Tackling rare genetic disorders with patient-focused science

Shannon Knight attributes her interest in neuroscience to an experience she had in high school. She and her sister attended a medical day for students at the nearby University of Illinois Chicago. As they were on their way out of the event, they walked past a room with a person holding a brain.

“We stopped and backpedaled into the room, and I was so fascinated,” says Knight. “I was able to hold the brain of a patient who had passed away of Alzheimer’s. The brain holds so much emotion, decision-making — everything. I realized that this man’s entire memory was in my hands, and something clicked for me. I decided that I really wanted to learn much more about this organ.”

Now in her sixth year of doctoral studies at MIT’s McGovern Institute for Brain Research, Knight is working on developing a novel gene therapy for childhood-onset epilepsy, specifically SYNGAP1 haploinsufficiency. This rare genetic disorder is caused by a mutation in the SYNGAP1 gene, rendering one of the two copies of the gene nonfunctional. 

SYNGAP1 is important for brain development and neuronal communication, and the disorder leads to seizures in children starting as young as 4 months old. Other symptoms include intellectual disabilities, challenges with eating and sleeping, and difficulties with movement.

While there are currently methods to address the symptoms of the disorder, such as anti-seizure medications and dietary restrictions, as the child ages, the seizures often become resistant to medications. Knight is working to develop a therapeutic using CRISPR, a biotechnology tool used to edit genes. This therapeutic aims to address the root cause of this medication resistance by focusing on the gene itself.

“The idea of leading science with empathy is something that I feel very deeply,” she says. “I hope my efforts in the lab work toward the benefit of the people affected, rather than just for the benefit of my own science.”

Researching gene therapies

Knight’s interest in the brain flourished as a neuroscience major at Bowdoin College, working with Professor Hadley Horch. While she had originally planned to be pre-med, Knight ultimately decided that it wasn’t the best fit. She enjoyed the research she did as part of her honors thesis, exploring the regeneration of neurons in the auditory system of crickets, and decided that she wanted to pursue more research in molecular neuroscience, as well as genetics.

After graduating, Knight worked at the Perrimon Lab at Harvard University, where she first learned about CRISPR, applying it in a fruit fly model. She worked for two years in the lab, co-authoring a few papers and applying to graduate schools. 

She ultimately landed in the lab of MIT Professor Guoping Feng, studying the potential of utilizing CRISPR to develop a gene therapy treatment for Phelan-McDermid Syndrome, a rare genetic disorder caused by a deletion or mutation on the 22nd chromosome.

“Many of our graduate students are passionate about making a positive impact to society through cutting-edge research, and Shannon is a perfect example,” says Feng, the James W. and Patricia T. Poitras Professor and associate director at the McGovern Institute. “She is developing gene therapy technologies that have the potential to help many kids with devastating neurodevelopmental disorders.”

Building off of the gene therapy research around Phelan-McDermid syndrome, which is now in clinical trials in patients, Knight is now in the early phases of testing gene therapy for SYNGAP1 disorder. The goal is to go through the same process for the SYNGAP1 gene therapy as for the Phelan-McDermid gene therapy — eventually obtaining U.S. Food and Drug Administration approval and beginning clinical trials. 

The testing of the gene therapy on mice with a version of SYNGAP1 disorder has alleviated seizures and all of the behavioral phenotypes. This promising work is being accelerated by the Rare Brain Disorders Nexus, an MIT initiative that launched in the fall of 2025.

“Something I think about a lot is the idea of who ‘deserves’ the attention of a gene therapy. I feel that, regardless of how rare a genetic disorder might be, it still deserves care,” says Knight. “SYNGAP1 disorder is extremely rare, only impacting one to four out of every 10,000 children. I am very fortunate to be at an institution like MIT that has so many labs and brilliant researchers working on diseases that impact large portions of society, and it was really important to me to spend my PhD years helping a small, often unseen population. Although I don’t actually have a relationship with someone who has SYNGAP1 disorder, I know so many people who feel invisible in systems, and it is really important to me to be able to focus on people who feel unseen and give them hope.”

Inspiring others in the lab

In addition to her passion for neuroscience and genetic research, Knight has also developed a love of teaching. She has been a teaching assistant for class 9.12 (Experimental Molecular Neurobiology), leading the lab portion of the course. She has enjoyed working closely with small classes of students, introducing them to the fundamentals of neuroscience lab research.

“We walked through the process of looking at a specific protein in neurons, and talked about how you can go from cell culture all the way up to a mouse brain — and all the steps in between,” she says. “It was so important to me to be able to teach the students and help them to consider all of the different types of experiments they could do.”

Knight received the Goodwin Medal in 2025 in recognition of her commitment to excellent teaching.

“I’ve talked to many of the students since then,” she says, “and many said it was one of their favorite classes.”

