Danielle Bassett, J. Peter Skirkanich Professor in the departments of Bioengineering and Electrical and Systems Engineering, has been called the “doyenne of network neuroscience.” The burgeoning field applies insights from the field of network science, which studies how the structure of networks relate to their performance, to the billions of neuronal connections that make up the brain.
Much of Basset’s research draws on mathematical and engineering principles to better understand how mental traits arise, but also applies them more broadly to other challenges in neuroscience.
The researchers used machine learning techniques to create a new classification system for neurodegenerative diseases, one which may redraw the boundaries between them and help explain clinical differences in patients who received the same diagnoses.
BioWorld’s Anette Breindl spoke with Bassett about the team’s findings.
Now, investigators have developed a new approach to classifying neurodegenerative disorders that used the overall patterns of protein aggregation, rather than specific proteins, to define six clusters of patients that crossed traditional diagnostic categories.
“We find that perhaps the way that clinicians have been diagnosing these disorders… is not necessarily the way these disorders work,” Danielle Bassett told BioWorld. “The way we’ve been trying to carve nature at joints is not the way that nature has joints. The joints are elsewhere.”
Continue reading Breindl’s article, “For neurodegeneration, a different way to slice the pie,” at BioWorld.
The BWF CASI Career Awards provide $500,000 over five years to bridge advanced postdoctoral training and the first three years of faculty service; and to foster the early career development of researchers who have transitioned from physical/mathematical/computational sciences or engineering into the biological sciences, and who are dedicated to pursuing a career in academic research. Goyal is one of just eight recipients of the 2020-2025 CASI award.
Goyal’s research work is centered around developing novel mathematical and experimental frameworks to study how a rare subpopulation of cancer cells are able to survive drug therapy and develop resistance, resulting in relapse in patients. In particular, his work will provide a view of different paths that single cancer cells take when becoming resistant, at unprecedented resolution and scale. In turn, this will help devise novel therapeutic strategies to combat the challenge of drug resistance in cancer.
“I am very excited to be a part of the community of the Burroughs Wellcome Fund CASI award past and present recipients, which also includes my postdoctoral adviser Arjun Raj, who received this award in 2008,” Goyal says. “This CASI award will help provide me with the freedom to pursue high risk research directions as I transition to faculty. I feel fortunate to be surrounded by kind and supportive colleagues in the Bioengineering Department at Penn, an environment that has been critical for my interdisciplinary journey as a scientist.”
Penn’s brainSTIM center will study neuromodulation to repair and enhance human brain function
Penn Medicine has launched a new center to study the brain, one of the most complex systems in the body:
The Penn Brain Science, Translation, Innovation, and Modulation (brainSTIM) Center brings together a team of leading neuroscientists, neurologists, psychiatrists, psychologists, and engineers at Penn using neuromodulation techniques to research, repair, and enhance human brain function—the first translational center of its kind in the region.
Among the key faculty involved in this new center is J. Peter Skirkanich Professor of Bioengineering Danielle Bassett. Bassett’s Complex Systems Lab studies biological, physical, and social systems by using and developing tools from network science and complex systems theory. Bassett, along with Assistant Professor of Psychiatry Desmond Oathes, will work to:
understand how TMS [i.e. transcranial magnetic stimulation] might improve working memory in healthy adults and those with ADHD by combining network control theory (a set of concepts and principles employed in engineering), magnetic stimulation of the brain, and functional brain imaging.
In a Q&A, Bioengineering doctoral candidate Ana P. Peredo explains how the idea of “regeneration” motivated her to join WIVA, Wharton Social Impact’s impact investing program.
Why would you — a bioengineering Ph.D. student — seek to join WIVA?
As a bioengineering Ph.D. student, Ana P. Peredo is currently working on the development of regenerative methods and drug-delivery approaches for musculoskeletal tissue
“As a high school student, I was motivated to study bioengineering because of its potential to generate impact through technical innovation. To me, bioengineering was a way to apply engineering principles to create medical technology in the hopes of devising solutions for global health concerns.
Though I have gained significant understanding of the current pressing healthcare needs, I felt that I was missing a key understanding of how investors think about social impact. To better understand how to apply my science background to the impact space, I joined WIVA. I also wanted to venture outside of healthcare and learn about other important social impact sectors such as education, energy, and environment, all of which WIVA explores in its deal-sourcing process.”
What have you learned through WIVA that you have not been exposed to before?
