A processed image representative of the types of images used in this study. Natural landscapes were transformed into binary images, ones made of black and white pixels, that were decomposed into different textures defined by specific statistics. (Image: Eugenio Piasini)
The human brain uses more energy than any other organ in the body, requiring as much as 20% of the body’s total energy. While this may sound like a lot, the amount of energy would be even higher if the brain were not equipped with an efficient way to represent only the most essential information within the vast, constant stream of stimuli taken in by the five senses. The hypothesis for how this works, known as efficient coding, was first proposed in the 1960s by vision scientist Horace Barlow.
Now, new research from the Scuola Internazionale Superiore di Studi Avanzati (SISSA) and the University of Pennsylvania provides evidence of efficient visual information coding in the rodent brain, adding support to this theory and its role in sensory perception. Published in eLife, these results also pave the way for experiments that can help understand how the brain works and can aid in developing novel artificial intelligence (AI) systems based on similar principles.
According to information theory—the study of how information is quantified, stored, and communicated—an efficient sensory system should only allocate resources to how it represents, or encodes, the features of the environment that are the most informative. For visual information, this means encoding only the most useful features that our eyes detect while surveying the world around us.
Vijay Balasubramanian, a computational neuroscientist at Penn, has been working on this topic for the past decade. “We analyzed thousands of images of natural landscapes by transforming them into binary images, made up of black and white pixels, and decomposing them into different textures defined by specific statistics,” he says. “We noticed that different kinds of textures have different variability in nature, and human subjects are better at recognizing those which vary the most. It is as if our brains assign resources where they are most necessary.”
Jennifer E. Phillips-Cremins, Associate Professor and Dean’s Faculty Fellow in Bioengineering and Genetics, has been awarded the 2022 Dr. Susan Lim Award for Outstanding Young Investigator by the International Society for Stem Cell Research (ISSCR), the preeminent, global organization dedicated to stem cells research.
This award recognizes the exceptional achievements of an investigator in the early part of his or her independent career in stem cell research. Cremins works in the field of epigenetics, and is a pioneer in understanding how chromatin, the substance within a chromosome, works:
“Dr. Phillips-Cremins is a gifted researcher with diverse skills across cell, molecular, and computational biology. She is a shining star in the stem cell field who has already made landmark contributions in bringing long-range chromatin folding mechanisms to stem cell research. In addition to her skills as an outstanding researcher,” ISSCR President Melissa Little, Ph.D., said. “She has flourished as an independent investigator, providing the stem cell field with unique and creative approaches that have facilitated conceptual leaps in our understanding of long-range spatial regulation of stem cell fate. Congratulations, Jennifer, on this prestigious honor.”
Cremins was awarded a NIH Director’s Pioneer Award in 2021 and a Chan Zuckerberg Initiative (CZI) grant as part of the CZI Collaborative Pairs Pilot Project in 2020. The long-term goal of her lab is to understand the mechanisms by which chromatin architecture governs genome function. The ISSCR will recognize Cremins and her research in a plenary session during the ISSCR annual meeting on June 15.
The Very Large Scale Microfluidic Integration (VLSMI) platform, a technology developed by the Penn researchers, contains hundreds of mixing channels for mass-producing mRNA-carrying lipid nanoparticles.
Penn Engineering secured a multi-million-dollar contract with Wellcome Leap under the organization’s $60 million RNA Readiness + Response (R3) program, which is jointly funded with the Coalition for Epidemic Preparedness Innovations (CEPI). Penn Engineers aim to create “on-demand” manufacturing technology that can produce a range of RNA-based vaccines.
The Penn Engineering team features Daeyeon Lee, Evan C Thompson Term Chair for Excellence in Teaching and Professor in Chemical and Biomolecular Engineering, Michael Mitchell, Skirkanich Assistant Professor of Innovation in Bioengineering, David Issadore, Associate Professor in Bioengineering and Electrical and Systems Engineering, and Sagar Yadavali, a former postdoctoral researcher in the Issadore and Lee labs and now the CEO of InfiniFluidics, a spinoff company based on their research. Drew Weissman of the Perelman School of Medicine, whose foundational research directly continued to the development of mRNA-based COVID-19 vaccines, is also a part of this interdisciplinary team.
The success of these COVID-19 vaccines has inspired a fresh perspective and wave of research funding for RNA therapeutics across a wide range of difficult diseases and health issues. These therapeutics now need to be equitably and efficiently distributed, something currently limited by the inefficient mRNA vaccine manufacturing processes which would rapidly translate technologies from the lab to the clinic.
The Penn researchers’ latest paper on the design of lipid nanoparticles was featured on the cover of the most recent edition of the journal Nano Letters.