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Q&A: Rethinking how innovation happens

Innovation is a concept that has become mythologized in the modern era: what it is, how to manage it, how to teach it, and how to get it to work for us. Despite these explorations, it remains fundamentally misunderstood, writes Eugene Fitzgerald, the Merton C. Flemings SMA Professor in MIT’s Department of Materials Science and Engineering, in his latest book, “The Invisible Engine: Why Innovation Evades Control.”

Fitzgerald draws on a decade of work leading international research programs, including the MIT and Masdar Institute Cooperative Program and the MIT-Singapore Alliance for Research and Technology, where he explored innovation as the integration of market applications, technology, and implementation.

Written at a moment when artificial intelligence is reshaping how we think about knowledge, research, and innovation, “The Invisible Engine” examines a deeper question: How does innovation actually happen, and how should society invest in it?

In this interview, Fitzgerald discusses his own experiences with innovation — including his co-invention of strained silicon at AT&T Bell Laboratories in the 1990s, which helped extend Moore’s Law, the semiconductor industry’s long-standing trend of increasing the number of transistors on chips roughly every two years — while exploring common misconceptions about innovation, how to create the conditions for it, and novel ways to prepare institutions for future uncertainty.

Q: What inspired you to write this book? 

A: The book really grew out of the last 10 years of work in research-to-market activity, from the MIT Masdar program to the MIT-Singapore Alliance. In science, we have professional journals and things like that that capture discoveries within individual fields, but these larger-scale projects — where science, economics, industry, and society all intersect — don’t really have an academic thread that connects them.

I wanted to write a book that condensed all of those connections, because the innovation process at that scale is really the intersection of many different fields. The dominant ones are science and economics, because those are the underlying principles that drive how innovation happens.

So I was interested in marking this moment in history and documenting the experiments we’ve done at scale — trying to understand how knowledge of the innovation process can be incorporated into large collaborative research programs.

What started as a practical effort to make these programs work became a broader and somewhat unexpected interest in the innovation process itself.

Q: What is the “invisible engine?” 

A: The invisible engine is this decentralized collective intelligence of different actors, which are people and companies that eventually create surprise in the marketplace, which brings great profit.

This concept of “surprise” comes from Frank Knight, an economist from the early 1900s who was trying to understand the Industrial Revolution happening around him. So he takes a close look at the entrepreneur and asks, “What does the entrepreneur do?” And his answer is that the entrepreneur takes on uncertainty. They bring something into the world without knowing exactly what will happen, and their reward is surprise — everyone is surprised that people want it and that it can be done. Because the entrepreneur is the first to discover that opportunity, they can earn a profit.

Q: How did your experience developing semiconductor technologies shape the ideas in the book? 

A: It started with Bell Labs. My colleague and I made an important discovery — we found a way of straining silicon in a thin-film form with very few defects, which had never been done before. From the physics point of view, it was a big result. But I was always interested in having impact in the world, not just scientific recognition, so I went to my manager and asked, “What do we do next?”

He said, “Go talk to the marketing people at AT&T.” In hindsight, that made perfect sense. Bell Labs, like a lot of great industrial labs, created a lot of stuff, but they couldn’t always commercialize it.

Then I came to MIT, which was an open aperture after Bell Labs. Here I could keep uncertainty open across all the elements and find convergence in different directions. Eventually I started a company, and going between institutions to stimulate things was an eye-opening experience. We eventually reached a settlement with Intel over a patent dispute because the industry discovered that strained silicon was needed to extend Moore’s Law — something we never expected.

A lot of people want things to be organized and say, “Oh yeah, look at all that chaos.” But no — the path from Bell Labs to MIT to a startup, and then to industry adoption, was the innovation process.

Q: What’s the biggest misconception about innovation? 

A: People think that all research investment works the same way if the goal is economic impact. But there are actually three different kinds of research investment, and they’re meant for different things.

There’s the one we all know about, which I call “altruistic science.” The purpose of altruistic science — in investing in an academic institution — is to produce educated people. It’s not done in the context of the world that ideas eventually have to succeed in. And if you honestly look at the direct economic yield over all these years, it’s basically zero.

Strategic research is the second investment category. As opposed to a single area of technology or science, it’s organized around a goal. A new F-35, for example, may need advancements in several fields, so the customer — in this case the government — wants them to come together. Basically, they’re taking economics out of the equation because they’re the only customer, but they have much broader uncertainty because they have multiple domains of technology that they have to deal with.

The third category is what I call “fundamental innovation.” It’s meant to represent the whole process from research to economic growth, even if it’s on 10-, 15-, or 20-year time horizons. Fundamental innovation is different because it has three variables: technology — what is physically possible; implementation — how it can be built and delivered; and market — who will adopt it, and why. Fundamental innovation involves all the necessary elements the whole time to converge on possible value. So you’re thinking about market applications the whole time, you’re thinking about new science and technology that could create new innovation options. Then you’re working in the real world saying, “OK, here’s how implementation would happen today, but maybe this could change, maybe that could change.” Not only are you doing your research, but the world is changing at the same time.

