(This appeared as an op-ed published February 12, 2026 in the San Jose Spotlight, written by Huy Tran (SIREN), Jeffrey Wang (CAIR-SFBA), and Jennifer Pinsof.)

As ICE and other federal agencies continue their assault on civil liberties, local leaders are stepping up to protect their communities. This includes pushing back against automated license plate readers, or ALPRs, which are tools of mass surveillance that can be weaponized against immigrants, political dissidents and other targets.

In recent weeks, Mountain View, Los Altos Hills, Santa Cruz, East Palo Alto and Santa Clara County have begun reconsidering their ALPR programs. San Jose should join them. This dangerous technology poses an unacceptable risk to the safety of immigrants and other vulnerable populations.

ALPRs are marketed to promote public safety. But their utility is debatable and they come with significant drawbacks. They don’t just track “criminals.” They track everyone, all the time. Your vehicle’s movements can reveal where you work, worship and obtain medical care. ALPR vendors like Flock Safety put the location information of millions of drivers into databases, allowing anyone with access to instantly reconstruct the public’s movements.

But “anyone with access” is far broader than just local police. Some California law enforcement agencies have used ALPR networks to run searches related to immigration enforcement. In other situations, purported issues with the system’s software have enabled federal agencies to directly access California ALPR data. This is despite the promises of ALPR vendors and clear legal prohibitions.

Communities are saying enough is enough. Just last week, police in Mountain View decided to turn off all of the city’s Flock cameras, following revelations that federal and other unauthorized agencies had accessed their network. The cameras will remain inactive until the City Council provides further direction.

Other localities have shut off the cameras for good. In January, Los Altos Hills terminated its contract with Flock following concerns about ICE. Santa Cruz severed relations with Flock, citing rising tensions with ICE. Most recently, East Palo Alto and Santa Clara County are reconsidering whether to continue their relationships with Flock, given heightened concern for the safety of immigrant communities.

California law prohibits local police from disclosing ALPR data to out-of-state or federal agencies. But at least 75 California police agencies were sharing these records out-of-state as recently as 2023. Just last year, San Francisco police allowed access to out-of-state agencies and 19 searches were related to ICE.

Even without direct access, ICE can exploit local ALPR systems. One investigation found more than 4,000 cases where police had made searches on behalf of federal law enforcement, including for immigration investigations.

Increasing the risk is that law enforcement routinely searches these networks without first obtaining a warrant. In San Jose, police aren’t required to have any suspicion of wrongdoing before searching ALPR databases, which contain a year’s worth of data representing hundreds of millions of records. In a little over a year, San Jose police logged more than 261,000 ALPR searches, or nearly 700 searches a day, all without a warrant.

Two nonprofit organizations, SIREN and CAIR California, represented by Electronic Frontier Foundation and the ACLU of Northern California, are currently suing to stop San Jose’s warrantless searches of ALPR data. But this is only the first step. A better solution is to simply turn these cameras off.

San Jose cannot afford delay. Each day these cameras remain active, they collect sensitive location data that can be misused to target immigrant families and violate fundamental freedoms. It is a risk materializing across California. City leaders must act now to shut down ALPR systems and make clear that public safety will not come at the expense of privacy, human dignity or community trust.

Related Cases: SIREN and CAIR-CA v. San Jose

Report from EFF, Center for Just Journalism, and IPVM Helps Cut Through Sales Hype

SAN FRANCISCO — A new report released today offers journalists tips on cutting through the sales hype about police surveillance technology and report accurately on costs, benefits, privacy, and accountability as these invasive and often ineffective tools come to communities across the nation. 

The “Selling Safety” report is a joint project of the Electronic Frontier Foundation (EFF), the Center for Just Journalism (CJJ), and IPVM. 

Police technology is often sold as a silver bullet: a way to modernize departments, make communities safer, and eliminate human bias from policing with algorithmic objectivity. Behind the slick marketing is a sprawling, under-scrutinized industry that relies on manufacturing the appearance of effectiveness, not measuring it. The cost of blindly deferring to advertising can be high in tax dollars, privacy, and civil liberties. 

“Selling Safety” helps journalists see through the spin. It breaks down how policing technology companies market their tools, and how those sales claims — which are often misleading — get recycled into media coverage. It offers tools for asking better questions, understanding incentives, and finding local accountability stories. 