“I learned how to assess early-stage startups for their impact and return-on-investment potential, as well as how to rigorously analyze company financials and projections.
I also had the opportunity to meet leading social impact professionals through WIVA. I attended a Wharton Social Impact Initiative event with Vincent Stanley, the Director of Philosophy at Patagonia. From this discussion, I learned about how the word ‘sustainable’ continues to be misused by companies and how companies should try to ‘regenerate’ the resources they consume to be truly deemed sustainable.
This conversation brought to mind my research experience with regeneration — could I use my WIVA deal-sourcing techniques to find impactful startups that use this concept?”
The paper-based tests could be integrated directly into facemasks and provide instant results at testing sites.
Cesar de la Fuente-Nunez, PhD
When Penn Health-Tech announced its Nemirovsky Engineering and Medicine Opportunity, or NEMO Prize, in February, the center’s researchers could only begin to imagine the impact the looming COVID-19 pandemic was about to unleash. But with the promise of $80,000 to support early-stage ideas at the intersection of engineering and medicine, the contest quickly sparked a winning innovation aimed at combating the crisis.
Judges from the University of Pennsylvania’s School of Engineering and Applied Sciences and Perelman School of Medicine awarded its first NEMO Prize to César de la Fuente, PhD, who proposed a paper-based COVID diagnostic system that could capture viral particles on a person’s breath, then give a result in a matter of seconds when taken to a testing site.
Similar tests for bacteria cost less than a dollar each to make. De la Fuente, a Presidential Assistant Professor in the departments of Psychiatry, Microbiology, and Bioengineering, is aiming to make COVID tests at a similar price point and with a smaller footprint so that they could be directly integrated into facemasks, providing further incentive for their regular use.
“Wearing a facemask is vital to containing the spread of COVID because, before you know you’re sick, they block your virus-carrying droplets so those droplets can’t infect others,” de la Fuente says. “What we’re proposing could eventually lead to a mask that can be infected by the virus and let you know that you’re infected, too.”
De la Fuente’s lab has conducted molecular dynamic simulations of the regions of the SARS-COV-2 spike protein (blue) that bind to the human ACE2 receptor (red and yellow).
De la Fuente’s expertise is in synthetic biology and molecular-scale simulations of disease-causing viruses and bacteria. Having such fine-grained computational models of these microbes’ binding sites allow de la Fuente to test them against massive libraries of proteins, seeing which bind best. Other machine learning techniques can then further narrow down the minimum molecular structures responsible for binding, resulting in functional protein fragments that are easier to synthesize and manipulate.
The spike-shaped proteins that give coronaviruses their crown-like appearance and name bind to a human receptor known as ACE2. De la Fuente and his colleagues are now aiming to characterize the molecular elements and environmental factors that would allow for the most precise, reliable detection of the virus.
An interdisciplinary research team has found statistical evidence of women being under-cited in academic literature. They are now studying similar effects along racial lines.
By Izzy Lopez
Danielle Bassett, Jordan Dworkin and Perry Zurn are leading efforts to analyze systemic bias in neuroscience citations, and have suggestions for combatting it.
Scientific papers are the backbone of a research community and the citation of those papers sparks conversation in a given field. This cycle of publication and citation leads to new knowledge, but what happens when implicit discrimination in a field leads to papers by minority scholars being cited less often than their counterparts? A new team of researchers has come together to ask this question and dig into the numbers of gender and racial bias in neuroscience.
The team members include physicist and neuroscientist Danielle Bassett, J. Peter Skirkanich Professor of Bioengineering at the University of Pennsylvania, with secondary appointments in the Departments of Neurology and Psychiatry in Penn’s Perelman School of Medicine, statistician Jordan Dworkin, then a graduate student in Penn Medicine’s Department of Biostatistics, Epidemiology and Bioinformatics, and ethicist Perry Zurn, an Assistant Professor of Philosophy at American University.
Their study on gender bias, which recently appeared in Nature Neuroscience, reports on the extent and drivers of gender imbalance in neuroscience reference lists. The team has also published a perspective paper in Neuron that makes practical recommendations for improving awareness of this issue and correcting for biases.
They are now working on a second study, led by Maxwell Bertolero, a postdoctoral researcher in Bassett’s lab, that considers the extent and drivers of racial imbalances in neuroscience reference lists.
Together, Bassett, Dworkin and Zurn are using their combined research strengths to uncover the under-citation of women or otherwise minority-led papers in neuroscience and to assess its significance. This research is fundamental in highlighting a true gap in representation in research paper citations, which can have detrimental effects for women and other minorities leading science. In addition, they provide actionable steps to address the problem and build a more equitable future.