So that’s really the biggest misconception — that innovation is about an idea. It isn’t. It’s a process of working with things in the world until they become valuable.

Q: Who did you write the book for? 

A: I wrote it for multiple audiences: individual innovators and students; researchers and faculty; corporate leaders; research funders; and policy-makers. So, people who have a stake in trying to figure out, either with their careers or with their investments — whether it’s government or private — how to invest in the far future.

Q: What’s one lesson you hope readers take away?

A: For the policy people, I would say: Understand how innovation works in the economy, stop getting in its way, come up with new methods to drive it more efficiently, and realize there are three different streams of investment — altruistic, strategic, and this fundamental innovation stream that is not purposely being funded.

For students, I think understanding this is how you can actually have impact. What I point out in the book is that being involved in the innovation process makes you T-shaped: You have technical depth in one area and a broad working knowledge of many areas. If you’re doing research under these conditions, you start to learn about the world and all these different dimensions. It inherently includes business, economics, and applications. You’ve become broader, but then you still have the technical depth to drill down into any area.

For universities, this is who we should be. We should be teaching people how to do this and how to participate in these research corporations that I’m talking about. I call them third places: places that bring everybody together for this purpose — for investment, for everything else. Universities are the ones that can really trigger that, because companies aren’t going to have enough time. The government and universities should be targeting these third places for innovation, and students and faculty will be able to become more T-shaped through that interaction.

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Mathematical framework connects biological principles to manufacturable, adaptive materials

The scales of a pine cone open in low humidity to scatter seeds, but close in damp conditions to protect seeds from moisture. An artificial material with the same behavior could be useful in applications like moisture-responsive shingles for passive cooling.

MIT researchers have now developed a system that simplifies the process of designing this type of bioinspired material. 

Their framework captures how mechanisms across length scales in a natural system, like the cells, fibers, and tissues inside a pine cone, work together to achieve unique properties. It then formally translates that behavior in an engineered system. 

The framework organizes biological behavior into building blocks that can be used to design synthetic structures that can be mathematically validated to perform the same way, and fabricated using a 3D printer. 

By taking much of the guesswork out of this design process, the framework could help engineers more readily create new adaptive materials while cutting development time and eliminating costs from failed prototypes. This framework could one day be used to design soft robotic grippers that respond automatically to their environment without any complex electronics, or morphing structures for airplane wings that predictably change their shape in response to temperature shifts.

“I’ve always been fascinated with natural materials and how complex behavior emerges from very simple building blocks,” says Lee Marom, an MIT graduate student in the departments of Mechanical Engineering and Architecture and lead author of a paper on this framework. “What really excites me about this work is going beyond bio-inspiration to what we could call ‘bio-derivation,’ where we move past observing a unique behavior to capturing the relationships and mechanisms that are actually producing that behavior, and then finding a systematic way to translate them into an engineered system.”

Marom is joined on the paper by corresponding author Markus Buehler, the Jerry McAfee Professor of Engineering in the departments of Civil and Environmental Engineering and Mechanical Engineering; Gioele Zardini, the Rudge and Nancy Allen Assistant Professor of Civil and Environmental Engineering, a principal investigator in the Laboratory for Information and Decision Systems, and an affiliate faculty with the Institute for Data, Systems, and Society; and Skylar Tibbits, an associate professor in the Department of Architecture. The research appears in the Journal of the Mechanics and Physics of Solids.

Biological building blocks

Pine cones can open and close their scales in response to humidity because of complex interactions within the organism’s structure. 

Shifts in humidity cause changes in microscopic cellulose fibers, which then cause transformations in larger groupings of fibers called laminas, which impact tissue layers, and so on, all the way up to the pinecone we see hanging from a tree branch.

“We instantiated the framework on the pine cone because it gives us a relatively simple, well-understood mechanism to demonstrate how the framework works. But its value becomes even greater as we apply it to more complex systems,” Marom says.

For engineers, the challenge is not necessarily reproducing an individual behavior, but translating the mechanisms and relationships that produce it across length scales. Without an explicit framework, these relationships need to be reformulated for each new system. 

To streamline the material design process, MIT researchers created a mathematical framework that captures how the components at each scale in a natural object work together to exhibit a certain behavior. The framework carries the design all the way to fabrication, translating the engineered behavior into verified manufacturing specifications and executable code that is used to 3D-print the object.

“What we were missing was a way to connect the mathematical description of a natural system all the way to its physical realization. The goal of this framework is to make that entire chain explicit so we can reason about what has to be preserved at each step,” Marom says.

The framework utilizes tools from category theory, which is a systematic method to compose larger systems from smaller ones in a way that is guaranteed to succeed.

Using category theory, the system maps out how a stimulus, such as humidity, causes a response at each level of the biological hierarchy within an organism like a pine cone. It models each level of the biological hierarchy as a separate building block that is independently validated.

Then the framework constructs a larger system from these building blocks by employing mathematical rules to ensure there is a valid transition between each step in the hierarchy. 