“The industry that provides technology to law enforcement is one of the most unregulated, unexamined, and consequential in the United States,” said EFF Senior Policy Analyst Matthew Guariglia. “Most Americans would rightfully be horrified to know how many decisions about policing are made: not by public employees, but by multi-billion-dollar surveillance tech companies who have an insatiable profit motive to market their technology as the silver bullet that will stop crime. Lawmakers often are too eager to seem ‘tough on crime’ and journalists too often see an easy story in publishing law enforcement press releases about new technology. This report offers a glimpse into how the police-tech sausage gets made so reporters and lawmakers can recognize the tactics of glossy marketing pitches, manufactured effectiveness numbers, and chumminess between companies and police.” 

“Surveillance and other police technologies are spreading faster than public understanding or oversight, leaving journalists to do critical accountability work in real time. We hope this report helps make that work easier,” said Hannah Riley Fernandez, CJJ’s Director of Programming. 

"The surveillance technology industry has a documented pattern of making unsubstantiated claims about technology,” said Conor Healy, IPVM's Director of Government Research. “Marketing is not a substitute for evidence. Journalists who go beyond press releases to critically examine vendor claims will often find solutions are not as magical as they may seem. In doing so, they perform essential accountability work that protects both taxpayer dollars and civil liberties." 

EFF also maintains resources for understanding various police technologies and mapping those technologies in communities across the United States. 

For the “Selling Safety” report:  https://www.eff.org/document/selling-safety-journalists-guide-covering-police-technology

For EFF’s Street-Level Surveillance hub: https://sls.eff.org/ 

For EFF’s Atlas of Surveillance: https://www.atlasofsurveillance.org/ 

Contact:  BerylLiptonSenior Investigative Researcherberyl@eff.org

Seven MIT researchers are among the 130 new members and 28 international members recently elected to the National Academy of Engineering (NAE) for 2026. Twelve additional MIT alumni were also elected as new members.

One of the highest professional distinctions for engineers, membership in the NAE is given to individuals who have made outstanding contributions to “engineering research, practice, or education,” and to “the pioneering of new and developing fields of technology, making major advancements in traditional fields of engineering, or developing/implementing innovative approaches to engineering education.”

The seven MIT electees this year include:

Moungi Gabriel Bawendi, the Lester Wolfe Professor of Chemistry in the Department of Chemistry, was honored for the synthesis and characterization of semiconductor quantum dots and their applications in displays, photovoltaics, and biology.

Charles Harvey, a professor in the Department of Civil and Environmental Engineering, was honored for contributions to hydrogeology regarding groundwater arsenic contamination, transport, and consequences.

Piotr Indyk, the Thomas D. and Virginia W. Cabot Professor in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory, was honored for contributions to approximate nearest neighbor search, streaming, and sketching algorithms for massive data processing.

John Henry Lienhard, the Abdul Latif Jameel Professor of Water and Mechanical Engineering in the Department of Mechanical Engineering, was honored for advances and technological innovations in desalination.

Ram Sasisekharan, the Alfred H. Caspary Professor of Biological Physics and Physics in the Department of Biological Engineering, was honored for discovering the U.S. heparin contaminant in 2008 and creating clinical antibodies for Zika, dengue, SARS-CoV-2, and other diseases.

Frances Ross, the TDK Professor in the Department of Materials Science and Engineering, was honored for ultra-high vacuum and liquid-cell transmission electron microscopies and their worldwide adoptions for materials research and semiconductor technology development.

Zoltán Sandor Spakovszky SM ’99, PhD ’01, the T. Wilson (1953) Professor in Aeronautics in the Department of Aeronautics and Astronautics, was honored for contributions, through rigorous discoveries and advancements, in aeroengine aerodynamic and aerostructural stability and acoustics.

“Each of the MIT faculty and alumni elected to the National Academy of Engineering has made extraordinary contributions to their fields through research, education, and innovation,” says Paula T. Hammond, dean of the School of Engineering and Institute Professor in the Department of Chemical Engineering. "They represent the breadth of excellence we have here at MIT. This honor reflects the impact of their work, and I’m proud to celebrate their achievement and offer my warmest congratulations.”