Your research team is a distinctive one. How did you come together for a study about gender discrimination in neuroscience citations?
Jordan Dworkin: It was a fortunate coincidence. In the run-up to a big neuroscience conference, I started seeing discussions on Twitter about gender-based discrimination in neuroscience. There were stories being shared of women’s papers being overlooked and reviewers seeing reference lists that were almost entirely made up of men. It was illuminating, especially because some people in the discussion were hesitant to take those experiences at face value. This skepticism, and occasional combativeness, seemed to stem from the view that citations are an untouchable, scientific bastion where researchers’ decisions are fully objective. The tension between that view and scholars’ lived experiences encouraged me to explore the existing literature on this issue.
As it turns out, there is really strong literature on issues of diversity and citation in science. Some disciplines have done field-specific investigations, such as the foundational studies in political science, international relations, and economics, but there wasn’t yet any research in neuroscience. Since biomedical sciences often have different approaches to citation, it seemed that it would be worth doing a deeper neuroscience-specific investigation to give quantitative backing to the issue of gender bias in neuroscience research.
Danielle Bassett: When Jordan and I started working together on this project, I knew it was important. To do it right, we needed to present the information in a way that made it actionable, with clear recommendations about how each of us as scientists can help address the issue. We also needed to add someone to the team with expertise in gender theory and research ethics. We especially wanted to make sure we were discussing gender bias in a way that was informed by recent advances in gender studies. That’s when we brought Perry in.
Perry Zurn: I’m a philosopher by training, with a focus on ethics and politics. Citations are both an ethical and a political issue. Citations reflect whose questions and whose contributions are recognized as important in the scholarly conversation. As such, citations can either bring in marginalized voices, voices that have been historically excluded from a conversation, or they can simply replicate that exclusion. My own field of philosophy has just as much of a problem with gender and racial diversity as STEM fields, something Dani and I have been talking about for a long time. This work seemed like a natural point of collaboration.
Describe this study and what it means for promoting gender diversity in neuroscience.
Bassett: For years now, various scholars and activists in science have drawn attention to issues of gender and racial inequities in the field. Most of these conversations, however, have placed responsibility for change in the hands of people in power, such as journal editors, grant reviewers, department chairs, presidents of scientific societies, etc. But many of the imbalances people notice, whether in conversation with peers or through studies like ours, are perpetuated by researchers at all levels. Given that every research project is built on prior research, and therefore every paper has a reference list of citations, every researcher can make a difference. Who we choose to cite matters.
Dworkin: To understand the role of gender in citation practices, we looked at the authors and reference lists of articles published in five top neuroscience journals since 1995. We accounted for self-citations, and various potentially relevant characteristics of papers, and we found that women-led papers are under-cited relative to what would be expected if gender was not a consideration in citation behavior. Importantly, we also found that the under-citation of women-led papers is driven largely by the citation behavior of men-led teams. We also found that this trend is getting worse over time, because the field is getting more diverse while citation rates are generally staying the same.
For a very simple example, if there were 10 women and 90 men neuroscientists in 1980, then citing 10% women would be roughly proportional. But with a diversifying field, say there are now 200 women and 200 men neuroscientists and citations are still 10% women. Sure, the percentage of women cited didn’t go down, but that percentage is now vastly lower than the true percentage in the field. That’s a dramatic example, but it shows you that if we’re going to call for equality in scientific citation, the number of women-led papers on a given reference list should reflect, or even exceed, the number of available and relevant women-led papers in a field, and our work found that it does not.
Bassett: This under-citation of women scientists is a key issue because the gaps in the amount of engagement that women’s work receives could have detrimental downstream effects on conference invitations, grant and fellowship awards, tenure and promotion, inclusion in syllabi, and even student evaluations. As a result, understanding and eliminating gender bias in citation practices is vital for addressing gender imbalances in a field.
Why are citations important to gender representation in neuroscience?
Dworkin: Unlike hiring and grant funding, citations are something every researcher participates in. For example, as a graduate student I did not have any role on a faculty search committee, or any power in an academic society to decide on conference speakers, but I still have reference lists in all my papers. Citations are a unique area where all researchers play a direct role, where each person has a chance to reflect on their own practices and use those practices to create change in their field. Their ubiquity means that citations function as a conversation within a field, and their presence or absence can signal whose work is valued and whose is not. On a more concrete level, citations are often used as metrics for a variety of important, potentially career-defining, decisions.
