Neil Sheppard, Adjunct Associate Professor of Pathology and Laboratory Medicine in the Perelman School of Medicine, and David Mai, a Bioengineering graduate student in the School of Engineering and Applied Science, explained the findings of their recent study, which offered a potential strategy to improve T cell therapy in solid tumors, to the European biotech news website Labiotech.
Folding@home is led by Gregory Bowman, a Penn Integrates Knowledge Professor who has appointments in the Departments of Biochemistry and Biophysics in the Perelman School of Medicine and the Department of Bioengineering in the School of Engineering and Applied Science. (Image: Courtesy of Penn Medicine News)
Two heads are better than one. The ethos behind the scientific research project Folding@home is that same idea, multiplied: 50,000 computers are better than one.
Folding@home is a distributed computing project which is used to simulate protein folding, or how protein molecules assemble themselves into 3-D shapes. Research into protein folding allows scientists to better understand how these molecules function or malfunction inside the human body. Often, mutations in proteins influence the progression of many diseases like Alzheimer’s disease, cancer, and even COVID-19.
Penn is home to both the computer brains and human minds behind the Folding@home project which, with its network, forms the largest supercomputer in the world. All of that computing power continually works together to answer scientific questions such as what areas of specific protein implicated in Parkinson’s disease may be susceptible to medication or other treatment.
Using the network hub at Penn, Bowman and his team assign experiments to each individual computer which communicates with other computers and feeds info back to Philly. To date, the network is comprised of more than 50,000 computers spread across the world.
“What we do is like drawing a map,” said Bowman, explaining how the networked computers work together in a type of system that experts call Markov state models. “Each computer is like a driver visiting different places and reporting back info on those locations so we can get a sense of the landscape.”
Individuals can participate by signing up and then installing software to their standard personal desktop or laptop. Participants can direct the software to run in the background and limit it to a certain percentage of processing power or have the software run only when the computer is idle.
When the software is at work, it’s conducting unique experiments designed and assigned by Bowman and his team back at Penn. Users can play scientist and watch the results of simulations and monitor the data in real time, or they can simply let their computer do the work while they go about their lives.
“Through their collaborative research, they are aiming to develop next-generation treatments for dental caries (tooth-decay) using lipid nanoparticles, the same delivery vehicles employed in the mRNA COVID-19 vaccine technology.
‘This project shows the type of innovative ideas and collaborations that we are kickstarting through the IDEA prize,’ says Dr. Michel Koo, co-director of the CiPD and Professor at Penn Dental Medicine. ‘This is a great example of synergistic interaction at the interface of engineering and oral health’ adds Dr. Kate Stebe, co-director of the CiPD and Professor at Penn Engineering.”
Brian Litt, Professor in Bioengineering in Penn Engineering and in Neurology in the Perelman School of Medicine, spoke to Neurology Today about the advances in technology for detecting and forecasting seizures.
The Litt Lab for Translational Neuroengineering translates neuroengineering research directly into patient care, focusing on epilepsy and a variety of research initiatives and clinical applications.
“Dr. Litt’s group is working with one of a number of startups developing ‘dry’ electrode headsets for home EEG monitoring. ‘They are still experimental, but they’re getting better, and I’m really optimistic about the possibilities there.'”
Brain development does not occur uniformly across the brain, but follows a newly identified developmental sequence, according to a new Penn Medicine study. Brain regions that support cognitive, social, and emotional functions appear to remain malleable—or capable of changing, adapting, and remodeling—longer than other brain regions, rendering youth sensitive to socioeconomic environments through adolescence. The findings are published in Nature Neuroscience.
Researchers charted how developmental processes unfold across the human brain from the ages of 8 to 23 years old through magnetic resonance imaging (MRI). The findings indicate a new approach to understanding the order in which individual brain regions show reductions in plasticity during development.
Brain plasticity refers to the capacity for neural circuits—connections and pathways in the brain for thought, emotion, and movement—to change or reorganize in response to internal biological signals or the external environment. While it is generally understood that children have higher brain plasticity than adults, this study provides new insights into where and when reductions in plasticity occur in the brain throughout childhood and adolescence.
