognized as being at the forefront of their field. This year, the Department of Bioengineering at the University of Pennsylvania is proud to recognize the work of four outstanding graduates in Bioengineering: William Benman, Alex Chan, Rohan Palanki and Sunghee Estelle Park. 

Read more about the 2024 Solomon R. Pollack awardees and their doctoral research below.

William Benman

Dissertation: “Remote control of cell function using heat and light as inputs”

Will conducts research in the lab of Lukasz Bugaj, Assistant Professor in Bioengineering, focusing on reprogramming cells so that their basic functions can be regulated artificially using heat and/or light as inputs. The goal of this work ranges from clinical applications, such as localized activation of cell therapies within patients via application of heat, to biological manufacturing, using light to activate production of valuable biologics during key phases of a cell’s life cycle. He earned his undergraduate degree in biomedical engineering from Boston University, where he graduated summa cum laude. At BU, he worked in the lab of Wilson Wong, where he was introduced to synthetic biology. During that time, he worked to develop a genetic logic framework that would allow cells to integrate chemical signals, such that each combination of signals would lead to a different, user-defined combination of genes being expressed. Outside of the lab, Benman enjoys baking and sharing his treats with lab members. He mentored the 2021 Penn iGEM team, which recently published their work in Communications Biology. After graduation, he will start a postdoctoral fellowship in Mikhail Shapiro’s lab at Caltech, where he plans to explore electrogenetics, focusing on how to co-opt electrically active cell types to transmit biochemical information out of the body. He is interested in researching ways to get cells to talk to electronic devices and vice/versa for two way communication, especially in the context of patient monitoring and precision therapies. 

“Will’s Ph.D. work broke new ground across several fields, discovering how certain proteins sense temperature, engineering those proteins for on-demand control of human cells, and building devices to allow us to communicate with cells with precision,” says Bugaj. “He has managed these accomplishments while elevating those around him through mentorship, including of graduate students, scores of undergraduates, and even grade-school students in the community. I am immensely proud of Will and what he has accomplished and am gratified by the recognition from the Sol Pollack award.”

Alex Chan

Dissertation: “Engineering small protein based inhibitors and biodegraders for cytosolic delivery and targeting of the undruggable proteome”

Alex conducts research in the lab of Andrew Tsourkas, Professor in Bioengineering and Co-Director, Center for Targeted Therapeutics and Translational Nanomedicine (CT3N). His research focuses on developing novel cancer therapeutics by engineering protein scaffolds so that they can be efficiently delivered into cells using lipid nanocarriers. These proteins can either behave as oncogenic inhibitors or be imbued with E3 domains for targeted protein degradation. He graduated from The Pennsylvania State University in 2018 with a B.S in Biomedical Engineering. There, he conducted undergraduate research on photo-activated silver nanoparticle miRNA delivery systems and wrote his senior honors thesis on this topic. At Penn, Alex served as a wellness co-chair within GABE (the Graduate Association of Bioengineers) and was awarded a graduate research fellowship program award by the National Science Foundation (NSF GRFP). In his spare time, Chan loves to cook and explore the local restaurant scene (and he thinks Philly is one of the most vibrant food meccas in America). Post-graduation, he plans to explore Asia before starting as a Senior Scientist in the biopharma industry. He intends to continue working on novel biologics-based medicines for unmet medical needs.

“I cannot think of anyone more deserving of this award than Alex,” says Tsourkas. “He not only demonstrates all of the traits that we love to see in our most successful Ph.D. students — intelligence, hard work ethic, and perseverance — but Alex has also exhibited a level of scientific independence that is beyond his years. I cannot wait to see what Alex achieves in the future.”

Rohan Palanki

Dissertation: “Ionizable lipid nanoparticles for in utero gene editing of congenital disease”

Rohan completed his B.S. in Bioengineering from Rice University in 2019 and subsequently matriculated into the Medical Scientist Training Program (M.D./Ph.D.) at the University of Pennsylvania. He conducted his doctoral research as an NIH Ruth L. Kirschstein Pre-Doctoral Fellow in the laboratories of Michael J. Mitchell, Associate Professor in Bioengineering, and William H. Peranteau, Associate Professor of Surgery at CHOP. After defending his thesis in 2024, he returned to medical school to complete his clinical training. He plans to pursue a career as a physician-engineer, conducting translational research at the intersection of biomaterials and genomic medicine. Outside of the lab, Palanki enjoys exploring new restaurants in Philadelphia and cheering on Philadelphia sports teams.

“Rohan pioneered new lipid nanoparticle gene editing technology in the lab that can treat deadly childhood diseases before a child is ever born,” says Mitchell. “Rohan is extremely deserving of this award, and I cannot wait to see what he accomplishes as a physician scientist developing new biomaterial and drug delivery technologies for pediatric applications.”