It assigns each building block in the natural system to a synthetic counterpart. In this way, the engineered material preserves the stimulus-response interactions that cause the natural organism’s unique behavior.

The work extends a research program in Buehler’s laboratory spanning more than a decade. 

Earlier studies used category theory to describe hierarchical materials and determine when building blocks could be replaced while preserving higher-level function. In subsequent work, Buehler and colleagues introduced “categorical prototyping,” using the same mathematics to preserve selected molecular-scale mechanics when translating computational models into large-scale 3D-printed prototypes. 

The new framework takes the next step by closing the entire chain, from multiscale biological mechanics, through an engineered realization and fabrication specification, to an experimentally validated, machine-executable design.

“Biological materials derive their extraordinary functionality from relationships that span scales, from molecular and fiber-level mechanisms to whole structures. Category theory gives us a way to make those relationships explicit and transferable. Once that design logic is captured mathematically, nature becomes a library of composable mechanisms that can be translated, recombined, and realized in new material systems,” Buehler says.

Compositional structure 

“Once we know that the relationships we mapped are valid, we can start recombining them in new ways. That means the framework isn’t only describing existing systems, it can also help us reason about ones we haven’t built before,” Marom explains.

For instance, the engineers mapped the humidity-driven bending behavior in a pine cone and the humidity-driven twisting behavior of a wheat awn as separate sets of building blocks. 

Then they combined some building blocks from each to design and fabricate a new type of actuator that exhibits thermal twisting behavior, without the need to do any new design work. When tested, the twisting actuator performed as the researchers expected.

In the future, engineers could use this framework to reliably combine verified components into new, bio-inspired designs for adaptive materials in applications like robotics, biomedical devices, or wearable technology.

“The systematization of our framework allows you to reuse pieces without needing to start from scratch each time, saving a huge amount of computation. That’s the real-world payoff,” Zardini says.

Now that the researchers have laid the groundwork with this mathematical framework, they can apply it to objects with more complex mechanics. They also plan to incorporate artificial intelligence models into their pipeline to expedite the discovery of new adaptive materials. 

“We have shown that the boundaries between disciplines do not matter as much as we think they do. Some of the principles from category theory can be used to guide and empower materials design. These mathematical structures seem to really have no boundaries,” Zardini says.

“The larger vision is physical AI: intelligence that can reason in terms of physical mechanisms and then turn those ideas into matter. Here we are beginning to build the infrastructure for that — composable physical knowledge, mathematical rules for determining what can be combined, and a path from a new design concept all the way to machine instructions and fabrication. Ultimately, this could allow AI not only to discover new materials and mechanisms, but to physically realize and test what it discovers,” Buehler says.  

This research was supported, in part, by the MIT Lemelson Engineering Fellowship, Singapore DSO National Laboratories, and the MIT Generative AI Impact Consortium.

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MIT engineers connect bacteria to create living transistors

MIT researchers have engineered bacteria that can function as transistors, allowing the team to create living “circuit boards” that can be printed onto a growth medium in a Petri dish. 

In electrical circuits, transistors function as switches that can turn current on or off. In the biological circuits that the researchers have created, bacterial switches control the flow of small molecules, which send signals to downstream circuit components. 

The research team designed two different transistors, along with three bacterial strains that relay information between the transistors, giving them the building blocks they need to design nearly any type of circuit. In a new study, they used these cells to create circuits that can add two or three inputs, or send one input to a specific location in the circuit.

“We’ve built some initial computer architecture components that are commonly used, but any operation can be built with these five strains,” says Hamid Doosthosseini PhD ’25, an MIT postdoc and the lead author of the new study. 

Using this approach, the researchers hope to develop circuits that one day could coat plant leaves or roots, where they could compute to sense and respond to environmental conditions such as drought or attack by pests. 

Christopher Voigt, head of MIT’s Department of Biological Engineering, is the senior author of the paper, which was recently published in Nature Chemical Biology. Former MIT postdoc Haorong Chen is also an author of the paper.

Cells as transistors

When designing synthetic biology circuits, researchers typically engineer cells to express proteins and transcription factors that interact to perform a task such as sensing a target molecule, which then triggers production of a specific output.

These simple circuits can perform various logic functions, but they must use unique transcription factors to avoid crosstalk within the circuit. There is a limited number of transcription factors that can be used for these circuits, which limits the overall complexity that can be achieved in a single cell. Additionally, putting too many circuits in one cell can overburden the cell’s protein production machinery.

In the new paper, the researchers took a different approach: Instead of building an entire circuit into one cell, they designed cells that could act as transistors. These transistors can then be combined in different ways to create a variety of circuits.

To create the transistors, the researchers chose a bacterium called Pantoea agglomerans, which commonly grows on surfaces, including plants. Using these cells, they made two types of transistors that can be switched on or off by a molecule called OC-6. One of the transistors is switched on by this input, and the other is switched off. Each transistor also detects the presence of a target molecule, in this case, OC-12. Depending on whether that molecule is present, and whether the switch is active, the transistors produce an output molecule known as OHC-14.