The findings reveal that reductions in brain plasticity occur earliest in “sensory-motor” regions, such as visual and auditory regions, and occur later in “associative” regions, such as those involved in higher-order thinking (problem solving and social learning). As a result, brain regions that support executive, social, and emotional functions appear to be particularly malleable and responsive to the environment during early adolescence, as plasticity occurs later in development.
“Studying brain development in the living human brain is challenging. A lot of neuroscientists’ understanding about brain plasticity during development actually comes from studies conducted with rodents. But rodent brains do not have many of what we refer to as the association regions of the human brain, so we know less about how these important areas develop,” says corresponding author Theodore D. Satterthwaite,the McLure Associate Professor of Psychiatry in the Perelman School of Medicine, and director of the Penn Lifespan Informatics and Neuroimaging Center (PennLINC).
Members of Team Sonura: Tifara Boyce, Gabriela Cano, Gabriella Daltoso, Sophie Ishiwari, & Caroline Magro (credit: Penn BE Labs)
Technical.ly Philly journalist Sarah Huffman recently paid another visit to Penn Bioengineering’s George H. Stephenson Foundation Educational Laboratory & Bio-MakerSpace, this time for the 2023 Senior Design Expo. Following the annual Senior Design presentations held in the Singh Center for Nanotechnology, in which graduating fourth-year undergraduates in Bioengineering presented their final capstone projects, the Expo offered an opportunity for the teams to do live demonstrations (or demos) for the department’s internal competition judges and the wider BE community.
“In the course of the day, students presented the challenge they were aiming to solve and the technical details of their solution. After, demonstrations sought to find if the devices really worked.
‘[It’s] looking at the device as a whole, because quite frankly, you can say whatever you want at a presentation, does it really work,’ said [BE Labs Director Sevile] Mannickarottu. ‘You can make it look pretty, “but does it work?” is the big question.'”
Joshua C. Doloff, Assistant Professor of Biomedical Engineering and Materials Science & Engineering at Johns Hopkins University, featured in The Jewish News Syndicate for his work on “Hope,” a new technology which offers pain- and injection-free treatment to people with Type 1 or “juvenile” diabetes. Doloff is an alumnus of Penn Bioengineering, Class of 2004:
“Doloff received his bachelor’s degree from the University of Pennsylvania and his graduate degrees from Boston University. In addition to his post in Johns Hopkins’ Department of Biomedical Engineering, he is a member of the Translational Tissue Engineering Center at Johns Hopkins University School of Medicine. His lab is interested in systems biology with an emphasis on engineering improved therapies in the fields of cancer, autoimmunity, transplantation medicine, including Type 1 diabetes and ophthalmology.”
Penn Bioengineering juniors work on their ECG devices in BE 3100, Bioengineering Modeling, Analysis and Design Laboratory II (aka BE MAD)
The George H. Stephenson Foundation Educational Laboratory & Bio-MakerSpace (aka the Penn BE Labs) played host last week to Sarah Huffman, a local journalist writing for Technical.ly Philly. During her visit to the lab, she chatted with third year undergraduates working on their ECG devices for monitoring breathing and heart rates, and senior design students applying all they’ve learned in their previous three years to their graduation capstone projects. She also got a chance to discuss the classes and learn about the lab’s vision to be a bio-makerspace with Sevile Mannickarottu, Director of Educational Labs for BE, and with David Issadore, Associate Professor in Bioengineering and in Electrical and Systems Engineering and professor of the third year spring lab course:
Journalist Sarah Huffman interviews BE 3100 professor David Issadore.
“’The students all come here and they hang out and they build stuff,’ said David Issadore, associate professor of bioengineering and electrical and systems engineering. ‘This junior-level course is kind of an entry point for their senior design. So next year, all these students are going to take on new projects, and then they all kind of hang around here and they build incredible stuff.’”
The profile of the BE Labs is part of Technical.ly’s 2023 Universities Month, a series focusing on the latest trends and tech in higher education.
Perelman School of Medicine (PSOM) professors and Penn Bioengineering Graduate Group members Carl June and Avery Posey are leading the charge in T cell therapy and the fight against cancer.