From COVID vaccines to cancer immunotherapies to the potential for correcting developmental disorders in utero, mRNA-based approaches are a promising tool in the fight against a wide range of diseases. These treatments all depend on providing a patient’s cells with genetic instructions for custom proteins and other small molecules, meaning that getting those instructions inside the target cells is of critical importance.
The current delivery method of choice uses lipid nanoparticles (LNPs). Thanks to surfaces customized with binding and signaling molecules, they encapsulate mRNA sequences and smuggle them through the cell membrane. But with a practically unlimited number of variables in the makeup of those surfaces and molecules, figuring out how to design the most effective LNP is a fundamental challenge.
Now, in a study featured on the cover of the journal Nano Letters, researchers from the University of Pennsylvania’s School of Engineering and Applied Science and Perelman School of Medicine have now shown how to computationally optimize the design of these delivery vehicles.
Using an established methodology for comparing a wide range of variables known as “orthogonal design of experiments,” the researchers simultaneously tested 256 candidate LNPs. They found the frontrunner was three times better at delivering mRNA sequences into T cells than the current standard LNP formulation for mRNA delivery.
The study was led by Michael Mitchell, Skirkanich Assistant Professor of Innovation in the Department of Bioengineering in Penn’s School of Engineering and Applied Science, and Margaret Billingsley, a graduate student in his lab.
Heatmaps are used by researchers in the lab of Jennifer Phillips-Cremins to visualize which physically distant genes are brought into contact when the genome is in its folded state.
More data is being produced across diverse fields within science, engineering, and medicine than ever before, and our ability to collect, store, and manipulate it grows by the day. With scientists of all stripes reaping the raw materials of the digital age, there is an increasing focus on developing better strategies and techniques for refining this data into knowledge, and that knowledge into action.
Enter data science, where researchers try to sift through and combine this information to understand relevant phenomena, build or augment models, and make predictions.
One powerful technique in data science’s armamentarium is machine learning, a type of artificial intelligence that enables computers to automatically generate insights from data without being explicitly programmed as to which correlations they should attempt to draw.
Advances in computational power, storage, and sharing have enabled machine learning to be more easily and widely applied, but new tools for collecting reams of data from massive, messy, and complex systems—from electron microscopes to smart watches—are what have allowed it to turn entire fields on their heads.
“This is where data science comes in,” says Susan Davidson, Weiss Professor in Computer and Information Science (CIS) at Penn’s School of Engineering and Applied Science. “In contrast to fields where we have well-defined models, like in physics, where we have Newton’s laws and the theory of relativity, the goal of data science is to make predictions where we don’t have good models: a data-first approach using machine learning rather than using simulation.”
Penn Engineering’s formal data science efforts include the establishment of the Warren Center for Network & Data Sciences, which brings together researchers from across Penn with the goal of fostering research and innovation in interconnected social, economic and technological systems. Other research communities, including Penn Research in Machine Learning and the student-run Penn Data Science Group, bridge the gap between schools, as well as between industry and academia. Programmatic opportunities for Penn students include a Data Science minor for undergraduates, and a Master of Science in Engineering in Data Science, which is directed by Davidson and jointly administered by CIS and Electrical and Systems Engineering.
Penn academic programs and researchers on the leading edge of the data science field will soon have a new place to call home: Amy Gutmann Hall. The 116,000-square-foot, six-floor building, located on the northeast corner of 34th and Chestnut Streets near Lauder College House, will centralize resources for researchers and scholars across Penn’s 12 schools and numerous academic centers while making the tools of data analysis more accessible to the entire Penn community.
Faculty from all six departments in Penn Engineering are at the forefront of developing innovative data science solutions, primarily relying on machine learning, to tackle a wide range of challenges. Researchers show how they use data science in their work to answer fundamental questions in topics as diverse as genetics, “information pollution,” medical imaging, nanoscale microscopy, materials design, and the spread of infectious diseases.
Bioengineering: Unraveling the 3D genomic code
Scattered throughout the genomes of healthy people are tens of thousands of repetitive DNA sequences called short tandem repeats (STRs). But the unstable expansion of these repetitions is at the root of dozens of inherited disorders, including Fragile X syndrome, Huntington’s disease, and ALS. Why these STRs are susceptible to this disease-causing expansion, whereas most remain relatively stable, remains a major conundrum.
Complicating this effort is the fact that disease-associated STR tracts exhibit tremendous diversity in sequence, length, and localization in the genome. Moreover, that localization has a three-dimensional element because of how the genome is folded within the nucleus. Mammalian genomes are organized into a hierarchy of structures called topologically associated domains (TADs). Each one spans millions of nucleotides and contains smaller subTADs, which are separated by linker regions called boundaries.
Associate professor and Dean’s Faculty Fellow Jennifer E. Phillips-Cremins.