Twelve additional alumni were elected to the National Academy of Engineering this year. They are: Anne Hammons Aunins PhD ’91; Lars James Blackmore PhD ’07; John-Paul Clarke ’91, SM ’92, SCD ’97; Michael Fardis SM ’77, SM ’78, PhD ’79; David Hays PhD ’98; Stephen Thomas Kent ’76, EE ’78, ENG ’78, PhD ’81; Randal D. Koster SM ’85, SCD ’88; Fred Mannering PhD ’83; Peyman Milanfar SM ’91, EE ’93, ENG ’93, PhD ’93; Amnon Shashua PhD ’93; Michael Paul Thien SCD ’88; and Terry A. Winograd PhD ’70.

Dean of Engineering Paula Hammond ’84 PhD ’93 made a resounding call for the MIT community to “embrace endless hope” and “never stop looking forward,” in a keynote address at the Institute’s annual MLK Celebration on Wednesday, Feb. 11.

“We each have a role to play in contributing to our future, and we each must embrace endless hope and continuously renew our faith in ourselves to accomplish that dream,” Hammond said, to an audience of hundreds at the event.

She added: “Whether it is through caring for those in our community, teaching others, providing inspiration, leadership, or critical support to others in their moment of need, we provide support for one another on our journey … It is that future that will feed the optimism and faith that we need to move forward, to inspire and encourage, and to never stop looking forward.”

The MLK Celebration is an annual tribute to the life and legacy of Martin Luther King Jr., and is always thematically organized around a quotation of King’s. This year, that passage was, “We must accept finite disappointment, but never lose infinite hope.”

Hammond and multiple other speakers at the event organized their remarks around that idea, while weaving in personal reflections about the importance of community, family, and mentorship.

As Hammond noted, “We can lay the path toward a better, greater time with the steps that we take today even in the face of incredible disappointment, shock and disruption.” She added: “Principles founded in fear, ignorance, or injustice ultimately fail because they do not meet the needs of a growing and prosperous nation and world.”

The event, which took place in MIT’s Walker Memorial (Building 50), featured remarks by students, staff, and campus leaders, as well as musical performances by the recently reconstituted MIT Gospel Choir. (Listen to one of those performances by clicking on the player at the end of this article.)

MIT President Sally A. Kornbluth provided introductory remarks, noting that this year’s event was occurring during “a time when feeling fractured, isolated, and pitted against each other feels exhaustingly routine. A time when it’s easy to feel discouraged.” As such, she added, “the solace we take from [coming together at this event] couldn’t be more relevant now.”

Kornbluth also offered laudatory thoughts about Hammond, a highly accomplished research scientist who has held numerous leadership roles at MIT and elsewhere. Hammond, a chemical engineer, was named dean of the MIT School of Engineering in December. Prior to that, she has served as vice provost for faculty, from 2023 to 2025, and head of the Department of Chemical Engineering, from 2015 to 2023. In honor of her accomplishments, Hammond was named an Institute Professor, MIT’s highest faculty honor. A member of MIT’s Koch Institute for Integrative Cancer Research, Hammond has developed polymers and nanoscale materials with multiple applications, including drug delivery, imaging, and even battery advances.

Hammond was awarded the National Medal of Technology and Innovation in 2024. That year she also received MIT’s Killian Award, for faculty achievement. And she has earned the rare distinction of having been elected to all three national academies — the National Academy of Engineering, the National Academy of Medicine, and the National Academy of Sciences.

“I’ve never met anyone who better represents MIT’s highest values and aspirations than Paula Hammond,” Kornbluth said, citing both Hammond’s record of academic excellence and Institute service.

Among other things, Kornbluth observed, “Paula has been a longtime champion of MIT’s culture of openness to people and ideas from everywhere. In fact, it’s hard to think of anyone more open to sharing what she knows — and more interested in hearing your point of view. And the respect she shows to everyone — no matter their job or background — is an example for us all.”

Michael Ewing ’27, a mechanical engineering major, provided welcoming remarks while introducing the speakers as well as the MLK Celebration planning committee.

Ewing noted that the event remains “extremely and vitally important” to the MIT community, and reflected on the meaning of this year’s motif, for individuals and larger communities.

“Dr. King’s hope constitutes the belief that one can make things better, even when current conditions are poor,” Ewing said. “In the face of adversity, we must remain connected to what’s most important, be grateful for both the challenges and the opportunities, and hold on to the long-term belief that no matter what, no matter what, there’s an opportunity for us to learn, grow, and improve.”