The researchers also used three strains of Pantoea agglomerans to create relays, which translate the OHC-14 signal into an output that can be fed into another transistor. Using these relay strains, the researchers can “wire” the transistors together, just like an electronic circuit board.

For example, they could create a bidirectional switch with two transistors that sense OC-12, and then send that information to different relay strains based on a switch input, ultimately feeding into other transistors that further process the signal.

The researchers created their circuits by printing colonies of bacteria onto plates containing agar, a growth medium. Each colony is printed about 5 millimeters from the nearest one. This allows the signals to travel only to the nearest colony, which then relays them to the next one, so information flows only in one direction.

Complex calculations

In this paper, the researchers demonstrated a transistor that can perform several types of logic operations depending on its location in the circuit layout, including “multi-input,” “or,” and “imply” gates. They also combined the transistors to create more complex circuits that can add up two signals, process more signals simultaneously, or function as a demultiplexer — a circuit that takes one incoming signal and sends it to one of several possible destinations, depending on a control signal.

The largest of these circuits, which adds two inputs together, contains 24 bacterial colonies wired together.

“This work shows that we can get toward more complicated functions by linking up simpler functions in individual cells,” Voigt says. “Computationally, there’s nothing that your iPhone can do that these circuits couldn’t do.” 

Circuits made from these cells take about eight hours to perform each calculation, much longer than a computer circuit. But, for biological applications, that is a reasonable amount of time, the researchers say. 

“We’re not trying to replace computers, but rather put computational control into biology. If you have bacteria on the root of a plant, or the plant itself is doing the computing, running a simple calculation overnight is fast enough relative to a growth season,” Voigt says.

If developed for use in agriculture, this type of circuit could be applied to the roots of plants to detect different types of stress. Once a particular input is detected, it would trigger a response such as synthesizing a fungicide.

The research was funded, in part, by the U.S. Defense Advanced Research Projects Agency and by the U.S. Intelligence Advanced Research Projects Activity.

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Flexible brain circuits can switch between different tasks

As we move through everyday life, our brains engage in a huge variety of cognitive tasks. For example, during a grocery run, we might have to recall the items for a recipe, remember where the clerk said the flour was located, and count out money to pay.  

Scientists have long theorized that the brain contains modules, or clusters of neurons, that perform the same computation across many different types of tasks. This type of modularity could help explain why our brains are able to take on so many functions, with little difficulty.

In a new study of mice, MIT neuroscientists have found the first evidence for the existence of these flexible modules. They identified neurons in the prefrontal cortex that can be used to store either a sensory input or an action plan in working memory.

“We found that the brain doesn’t dedicate a separate group of neurons for every type of information. Instead, it uses the same populations of neurons to perform the same computation on different kinds of information, which means the same subset of neurons can hold both an action and a sensory stimulus in working memory,” says Yuma Osako, an MIT postdoc and the lead author of the new study.

The discovery supports the theory that reusable circuits allow the brain to mix and match components to generate a rich variety of behavior, the researchers say. 

Mriganka Sur, the Newton Professor of Neuroscience at MIT’s Picower Institute for Learning and Memory, and Timothy Buschman PhD ’08, a professor at the Princeton Neuroscience Institute, are the senior authors of the paper, which appears today in Nature Neuroscience. MIT graduate student Greggory Heller and postdoc Sofie Ahrlund-Richter are also authors of the study.

Cognitive building blocks

Dating back to his time as a graduate student at MIT, Buschman has been interested in understanding how the brain is able to perform so many different kinds of behavior. 

“One of the solutions that’s always been proposed has been this idea of compositionality — that you can take pieces of cognition that perform part of a task and reuse them in another task,” he says. 

In a study published last year, Buschman’s lab at Princeton showed that when animals perform a task such as categorizing objects based on their shape or color, they assemble neural circuits that perform different pieces of the task. Just like “cognitive Legos,” these building blocks can be flexibly combined to generate new behaviors.

Osako, who joined Sur’s lab several years ago, was also interested in studying cognitive flexibility. He and Sur teamed up with Buschman to explore a related question: whether individual neural circuits can be repurposed to perform different functions. 

“Our everyday life requires us to temporarily hold many different kinds of information. One big question is how the brain can represent an unlimited variability of information using only a finite number of neurons,” Osako says.

To get at that question, the researchers trained mice on a task in which they have to determine whether two sensory stimuli (high or low pitched tones) are the same, and respond accordingly. 

The researchers recorded electrical impulses from the brain while the mice performed this task, focusing on the prefrontal cortex, which is involved in executive functions such as planning and decision-making, and the parietal cortex, which processes sensory information and plans movement.

After measuring electrical activity from thousands of neurons, the researchers performed computational analyses that allowed them to identify groups of neurons that encode specific pieces of information.