Avery Posey, PhDCarl June, MD
Advances in genome editing through processes such as CRISPR, and the ability to rewire cells through synthetic biology, have led to increasingly elaborate approaches for modifying and supercharging T cells for therapy. Avery Posey, Assistant Professor of Pharmacology, and Carl June, the Richard W. Vague Professor in Immunotherapy, explain how new techniques are providing tools to counter some of the limitations of current CAR T cell therapies in a recent Nature feature.
The pair were also part of a team of researchers from PSOM, the Children’s Hospital of Philadelphia (CHOP), and the Corporal Michael J. Crescenz VA Medical Center to receive an inaugural $8 million Therapy ACceleration To Intercept CAncer Lethality (TACTICAL) Award from the Prostate Cancer Foundation. Their project will develop new clinic-ready CAR T cell therapies for Metastatic Castrate-Resistant Prostate Cancer (mCRPC).
Penn Engineering’s newly established ASSET Center aims to make AI-enabled systems more “safe, explainable and trustworthy” by studying the fundamentals of the artificial neural networks that organize and interpret data to solve problems.
ASSET’s first funding collaboration is with Penn’s Perelman School of Medicine (PSOM) and the Penn Institute for Biomedical Informatics (IBI). Together, they have launched a series of seed grants that will fund research at the intersection of AI and healthcare.
Teams featuring faculty members from Penn Engineering, Penn Medicine and the Wharton School applied for these grants, to be funded annually at $100,000. A committee consisting of faculty from both Penn Engineering and PSOM evaluated 18 applications and judged the proposals based on clinical relevance, AI foundations and potential for impact.
Artificial intelligence and machine learning promise to revolutionize nearly every field, sifting through massive amounts of data to find insights that humans would miss, making faster and more accurate decisions and predictions as a result.
Applying those insights to healthcare could yield life-saving benefits. For example, AI-enabled systems could analyze medical imaging for hard-to-spot tumors, collate multiple streams of disparate patient information for faster diagnoses or more accurately predict the course of disease.
Given the stakes, however, understanding exactly how these technologies arrive at their conclusions is critical. Doctors, nurses and other healthcare providers won’t use such technologies if they don’t trust that their internal logic is sound.
“We are developing techniques that will allow AI-based decision systems to provide both quantifiable guarantees and explanations of their predictions,” says Rajeev Alur, Zisman Family Professor in Computer and Information Science and Director of the ASSET Center. “Transparency and accuracy are key.”
“Development of explainable and trustworthy AI is critical for adoption in the practice of medicine,” adds Marylyn Ritchie, Professor of Genetics and Director of the Penn Institute for Biomedical Informatics. “We are thrilled about this partnership between ASSET and IBI to fund these innovative and exciting projects.”
Seven projects were selected in the inaugural class, including projects from Dani S. Bassett, J. Peter Skirkanich Professor in the Departments of Bioengineering, Electrical and Systems Engineering, Physics & Astronomy, Neurology, and Psychiatry, and several members of the Penn Bioengineering Graduate Group: Despina Kontos, Matthew J. Wilson Professor of Research Radiology II, Department of Radiology, Penn Medicine and Lyle Ungar, Professor, Department of Computer and Information Science, Penn Engineering; Spyridon Bakas, Assistant Professor, Departments of Pathology and Laboratory Medicine and Radiology, Penn Medicine; and Walter R. Witschey, Associate Professor, Department of Radiology, Penn Medicine.
Optimizing clinical monitoring for delivery room resuscitation using novel interpretable AI
Elizabeth Foglia, Associate Professor, Department of Pediatrics, Penn Medicine and the Children’s Hospital of Philadelphia
Dani S. Bassett, J. Peter Skirkanich Professor, Departments of Bioengineering and Electrical and Systems Engineering, Penn Engineering
This project will apply a novel interpretable machine learning approach, known as the Distributed Information Bottleneck, to solve pressing problems in identifying and displaying critical information during time-sensitive clinical encounters. This project will develop a framework for the optimal integration of information from multiple physiologic measures that are continuously monitored during delivery room resuscitation. The team’s immediate goal is to detect and display key target respiratory parameters during delivery room resuscitation to prevent acute and chronic lung injury for preterm infants. Because this approach is generalizable to any setting in which complex relations between information-rich variables are predictive of health outcomes, the project will lay the groundwork for future applications to other clinical scenarios.