“The genetic code is made up of three billion base pairs. Stretched out end to end, it is 6 feet 5 inches long, and must be subsequently folded into a nucleus that is roughly the size of a head of a pin,” says Jennifer Phillips-Cremins, associate professor and dean’s faculty fellow in Bioengineering. “Genome folding is an exciting problem for engineers to study because it is a problem of big data. We not only need to look for patterns along the axis of three billion base pairs of letters, but also along the axis of how the letters are folded into higher-order structures.”
To address this challenge, Phillips-Cremins and her team recently developed a new mathematical approach called 3DNetMod to accurately detect these chromatin domains in 3D maps of the genome in collaboration with the lab of Dani Bassett, J. Peter Skirkanich Professor in Bioengineering.
“In our group, we use an integrated, interdisciplinary approach relying on cutting-edge computational and molecular technologies to uncover biologically meaningful patterns in large data sets,” Phillips-Cremins says. “Our approach has enabled us to find patterns in data that classic biology training might overlook.”
In a recent study, Phillips-Cremins and her team used 3DNetMod to identify tens of thousands of subTADs in human brain tissue. They found that nearly all disease-associated STRs are located at boundaries demarcating 3D chromatin domains. Additional analyses of cells and brain tissue from patients with Fragile X syndrome revealed severe boundary disruption at a specific disease-associated STR.
“To our knowledge, these findings represent the first report of a possible link between STR instability and the mammalian genome’s 3D folding patterns,” Phillips-Cremins says. “The knowledge gained may shed new light into how genome structure governs function across development and during the onset and progression of disease. Ultimately, this information could be used to create molecular tools to engineer the 3D genome to control repeat instability.”
Speaker: Shelly R. Peyton, Ph.D.
Professor, Armstrong Professional Development Professor
Chemical Engineering, Biomedical Engineering Adjunct
College of Engineering
University of Massachusetts Amherst
Date: Thursday, December 9, 2021
Time: 3:30-4:30 PM EST
Zoom – check email for link
This seminar will be held virtually, but students registered for BE 699 can gather to watch in Moore 216.
Abstract: Improved experimental model systems are critically needed to better understand cancer progression and bridge the gap between lab bench proof-of-concept studies, validation in animal models, and eventual clinical application. Many methods exist to create biomaterials, including hydrogels, which we use to study cells in contexts more akin to what they experience in the human body. Our lab has multiple approaches to create such biomaterials, based on combinations of poly(ethylene glycol) (PEG) with peptides and zwitterions. In this presentation, I will discuss our synthetic approaches to building life-like materials, how we use these systems to grow cells and understand how a cell’s environment, particularly the extracellular matrix regulates cancer cell growth, dormancy, and drug sensitivity.
Shelly Peyton Bio: Shelly Peyton is the Armstrong Professor and Graduate Program Director, and chair of the Diversity, Equity, and Inclusion (DEI) committee of Chemical Engineering at the University of Massachusetts Amherst. She is co-director of the Models 2 Medicine Center in the Institute for Applied Life Sciences. She received her B.S. in Chemical Engineering from Northwestern University in 2002 and went on to obtain her MS and PhD in Chemical Engineering from the University of California, Irvine. She was then an NIH Kirschstein post-doctoral fellow in the Biological Engineering department at MIT before starting her academic appointment at UMass in 2011. Shelly leads an interdisciplinary group of engineers and molecular cell biologists seeking to create and apply novel biomaterials platforms toward new solutions to grand challenges in human health. Her lab’s unique approach is using our engineering expertise to build simplified models of human tissue with synthetic biomaterials. They use these systems to understand 1) the physical relationship between metastatic breast cancer cells and the tissues to which they spread, 2) the role of matrix remodeling in drug resistance, and 3) how to create bioinspired mechanically dynamic and activatable biomaterials. Among other honors for her work, Shelly was a 2013 Pew Biomedical Scholar, received a New Innovator Award from the NIH, and she was awarded a CAREER grant from the NSF. Shelly is co-PI with Jeanne Hardy on the Biotechnology (BTP) NIH T32 program and is a co-PI of the PREP program at UMass, which brings students from URM groups to UMass for a 1-year post-BS study to help prepare them for graduate school.
Kariyawasam is a double major in Engineering’s Department of Bioengineering, with concentrations in computational medicine and medical devices, and in the Wharton School, with concentrations in finance and entrepreneurship and innovation.
“We are so proud of our newest Penn Rhodes Scholars who have been chosen for this tremendous honor and opportunity,” said President Amy Gutmann. “The work Raveen has done in health care innovation and accessibility and Nicholas has done to support student well-being while at Penn is impressive, and pursuing a graduate degree at Oxford will build upon that foundation. We look forward to seeing how they make an impact in the future.”