They focused on two time periods — the time between the first and second tone, when the animals are holding a memory of the first tone, and the time between the second tone and the point where they have to decide on an action. During that second period, the animals are holding their decision and action plan in their working memory.

Within the parietal cortex, the researchers found that neurons appeared to exclusively store memory of the tone. But in the prefrontal cortex, they identified a cluster of neurons that could switch between the two types of memory. During the first period, they stored a memory of the first tone, but during the second, they were responsible for remembering the plan of action.

Re-using these clusters for different purposes allows the animals to flexibly store different types of information, the researchers say.

“When mice do tasks that test whether memory computations can be reused, the answer is they are. There are subspaces of functional activity in the prefrontal cortex that can be the substrate of mixing and matching toward flexible cognition,” Sur says.

Computational flexibility

The new findings offer support for the idea that the same computational circuits can be used for different purposes, Buschman says.

“The main result from this study is that there’s a circuit in the brain that maintains items in working memory, and you can put either sensory or motor information into it, and flexibly reuse it depending on what your current task is,” he says. “This means you do not have to build an entire new circuit for holding information in mind every time you want to learn a new task.”

The researchers now plan to study whether inhibiting these modules during different parts of the task affects the animals’ behavior, which could offer additional evidence that the flexible modules they identified participate in a variety of functions.

The research was funded by the National Institutes of Health, a MURI Grant, the Picower Institute Innovation Fund, the Japan Society for the Promotion of Science Overseas Research Fellowships, and the Uehara Memorial Foundation Postdoctoral Fellowship. 

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Professor Emeritus Chiang Chung Mei, pioneering scholar of ocean wave dynamics and fluid mechanics, dies at 91

Chiang Chung "C.C." Mei, professor emeritus in the MIT Department of Civil and Environmental Engineering (CEE), a renowned hydrodynamicist whose work shaped the field’s understanding of ocean waves and their interactions with coastal and offshore structures, passed away peacefully at home in Waltham, Massachusetts, on July 16. He was 91. 

For more than four decades, Mei was a defining presence in CEE. Since joining the MIT faculty as an associate professor in 1965, he became one of the world's foremost authorities on theoretical hydrodynamics, fluid mechanics, and ocean and coastal wave phenomena, retiring in 2010 after 45 years on the faculty. Throughout his career, he earned a reputation among colleagues and students as a generous mentor and thoughtful leader.

An elegant, rigorous scholar

Mei's research advanced the science of ocean wave hydrodynamics, spanning nearly every aspect, including nearshore currents, sediment transport and resuspension, the formation of sand ripples and bars on beaches, wave-induced stresses and seabed deformation, and the removal of contaminants from soils. In later years, he extended his mathematical approach to biofluid dynamics, publishing on flow problems in blood vessels and the inner ear, including a paper on "Streaming and diffusion in the cochlea" that appeared in the Journal of Fluid Mechanics in July 2025.

He authored over 300 publications, was cited more than 14,000 times, and coauthored the landmark books "Theory and Applications of Ocean Surface Waves" and "Homogenization Methods for Multiscale Mechanics." Mei published decades of influential research on wave power extraction, harbor oscillations, waves over muddy seabeds, coastal vegetation, landslide-generated waves, tsunamis, and hydrodynamic resonance. His work combined mathematical elegance with practical engineering application and continues to guide solutions to some of the world's most complex ocean-based environmental challenges, including coastal defenses against storms and oil spill response strategies.

"C.C. tackled deep, diverse, difficult, and important fluid dynamics questions with utmost finesse and elegance," says Lydia Bourouiba, the Japan Steel Industry Professor. "He was an inspiring scholar, an intellectual leader, and a wonderful mentor, whose rigor set the standard we should continue to uphold. We lost a true giant in our field."

His excellence and leadership in research and teaching earned numerous prestigious recognitions, including a Guggenheim Fellowship in 1972, election to the National Academy of Engineering in 1986, fellowship in the American Physical Society, the Theodore von Kármán Medal in 2007, and appointment as a Ford Professor of Engineering at MIT. 

Beyond his scientific achievements, Mei devoted himself to the MIT community, serving as interim head of CEE from 2001 to 2002 and helping guide the department through a period of transition with the same humility and steadiness that characterized his scholarly contributions. In 2015, the department established the C.C. Mei Distinguished Speaker Series in his honor — an idea that grew out of the initiative of Bourouiba to revive CEE's environmental seminar series and honor its strong historical legacy in fluid dynamics. "Discussing the idea with C.C., he thought it was an excellent idea and was so generously supportive. He embodied the excellence I wanted the new series to reflect; naturally, we named it in his honor," she says. The series continues to bring internationally renowned scholars to MIT. 

A mentor whose students became family

Mei's influence was equally profound in the lives of his students. Over more than 45 years at MIT, Mei advised and mentored generations of engineers, many of whom became leaders in academia, industry, and government. Even in his final days, those relationships endured. 