The annual MLK Celebration also highlighted further reflections from students and staff on King’s life and legacy and the value of his work.

“Everyone that has fought for a greater good in this world has left the battle without something that they came with,” said Oluwadara Deru, a senior in mechanical engineering and the featured undergraduate speaker. “But what they gained is invaluable.”

Ekua Beneman, a graduate student in chemistry, offered thoughts relating matters of academic achievement, and helping others in a university setting, to the larger themes of the celebration.

“Hope is not pretending disappointment doesn’t exist,” Beneman said. “Hope is choosing to pass forward what was once given to you. At a place like MIT, infinite hope looks like mentorship. It looks like making space. It looks like sharing knowledge instead of guarding or gatekeeping it. If we truly want to honor Dr. King’s legacy, beyond this beautiful celebration today, we do it by choosing community, mentorship, and hope in action.”

Denzil Streete, associate dean and director of the Office of Graduate Education, related the annual theme to everyday life at the Institute, as well as social life everywhere.

“Hope lies in small, often uncelebrated acts,” Streete said. “Showing up. Being present. Responding with patience. Translating complicated processes into next steps. Making one more call. Sending one more email.”

He concluded: “See your daily work as moral work … Every day, through joy and care, we choose infinite hope, for our students, and for one another.”

Reverend Thea Keith-Lucas, chaplain to the Institute and associate dean in the Office of Religious, Spiritual, and Ethical Life, offered both an invocation and a benediction at the event.

The annual celebration includes the Dr. Martin Luther King Jr. Leadership Awards Recipients, given this year to Melissa Smith PhD ’12, Fred Harris, Carissma McGee, Janine Medrano, and Edwin Marrero.

For all the turbulence in the world, Hammond said toward the conclusion of her address, people can continue to make progress in their own communities, and can be intentional about focusing, in part, on the possibilities of progress ahead.

At MIT, Hammond noted, “The commitment of our faculty, students, and staff to continuously learn, to ask deep questions and to apply our knowledge, our perspectives and our insights to the biggest world problems is something that gives me infinite hope and optimism for the future.”

MIT News · MIT Gospel Choir, MLK Luncheon 2026

Here are three papers describing different side-channel attacks against LLMs.

“Remote Timing Attacks on Efficient Language Model Inference“:

Abstract: Scaling up language models has significantly increased their capabilities. But larger models are slower models, and so there is now an extensive body of work (e.g., speculative sampling or parallel decoding) that improves the (average case) efficiency of language model generation. But these techniques introduce data-dependent timing characteristics. We show it is possible to exploit these timing differences to mount a timing attack. By monitoring the (encrypted) network traffic between a victim user and a remote language model, we can learn information about the content of messages by noting when responses are faster or slower. With complete black-box access, on open source systems we show how it is possible to learn the topic of a user’s conversation (e.g., medical advice vs. coding assistance) with 90%+ precision, and on production systems like OpenAI’s ChatGPT and Anthropic’s Claude we can distinguish between specific messages or infer the user’s language. We further show that an active adversary can leverage a boosting attack to recover PII placed in messages (e.g., phone numbers or credit card numbers) for open source systems. We conclude with potential defenses and directions for future work...

The president's rollback of tailpipe regulations comes as the global marketplace shifts to electric vehicles.

States and feds are doing little to affect data center growth. People in a Virginia county are pushing back but running into big obstacles.

Forty-one senators are accusing EPA of treating the repeal as a “foregone conclusion.”

Thousands of disaster workers remained on the job as Homeland Security funding lapsed Saturday. But states could see FEMA reimbursements stall.

President Donald Trump said the "U.K.’s got enough trouble without getting involved with Gavin Newscum."

Several Democrats joined Republicans to vote against the Clear Horizons Act, which was based on the governor's executive order to cut emissions in the fossil-fuel-heavy state.

The state solicitor general cited West Virginia's landmark Supreme Court triumph over an Obama-era climate rule as one reason he expects EPA's endangerment finding repeal to survive legal challenge.

The case was the first to ask an electric utility to pay to help communities respond to climate change.

The EU Emissions Trading System has come under intense pressure from heavy industry for raising energy costs and contributing to plant closures.

The crisis affected everything from restaurants to factories, some of which were forced to halt production.

The decline in China’s emissions, while small, may mark a turning point for the world’s largest polluter.