Bassett: There are a lot of underrepresented scholars who have fantastic ideas and write really interesting papers but they’re not being acknowledged — and cited — in the way they deserve. And there are great role models for all the young women who are thinking about going into science, but unless the older women scientists are being cited, the younger ones will never see them. Without serious changes in the field, and a deep commitment to gender and racial diversity, many young women and minority scientists won’t stick with it, they won’t be hired, they won’t be promoted, and they won’t be put in the textbooks.
Zurn: Exactly. I think it’s important not only to think about who we’re citing as leading scientists, but also what sorts of people we’re representing as scientists at all. If you are looking at neuroscience as a field and you see predominantly white cisgender men in the research labs and the reference lists, then you begin to think that is what a neuroscientist looks like. But this homogeneity is neither representative of an increasingly diverse field like neuroscience, nor supportive of continuing efforts to diversify STEM in general. We need to expand what a scientist looks like and citations are one way to do that.
Danielle Bassett also has appointments in Penn Engineering’s Department of Electrical and Systems Engineering and Penn Arts & Sciences Department of Physics and Astronomy.
Jordan Dworkin is now an Assistant Professor of Clinical Biostatistics in the Department of Psychiatry at Columbia University.
Kristin Linn, Assistant Professor of Biostatics, Russell Shinohara, Associate Professor of Biostatistics, and Erin Teich, a postdoctoral researcher in Bassett’s lab, also contributed to the study published in Nature Neuroscience. It was supported by the National Institute of Neurological Disorders and Stroke through grants R01 NS085211 and R01 NS060910, the John D. and Catherine T. MacArthur Foundation, the Alfred P. Sloan Foundation, and the National Science Foundation through CAREER Award PHY-1554488.
In May, Lyle Ungar, Professor of Computer and Information Science and Angela Duckworth, Rosa Lee and Egbert Chang Professor in Penn Arts & Sciences and the Wharton School, contributed to a New York Times op-ed on how to slow the COVID-19 pandemic through a culture of mask-wearing.
As infections continue to rise, Ungar and Duckworth are following up with another op-ed. Writing in the Philadelphia Inquirer, they outline the need to rapidly ramp-up the city and state’s contact tracing capacity:
Guidelines from health officials suggest Pennsylvania needs about 4,000 contact tracers, including 2,000 for the Philadelphia metro area. Our state has been operating with fewer than 200.
Huwe earned dual B.S. degrees in Biology and Chemistry in 2009 from Mississippi College, where he was inducted into the Hall of Fame. At Mississippi College, Huwe had his first exposure to computational research in the laboratory of David Magers, Professor of Chemistry and Biochemistry. He went on to earn his Ph.D. in Biochemistry and Molecular Biophysics in 2014 in the laboratory of Ravi Radhakrishnan, Chair of the Bioengineering Department at Penn. As an NSF Graduate Research Fellow in Radhakrishnan’s lab, Huwe focused his research on using computational molecular modeling and simulations to elucidate the functional consequences of protein mutations associated with human diseases. Dr. Huwe then joined the structural bioinformatics laboratory Roland Dunbrack, Jr., Professor at the Fox Chase Cancer Center as a T32 post-doctoral trainee. During his post-doctoral training, Huwe held adjunct teaching appointments at Thomas Jefferson University and at the University of Pennsylvania. In 2017, Huwe became an Assistant Professor of Biology at Temple University, where he taught medical biochemistry, medical genetics, cancer biology, and several other subjects.
During each of his appointments, Huwe became increasingly more passionate about teaching, and he decided to dedicate his career to medical education. Huwe is very excited to be joining Mercer University School of Medicine as an Assistant Professor of Biomedical Sciences this summer. There, he will serve in a medical educator track, primarily teaching first and second year medical students.
“Without Ravi Radhakrishnan and Philip Rea, Professor of Biology in Penn’s School of Arts & Sciences, giving me my first teaching opportunities as a graduate guest lecturer at Penn, I may never have discovered how much I love teaching,” says Huwe. “And without the support and guidance of each of my P.I.’s [Dr.’s Magers, Radhakrishnan, and Dunbrack], I certainly would not be where I am, doing what I love. I am incredibly thankful for all of the people who helped me in my journey to find my dream job.”
Congratulations and best of luck from everyone in Penn Bioengineering, Dr. Huwe!