One of his first doctoral students, Professor Emertius Ole Madsen, visited him just hours before his passing. Former student Yile Li SM '01, PhD '06, who continued collaborating with Mei on biofluid dynamics research in his later years, remained in close conversation with him throughout his final days. Li recalls the highlight of his discussions with Mei. "He told me there are three stages of doing research: solving problems using mathematical methods, modeling problems by capturing core physics, and ultimately discovering entirely new problems," Li says. "His own work proved he was a master of all three."

For Mei's family, MIT was never simply his workplace. "CEE was truly the center of my father's life," says his daughter Deborah Mei. "For more than 50 years, it shaped not just his career, but our whole family's world. His students, colleagues, and collaborators weren't separate from our home life — they were part of it, for as long as I can remember."

Colleagues consistently remember not only Mei's intellectual brilliance, but also his extraordinary generosity. Mei was known for the warmth he extended to junior colleagues finding their footing at MIT. 

"He was so respectful and kind to me when I was hired in 1976, and feeling like a fish out of water," says Institute Professor Sallie “Penny” Chisholm. "I will never forget that. A great gentleman, indeed." 

Heidi Nepf, the Donald and Martha Harleman Professor, recalls Mei as "an exceptional scholar and a wonderful colleague."

Rafael L. Bras, professor emeritus, remembers Mei as the model of the gentleman scholar. "He cared deeply about people, loved his profession, and touched countless lives, both directly and indirectly. Everybody loved him."

A full and joyful life

Mei was born on April 4, 1935, in Wuchang, Hubei Province, China, the only son and first child of Ju-Long Mei and Wu Yu-Ling. He earned his BS from National Taiwan University in 1955, his MS from Stanford University in 1958, and his PhD from Caltech in 1963.

Those who knew him describe a man who was passionate, playful, endlessly curious, and quick with both words and affection. The home he shared with his wife, Caroline, became a gathering place for generations of the Mei family, his MIT colleagues and students alike with animated conversation, humor, and his familiar loving banter with his wife and siblings, as those close to him remember it.

To generations of students and colleagues, Mei was known as much for his patience, kindness, intellectual curiosity, and quiet encouragement as for his scientific accomplishments. He was always willing to discuss an idea, help a student work through a difficult problem, or offer thoughtful guidance to a young colleague beginning an academic career. His legacy lives on not only in the theories that continue to shape coastal and ocean engineering, but also in the worldwide community of scholars he mentored, inspired, and welcomed over more than half a century at MIT.

Mei is survived by his wife, Caroline (Schmitt) Mei of Waltham; his daughter, Deborah Yupin Mei, and her husband, Juan Ignacio Garcia De Motiloa Ubis of Singapore; his sisters Helen Chiang-Hua Mei Chao of Potomac, Maryland; Teresa Chiang-Ming Mei Wu of Bethesda, Maryland; Heidi Chiang-Kuo Mei Hsia and her husband, Jack, of Potomac, Maryland; and Christine Chiang Ying Mei and her husband, Paul Tung, of Rancho Palos Verdes, California; his grandchildren, Juan Ignacio Jr. and Lauren; and many nieces, nephews, grand-nieces, and grand-nephews.

Gifts may be made in Mei's memory to the Chiang and Caroline Mei Fund 

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Drug that targets an inflammatory enzyme could help prevent lung cancer

Every year, lung cancer kills more than 100,000 people in the United States. Smoking is the leading risk factor for lung cancer, but other environmental exposures can also contribute to the disease.

In an advance that could help prevent some of those lung cancer deaths, MIT researchers have shown that blocking an enzyme involved in lung inflammation appears to reduce the risk of developing tumors. 

The researchers found that this enzyme, caspase-1, is active in developing tumors in mice. When they treated the mice with a small-molecule drug that inhibits caspase-1, the mice were much less likely to develop lung tumors.

That drug has already gone into clinical trials for other diseases, and the researchers now hope to test it as a preventative drug in people with elevated risk for lung cancer. 

“If you look at global cancer deaths, lung cancer causes most of them, and most of that is driven by tobacco smoking. Additionally, people who are ‘never smokers’ are showing up with lung cancer. You can imagine a future where you get a test and if you’re deemed high-risk, you go on a preventative medicine. This concept is called cancer interception, and it could help millions of people,” says Sangeeta Bhatia, the John and Dorothy Wilson Professor of Health Sciences and Technology and of Electrical Engineering and Computer Science at MIT, and a member of MIT’s Koch Institute for Integrative Cancer Research and the Institute for Medical Engineering and Science (IMES).

Bhatia is the senior author of the new study, which appears today in Science Advances. Cathy Wang PhD ’26 is the lead author of the paper. 