Key risks that the European Central Bank is monitoring include the financial impacts of extreme weather events on physical assets and supply chains.

In many academic circles, innovation is imagined as a lab-to-market pipeline that travels through patent filings, venture rounds, and coastal research hubs. But a growing movement inside U.S. universities is pushing students toward a different frontier: solving real engineering problems alongside rural communities whose challenges directly shape national food security. 

A compelling example of this shift can be found in the story of Kiyoko “Kik” Hayano, a second-year mechanical engineering student at MIT, and her work through MIT D-Lab with Keo Fish Farms, a commercial aquaculture operation in the Arkansas Delta.

Hayano’s journey — from a small, windswept town in rural Wyoming to MIT’s campus in Cambridge, Massachusetts, and on to a working Arkansas fish farm — offers a tangible glimpse into how applied engineering, academic partnerships, and on-the-ground innovation can create new models for regenerative agriculture in the United States.

Wyoming childhood and an engineering dream

Hayano grew up in Powell, Wyoming (population ~6,400), a community defined by agriculture, water scarcity, and long distances. Her early interests in gardening with her grandmother and tinkering with irrigation projects through her high school’s agricultural center formed the foundation for a more ambitious goal: studying mechanical engineering at MIT.

That ambition paid off. Shortly after arriving in Cambridge, Hayano connected with MIT D-Lab, a program founded to co-create engineering solutions with communities, rather than for them — especially in regions facing poverty, resource constraints, or climate-related disruptions. For many MIT students, D-Lab is their entry point into field-based development work across Africa, Latin America, and Southeast Asia. Increasingly, however, the program has expanded its domestic mission to include rural areas of the United States experiencing food, water, and energy insecurity.

MIT D-Lab meets the Arkansas Delta

That domestic shift set the stage for a new joint effort. In 2024, Keo Fish Farms — a commercial aquaculture farm near Keo, Arkansas — contacted D-Lab seeking technical collaboration on a growing water quality challenge. The farm had begun to observe elevated iron levels in its groundwater, leading to fish mortality events during peak summer conditions. The problem was both biological and mechanical: Aquaculture species like hybrid striped bass and triploid grass carp require consistent, clean water inputs, and well systems tapping iron-rich geologic layers were compromising fish health, hatchery performance, and long-term viability.

Kendra Leith, MIT D-Lab associate director for research, saw an opportunity. The Delta region represents a collision of three major realities that matter deeply to both public policy and academic research: high-value protein production, aging or inadequate water infrastructure, and generational rural decline.

For Hayano, the chance to work on an important engineering problem with environmental, agricultural, and economic implications was exactly why she chose mechanical engineering in the first place.

Applied engineering in a living laboratory

When Hayano arrived at Keo Fish Farms, the project was structured as a co-creative engineering engagement — D-Lab’s core model. She documented the existing water intake system, analyzed the well depth relative to geological iron strata, and evaluated filtration options including aeration, sedimentation, and emerging biochar-based media.

The collaboration generated three immediate academic values. First, the team reviewed real constraints, a process known as ground truthing. Constraints in this situation included iron levels that shift seasonally, capital budgets that do not assume infinite funding, and labor cycles tied to harvest seasons. The team then scoped out the technology that might be used to mitigate problem areas. Iron-reduction solutions ranged from drilling deeper wells to incorporating biochar and other regenerative filtration mediums capable of binding contaminants while improving soil and plant health elsewhere on the farm. Finally, they reviewed policy relevance: Water quality in aquaculture sits at the intersection of U.S. Department of Agriculture (USDA) conservation, Environmental Protection Agency (EPA) water standards, climate-driven aquifer variability, and domestic protein security — issues central to U.S. food systems.

Leith notes that “the most transformative experiences happen when students and communities learn from one another.” The Keo project, she adds, is an example of how domestic food production systems can act as test beds for innovation that previously would have been deployed exclusively abroad.

Regenerative agriculture as a national opportunity

While Keo Fish Farms played a supporting role in the narrative, the project highlighted a broader challenge and opportunity: Can U.S. aquaculture transition toward regenerative agriculture principles?

Regenerative agriculture — long associated with row crops, grazing systems, and soil carbon — rarely includes aquaculture in the national conversation. Yet aquaculture sits at the nexus of water chemistry, nutrient cycling, renewable energy integration, biochar and filtration research, protein production, and greenhouse gas mitigation.