A new series of short videos on the BE Labs Youtube Channel highlights the unique and innovative approach to engineering education found in The George H. Stephenson Foundation Educational Laboratory & Bio-MakerSpace, the primary teaching lab for the Department of Bioengineering at Penn Engineering. This video series explores how “engineering is fundamentally interdisciplinary” and demonstrates the ways in which Penn students from Bioengineering and beyond have combined the fields of biology, chemistry, and electrical, mechanical, and materials engineering into one exciting and dynamic “MakerSpace.”
“Our Bio-MakerSpace” takes viewers on a tour inside BE’s one-of-a-kind educational laboratories.
Produced primarily on smart phones and with equipment borrowed from the Penn Libraries, and software provided by Computing and Educational Technology Services, the videos were made by rising Bioengineering junior Nicole Wojnowski (BAS ‘22). Nicole works on staff as a student employee of the BE Labs and as a student researcher in the Gottardi Lab at the Children’s Hospital of Philadelphia (CHOP), helmed by Assistant Professor of Pediatrics Riccardo Gottardi.
Sevile Mannickarottu, Director of the Educational Labs in Bioengineering, says that the philosophy of the Bio-MakerSpace “encourages a free flow of ideas, creativity, and entrepreneurship between Bioengineering students and students throughout Penn. We are the only open Bio-MakerSpace with biological, chemical, electrical, materials, and mechanical testing and fabrication facilities, all in one place, anywhere.”
Previous stories on the BE blog have gone into detail about how BE’s Bio-MakerSpace has become a hub for start-ups in recent years, how students can build their own makerspace for under $1500, and more. Major award-winning start-ups including Strella Biotechnology and InstaHub got their start in the BE Labs.
To learn more about the Bio-MakerSpace, check out the other videos below.
Bioengineering doctoral student Dayo Adewole co-founded the company Instahub, which also took home a PIP award in 2019. Dayo also graduated from the BE undergraduate program in 2014. In this video, he discusses the helpfulness and expertise of the BE Labs staff.
Senior Associate Dean for Penn Engineering and Solomon R. Pollack Professor in Bioengineering David Meaney discusses how the Bio-MakerSpace is the only educational lab on campus to provide “all of the components that one would need to make the kinds of systems that bioengineers make.”
New research finds that works of literature, musical pieces, and social networks have a similar underlying structure that allows them to share large amounts of information efficiently.
Examples of statistical network analysis of characters in two of Shakespeare’s tragedies. Two characters are connected by a line, or edge, if they appear in the same scene. The size of the circles that represent these characters, called nodes, indicate how many other characters one is connected to. The network’s density relates to how complete the graph is, with 100% density meaning that it has all of the characters are connected. (Image: Martin Grandjean)
By Erica K. Brockmeier
To an English scholar or avid reader, the Shakespeare Canon represents some of the greatest literary works of the English language. To a network scientist, Shakespeare’s 37 plays and the 884,421 words they contain also represent a massively complex communication network. Network scientists, who employ math, physics, and computer science to study vast and interconnected systems, are tasked with using statistically rigorous approaches to understand how complex networks, like all of Shakespeare, convey information to the human brain.
New research published in Nature Physics uses tools from network science to explain how complex communication networks can efficiently convey large amounts of information to the human brain. Conducted by postdoc Christopher Lynn, graduate students Ari Kahn and Lia Papadopoulos, and professor Danielle S. Bassett, the study found that different types of networks, including those found in works of literature, musical pieces, and social connections, have a similar underlying structure that allows them to share information rapidly and efficiently.
Technically speaking, a network is simply a statistical and graphical representation of connections, known as edges, between different endpoints, called nodes. In pieces of literature, for example, a node can be a word, and an edge can connect words when they appear next to each other (“my” — “kingdom” — “for” — “a” — “horse”) or when they convey similar ideas or concepts (“yellow” — “orange” — “red”).
The advantage of using network science to study things like languages, says Lynn, is that once relationships are defined on a small scale, researchers can use those connections to make inferences about a network’s structure on a much larger scale. “Once you define the nodes and edges, you can zoom out and start to ask about what the structure of this whole object looks like and why it has that specific structure,” says Lynn.
Building on the group’s recent study that models how the brain processes complex information, the researchers developed a new analytical framework for determining how much information a network conveys and how efficient it is in conveying that information. “In order to calculate the efficiency of the communication, you need a model of how humans receive the information,” he says.
Penn’s brainSTIM center will study neuromodulation to repair and enhance human brain functio