Blocking inflammation

Preventing lung cancer in patients who are at high risk could significantly reduce the death toll of the disease. In 2017, a clinical trial run by Novartis yielded a tantalizing hint that targeting lung inflammation could prevent some lung cancer cases. That trial, known as CANTOS, was designed to examine whether an anti-inflammatory drug — an antibody that blocks the cytokine IL-1 beta — could reduce the risk of strokes and heart attacks. Unexpectedly, the researchers found that this treatment led to lower rates of lung cancer in a subset of people.

Later trials showed that the antibody had little effect in patients who had established lung cancer, but researchers are still exploring the possibility of using it to prevent progression of lung cancer in high-risk patients. A recent study by the Swanton lab at the Francis Crick Institute identified a set of proteins, across several biological pathways and cell types, that could be used to predict which patients would respond to treatment with an IL-1 beta antibody. 

IL-1 beta requires protease cleavage to be converted to its mature, active form. Thus, Bhatia and her team wondered if enzymes called proteases, which cleave other proteins, might be involved in driving the inflammatory pathway that includes IL-1 beta.

For several years, Bhatia’s lab has been developing tools to track and visualize proteases, since the activity of these enzymes can contribute to cancer development. Proteases can help tumor cells escape their original locations by cutting through proteins of the extracellular matrix, and they also play essential roles in guiding inflammatory cell migration, which can influence tumor growth and immune system targeting.

By coming up with ways to detect these enzymes, Bhatia’s lab has created diagnostic nanosensors for cancer and other diseases. The sensors consist of nanoparticles decorated with peptides that can be cleaved by certain proteases, revealing when proteases are active in a particular tissue or disease state.

In addition to their role in cancer, proteases are known to be involved in the regulation of inflammation. In their new study, Bhatia and her colleagues adapted their nanosensors to identify proteases that may participate in IL-1 beta-mediated inflammatory pathways. 

“We know that proteases are very important in inflammation, and we wanted to pinpoint which ones might be the most active during early lung cancer development,” Wang says.

For this study, the researchers used a mouse model developed by Tyler Jacks, the David H. Koch Professor of Biology at MIT and a member of the Koch Institute. This model, known as KPS, is engineered to turn on cancer-causing mutations in the p53 and Kras genes. The mice also express a peptide called SIINFEKL, which helps to activate T cells and stimulate inflammation in the lung.

The researchers designed their experiments to allow them to model increased cancer risk, beginning before tumor formation was detectable. Five weeks after they induced the cancer-causing mutations, the researchers injected some of the mice with an antibody that blocks IL-1 beta, while others were untreated. Three weeks later, the researchers used their nanosensors to detect proteases that were active in the lungs. 

Those experiments showed that in untreated mice, which all developed lung tumors, caspase-1 was very active. However, in the treated mice, which had fewer tumors, caspase-1 activity was significantly reduced. The researchers also found that in untreated mice, the active caspase-1 was found primarily in lung tumors, not in nearby healthy tissue.

Working with Lecia Sequist, a professor of medicine at Havard Medical School and physician at Mass General Brigham, the researchers also analyzed a small number of human lung fluid samples. In these samples, they also found higher levels of caspase-1 activity from patients with lung cancer compared to healthy donors, despite a common smoking history.

A repurposed drug

The observation that caspase-1 activity is interrelated with the IL-1 beta inflammation pathway was not completely surprising, given IL-1 beta itself required protease cleavage to be converted to its mature, active form. The MIT team then investigated whether inhibitors of caspase-1 might also provide the same protective effects as inhibitors of IL-1 beta, or even improve them. 

Before tumors developed, the researchers began treating the at-risk KPS mice with either a caspase-1 inhibitor, an IL-1 beta antibody, or both. In mice that received both drugs, nearly 20 percent never developed tumors at all. In the mice that received either the caspase-1 inhibitor or the IL-1 beta antibody alone, tumors were much smaller and less numerous than in untreated mice.

Unlike antibodies, which need to be given intravenously, caspase-1 inhibitors can be taken orally, which could make them more appealing as a preventative treatment. Another opportunity provided by these drugs is that they have previously been tested in clinical trials for treatment of rheumatoid arthritis and other diseases.

“What’s so attractive about using this caspase-1 inhibitor is that it has actually been tested in humans. It’s already been through safety studies, and we think it could potentially be repurposed for cancer prevention,” Bhatia says.

The researchers hope to test the drug in a clinical trial, potentially using the biomarkers that were identified by the Swanton team to identify subjects who are likely responsive to IL-1 beta antibody treatment. 

The authors of the study also include MIT researchers Qian Zhong, Shih-Ting Wang, Carmen Martin-Alonso, Sofia Neaher, Sahil Patel, Tiziana Parisi, Jesse Kirkpatrick, and Tyler Jacks. 

The study was funded by Johnson & Johnson, Upstage Lung Cancer through the Koch Institute Frontier Research Program, the Virginia and D.K. Ludwig Fund for Cancer Research, the Koch Institute’s Marble Center for Cancer Nanomedicine, the Koch Institute Support (core) Grant from the National Cancer Institute, and a core center grant from the National Institute of Environmental Health Sciences. 

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