Hayano’s work helped illuminate that regenerative aquaculture will likely depend on regenerative water systems, where filtration, biochar, solar energy, and nutrient reuse form a closed-loop infrastructure, rather than a linear extract–use–discharge model.

D-Lab’s domestic projects increasingly intersect with this space, creating pathways for MIT students and faculty to collaborate with USDA, the U.S. Department of Energy (DoE), and National Science Foundation (NSF) priorities around rural innovation, renewable energy, and water systems engineering.

The role of industry partners: less spotlight, more signal

Keo Fish Farms’ involvement served as a platform — not a spotlight — for the engineering and policy implications emerging from the project. The farm provided three critical ingredients academic institutions often lack: a real commercial engineering problem with economic consequences, a living laboratory for field research and prototyping, and a pathway for future regenerative adoption at scale.

The farm’s leadership has stated that its long-term goal is to become a first-in-class demonstration site for regenerative aquaculture in the United States, combining advanced iron and sediment filtration, biochar production from local rice hull waste streams, renewable solar energy systems, water recycling and nutrient recovery, reduced chemical inputs, and habitat and biodiversity considerations.

To be sure, the D-Lab collaboration did not solve that entire puzzle, but it created the blueprint for a pathway, showing how academic partnerships can accelerate regenerative transitions in rural U.S. agriculture and aquaculture systems.

Lessons for universities and policymakers

For universities, the Keo–MIT D-Lab partnership offers a replicable model for experiential learning for STEM students, field-based regenerative research, technology validation in live agricultural systems, and cross-disciplinary collaboration. And for federal and state policymakers, it illustrates how rural communities can serve as innovation sites, why water infrastructure modernization matters to food security, how regenerative agriculture can expand beyond soil and grazing, and why public-private-academic partnerships deserve new funding pathways.

All of this aligns with emerging priorities at the USDA, DoE, NSF, and EPA around sustainability, climate resilience, and domestic protein systems.

For Hayano, the experience reinforced that engineering careers can be rooted not only in Silicon Valley labs or aerospace firms, but also in overlooked rural systems that feed the country. 

“I’m really grateful for the experience,” she reflected after the project. “It opened my eyes to how engineering can support sustainable food systems and rural communities.”

The sentiment echoes a broader trend among students seeking careers at the intersection of technology, environment, and public good. Whether Hayano returns to the Arkansas Delta or not, her path captures something deeply relevant to America’s innovation story: talent emerging from rural places, innovating at world-class institutions, and returning engineering capacity back into the country’s agricultural heartland.

It is, in many ways, a modern form of the American dream — one grounded not in abstraction, but in water, food, soil, and the systems that will define our next century.

Industrial yeasts are a powerhouse of protein production, used to manufacture vaccines, biopharmaceuticals, and other useful compounds. In a new study, MIT chemical engineers have harnessed artificial intelligence to optimize the development of new protein manufacturing processes, which could reduce the overall costs of developing and manufacturing these drugs.

Using a large language model (LLM), the MIT team analyzed the genetic code of the industrial yeast Komagataella phaffii — specifically, the codons that it uses. There are multiple possible codons, or three-letter DNA sequences, that can be used to encode a particular amino acid, and the patterns of codon usage are different for every organism.

The new MIT model learned those patterns for K. phaffii and then used them to predict which codons would work best for manufacturing a given protein. This allowed the researchers to boost the efficiency of the yeast’s production of six different proteins, including human growth hormone and a monoclonal antibody used to treat cancer.

“Having predictive tools that consistently work well is really important to help shorten the time from having an idea to getting it into production. Taking away uncertainty ultimately saves time and money,” says J. Christopher Love, the Raymond A. and Helen E. St. Laurent Professor of Chemical Engineering at MIT, a member of the Koch Institute for Integrative Cancer Research, and faculty co-director of the MIT Initiative for New Manufacturing (MIT INM).

Love is the senior author of the new study, which appears this week in the Proceedings of the National Academy of Sciences. Former MIT postdoc Harini Narayanan is the paper’s lead author.

Codon optimization

Yeast such as K. phaffii and Saccharomyces cerevisiae (baker’s yeast) are the workhorses of the biopharmaceutical industry, producing billions of dollars of protein drugs and vaccines every year.