Sunghee Estelle Park

Dissertation: “Engineering stem cells and organoids on a chip for the study of human health and disease”

Sunghee Estelle Park earned her BMSE and MSME from Korea University and her Ph.D. in Bioengineering at the University of Pennsylvania, graduating in July 2023. She conducted doctoral research in the BIOLines Lab of Dan Huh, Associate Professor in Bioengineering. Her Ph.D. research combined principles in developmental biology, stem cell biology, organoids, and organ-on-a-chip technology to develop innovative in vitro models that can faithfully replicate the pathophysiology of various human diseases. Her doctoral dissertation presented engineering approaches to create stem cell derived three-dimensional (3D) miniature models of human organs on a chip that mimic the physiology and function of living human tissues. Park was appointed Assistant Professor of Biomedical Engineering in the Weldon School of Biomedical Engineering at Purdue University beginning January 2024. Her research lab focuses on using engineered tissues and organoid models to understand how biomechanical and biochemical cues direct stem cell differentiation, maturation, and function during development and disease progression, with a particular emphasis on the lung and intestine. 

“With her deep knowledge, extensive experience, and leadership, Estelle led the major undertaking of harnessing the power of microengineering technologies to create more in vivo-like culture environments in my group, and she played a central role in demonstrating the proof-of-concept of generating organoid-based in vitro models that enable new capabilities for studying complex human diseases and developing new therapeutics,” says Huh. “I am extremely proud of her tremendous accomplishments as a trailblazer in this emerging area and have every confidence that her work as an independent investigator will continue to make great contributions to advancing the field.”

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Karen Xu Honored with P.E.O. Scholar Award

Karen Xu, a 2024 doctoral graduate in Bioengineering at the University of Pennsylvania, is one of 100 doctoral students in the U. S. and Canada selected to receive a $25,000 Scholar Award from the P.E.O. Sisterhood. 

The P.E.O. Scholar Awards were established in 1991 to provide substantial merit-based awards for women of the United States and Canada who are pursuing a doctoral-level degree at an accredited college or university.  Scholar Awards recipients are a select group of women chosen for their high level of academic achievement and their potential for having a positive impact on society.

The P.E.O., founded January 21, 1869, at Iowa Wesleyan College, Mount Pleasant, Iowa, is a philanthropic educational organization dedicated to supporting higher education for women.  There are approximately 6,000 local chapters in the United States and Canada with nearly a quarter of a million active members.

Xu graduated summa cum laude with a B.S.E. in Biomedical Engineering from Duke University in 2018, after which she joined the M.D.-Ph.D. program at the University of Pennsylvania. She completed her Ph.D. in Bioengineering in spring 2024, funded by an NIH NRSA F30 fellowship, and is set to earn her M.D. in 2026. Under the mentorship of Jason Burdick, Bowman Endowed Professor in Chemical and Biological Engineering at the University of Colorado Boulder and Adjunct Professor in Bioengineering in Penn Engineering, and Robert Mauck, Mary Black Ralston Professor in Orthopaedic Surgery in the Perelman School of Medicine and in Bioengineering in Penn Engineering, her doctoral research has focused on engineering disease models to facilitate therapeutic discoveries. Her doctoral thesis involved the fabrication of hydrogels as tissue mimics to investigate how extracellular environments affect cell behaviors, thereby informing repair of dense connective tissues.

Beyond her research, Xu has taught with the Educational Pipeline Program at the Netter Center and the Perelman School of Medicine, where she hopes to inspire and support the next generation of healthcare workers and scientists.

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Arjun Raj Explores Whether Cells Can Learn in 2024 Heilmeier Lecture

Arjun Raj (center) accepts the Heilmeier Award, with Bioengineering Department Chair Ravi Radhakrishnan (left) and Dean Vijay Kumar (right).

Arjun Raj, Professor in Bioengineering at Penn Engineering and in Genetics at the Perelman School of Medicine, has been honored with the 2023-24 George H. Heilmeier Faculty Award for Excellence for “pioneering the development and application of single-cell, cancer-fighting technologies.”

The George H. Heilmeier Faculty Award for Excellence in Research was “established by Penn Engineering for the purpose of recognizing excellence in scholarly activities of the faculty. Named in honor of George H. Heilmeier, it recognizes his extraordinary research career, his leadership in technical innovation and public service, and his loyal and steadfast support of Penn Engineering.”

Dr. Raj delivered his lecture, entitled “Can a Cell Learn?” on April 8, 2024. In this talk, Raj explores whether it is possible for cells to adapt to their environment by learning, thereby overcoming their genetic destiny.

Learn more about, this award, Dr. Raj and his research here. View the lecture recording below.

The Raj Lab for Systems Biology is interested in building a quantitative understanding of cellular function. They develop new tools for quantifying biological processes based on imaging and sequencing and then use those techniques to help us answer questions in molecular and cellular biology. Read more stories featuring Raj 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.