The Rhodes is highly competitive and one of the most prestigious scholarships in the world. The scholarships provide all expenses for as long as four years of study at Oxford University in England.
According to the Rhodes Trust, about 100 Rhodes Scholars will be selected worldwide this year, chosen from more than 60 countries. Several have attended American colleges and universities but are not U.S. citizens and have applied through their home country, including Kariyawasam in Sri Lanka.
While biologists and chemists race to develop new antibiotics to combat constantly mutating bacteria, predicted to lead to 10 million deaths by 2050, engineers are approaching the problem through a different lens: finding naturally occurring antibiotics in the human genome.
The billions of base pairs in the genome are essentially one long string of code that contains the instructions for making all of the molecules the body needs. The most basic of these molecules are amino acids, the building blocks for peptides, which in turn combine to form proteins. However, there is still much to learn about how — and where — a particular set of instructions are encoded.
Now, bringing a computer science approach to a life science problem, an interdisciplinary team of Penn researchers have used a carefully designed algorithm to discover a new suite of antimicrobial peptides, hiding deep within this code.
The study, published in Nature Biomedical Engineering, was led by César de la Fuente, Presidential Assistant Professor in Bioengineering, Microbiology, Psychiatry, and Chemical and Biomolecular Engineering, spanning both Penn Engineering and Penn Medicine, and his postdocs Marcelo Torres and Marcelo Melo. Collaborators Orlando Crescenzi and Eugenio Notomista of the University of Naples Federico II also contributed to this work.
“The human body is a treasure trove of information, a biological dataset. By using the right tools, we can mine for answers to some of the most challenging questions,” says de la Fuente. “We use the word ‘encrypted’ to describe the antimicrobial peptides we found because they are hidden within larger proteins that seem to have no connection to the immune system, the area where we expect to find this function.”
Cathy and Marc Lasry Professor Vijay Balasubramanian at Penn’s BioPond.
A study published in PLOS Computational Biology describes a new model for how the olfactory system discerns unique odors. Researchers from the University of Pennsylvania found that a simplified, statistics-based model can explain how individual odors can be perceived as more or less similar from others depending on the context. This model provides a starting point for generating new hypotheses and conducting experiments that can help researchers better understand the olfactory system, a complex, crucial part of the brain.
The sense of smell, while crucial for things like taste and hazard avoidance, is not as well studied as other senses. Study co-author Vijay Balasubramanian, a theoretical physicist with an interest in how living systems process information, says that olfaction is a prime example of a complex information-processing system found in nature, as there are far more types of volatile molecules—on the scale of tens or hundreds of thousands—than there are receptor types in the nose to detect them, on the scale of tens to hundreds depending on the species.
“Every molecule can bind to many receptors, and every receptor can bind to many molecules, so you get this combinatorial mishmash, with the nose encoding smells in a way that involves many receptor types to collectively tell you what a smell is,” says Balasubramanian. “And because there are many fewer receptor types than molecular species, you basically have to compress a very high dimensional olfactory space into a much lower dimensional space of neural responses.”
A cross-disciplinary Penn team is pioneering a new approach to peripheral nerve repair.
In a new publication in the journal npjRegenerative Medicine, a team of Penn researchers from the School of Dental Medicine and the Perelman School of Medicine “coaxed human gingiva-derived mesenchymal stem cells (GMSCs) to grow Schwann-like cells, the pro-regenerative cells of the peripheral nervous system that make myelin and neural growth factors,” addressing the need for regrowing functional nerves involving commercially-available scaffolds to guide nerve growth. The study was led by Anh Le, Chair and Norman Vine Endowed Professor of Oral Rehabilitation in the Department of Oral and Maxillofacial Surgery/Pharmacology at the University of Pennsylvania School of Dental Medicine, and was co-authored by D. Kacy Cullen, Associate Professor in Neurosurgery at the Perelman School of Medicine at Penn and the Philadelphia Veterans Affairs Medical Center and member of the Bioengineering Graduate Group:
D. Kacy Cullen (Image: Eric Sucar)
“To get host Schwann cells all throughout a bioscaffold, you’re basically approximating natural nerve repair,” Cullen says. Indeed, when Le and Cullen’s groups collaborated to implant these grafts into rodents with a facial nerve injury and then tested the results, they saw evidence of a functional repair. The animals had less facial droop than those that received an “empty” graft and nerve conduction was restored. The implanted stem cells also survived in the animals for months following the transplant.
“The animals that received nerve conduits laden with the infused cells had a performance that matched the group that received an autograft for their repair,” he says. “When you’re able to match the performance of the gold-standard procedure without a second surgery to acquire the autograft, that is definitely a technology to pursue further.”
Read the full story and view the full list of collaborators in Penn Today.