To engineer yeast for industrial protein production, researchers take a gene from another organism, such as the insulin gene, and modify it so that the microbe will produce it in large quantities. This requires coming up with an optimal DNA sequence for the yeast cells, integrating it into the yeast’s genome, devising favorable growth conditions for it, and finally purifying the end product.

For new biologic drugs — large, complex drugs produced by living organisms — this development process might account for 15 to 20 percent of the overall cost of commercializing the drug.

“Today, those steps are all done by very laborious experimental tasks,” Love says. “We have been looking at the question of where could we take some of the concepts that are emerging in machine learning and apply them to make different aspects of the process more reliable and simpler to predict.”

In this study, the researchers wanted to try to optimize the sequence of DNA codons that make up the gene for a protein of interest. There are 20 naturally occurring amino acids, but 64 possible codon sequences, so most of these amino acids can be encoded by more than one codon. Each codon corresponds to a unique transfer RNA (tRNA) molecule, which carries the correct amino acid to the ribosome, where amino acids are strung together into proteins.

Different organisms use each of these codons at different rates, and designers of engineered proteins often optimize the production of their proteins by choosing the codons that occur the most frequently in the host organism. However, this doesn’t necessarily produce the best results. If the same codon is always used to encode arginine, for example, the cell may run low on the tRNA molecules that correspond to that codon.

To take a more nuanced approach, the MIT team deployed a type of large language model known as an encoder-decoder. Instead of analyzing text, the researchers used it to analyze DNA sequences and learn the relationships between codons that are used in specific genes.

Their training data, which came from a publicly available dataset from the National Center for Biotechnology Information, consisted of the amino acid sequences and corresponding DNA sequences for all of the approximately 5,000 proteins naturally produced by K. phaffii.

“The model learns the syntax or the language of how these codons are used,” Love says. “It takes into account how codons are placed next to each other, and also the long-distance relationships between them.”

Once the model was trained, the researchers asked it to optimize the codon sequences of six different proteins, including human growth hormone, human serum albumin, and trastuzumab, a monoclonal antibody used to treat cancer.

They also generated optimized sequences of these proteins using four commercially available codon optimization tools. The researchers inserted each of these sequences into K. phaffii cells and measured how much of the target protein each sequence generated. For five of the six proteins, the sequences from the new MIT model worked the best, and for the sixth, it was the second-best.

“We made sure to cover a variety of different philosophies of doing codon optimization and benchmarked them against our approach,” Narayanan says. “We’ve experimentally compared these approaches and showed that our approach outperforms the others.”

Learning the language of proteins

K. phaffii, formerly known as Pichia pastoris, is used to produce dozens of commercial products, including insulin, hepatitis B vaccines, and a monoclonal antibody used to treat chronic migraines. It is also used in the production of nutrients added to foods, such as hemoglobin.

Researchers in Love’s lab have started using the new model to optimize proteins of interest for K. phaffii, and they have made the code available for other researchers who wish to use it for K. phaffii or other organisms.

The researchers also tested this approach on datasets from different organisms, including humans and cows. Each of the resulting models generated different predictions, suggesting that species-specific models are needed to optimize codons of target proteins.

By looking into the inner workings of the model, the researchers found that it appeared to learn some of the biological principles of how the genome works, including things that the researchers did not teach it. For example, it learned not to include negative repeat elements — DNA sequences that can inhibit the expression of nearby genes. The model also learned to categorize amino acids based on traits such as hydrophobicity and hydrophilicity.

“Not only was it learning this language, but it was also contextualizing it through aspects of biophysical and biochemical features, which gives us additional confidence that it is learning something that’s actually meaningful and not simply an optimization of the task that we gave it,” Love says.

The research was funded by the Daniel I.C. Wang Faculty Research Innovation Fund at MIT, the MIT AltHost Research Consortium, the Mazumdar-Shaw International Oncology Fellowship, and the Koch Institute.

Attacks against modern generative artificial intelligence (AI) large language models (LLMs) pose a real threat. Yet discussions around these attacks and their potential defenses are dangerously myopic. The dominant narrative focuses on “prompt injection,” a set of techniques to embed instructions into inputs to LLM intended to perform malicious activity. This term suggests a simple, singular vulnerability. This framing obscures a more complex and dangerous reality. Attacks on LLM-based systems have evolved into a distinct class of malware execution mechanisms, which we term “promptware.” In a ...