ARTIFICIAL INTELLIGENCE

Hugging Face’s New AI Biology Model Lets You Chat With DNAEmily Mullin | Wired ($)

“The AI platform Hugging Face on Thursday released a new open model for understanding DNA that can run locally on a laptop. Developed in collaboration with researchers at Zhongguancun Academy in China and the University of Naples Federico II in Italy, the model is trained on genomic sequences and natural language so that people can ask questions about the effects of DNA changes on gene activity and health and get conversational answers in return.”

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There is no big red button or glass box hiding a lever.

In the wake of high-profile hacking incidents involving advanced artificial intelligence agents, some politicians and business executives have been calling for an AI kill switch: a safeguard that could shut down these AI systems in an emergency.

The idea is that, if a model were to behave in dangerous ways, a kill switch would allow humans to regain control over that system by metaphorically flipping a switch from “on” to “off.”

Several efforts at AI regulation in 2026 have focused on the notion of a kill switch. These include proposed congressional legislation such as the AI Kill Switch Act, and California Gov. Gavin Newsom’s executive order aimed at reining in AI. President Donald Trump has opposed the idea of an AI kill switch as unnecessary regulation, and to date there has been no national regulation that includes a kill switch.

Experts such as Dylan Baker, a research engineer at the nonprofit Distributed AI Research Institute, have explained that one regulatory problem with the metaphor of the kill switch is that today’s advanced AI models involve a complex infrastructure of distributed technologies, which is beyond the control of a single kill switch.

Additionally, the metaphor of the kill switch also obscures the human decisions involved in the responsibility and oversight of an out-of-control model. The problem is that the metaphor does not account for the technological or human aspects of AI.

Black Boxes and Kill Switches

The metaphor of the kill switch is not necessarily new. In fact, it is rooted in the same rhetoric as the black box metaphor, which similarly blurs the complex aspects of AI by framing inner workings as confined and unintelligible. Referring to AI as a black box frames these systems in terms of restricted incomprehensibility: a monolithic stranger to human control and understanding.

At the University of Cambridge, digital humanities researcher Eryk Salvaggio argues that “the black box of AI cultivates useful confusion about what can and cannot be known” about the complexity of frontier models. The idea of the kill switch cultivates a similar confusion by obscuring the distributed infrastructure and human influence that are important aspects of AI. While these metaphors have opposite meanings—resisting control and regaining control—they invoke the same implicit argument about the nuts and bolts of AI.

Both the black box and kill switch are what cognitive linguist George Lakoff and philosopher Mark Johnson call structural metaphors. They rely on simple physical concepts—for example, object, substance, up-down, and in-out. Structural metaphors use one clearly delineated concept to help make another concept clearer. For example, black box uses the concept of an opaque container to give a clear structure to the concept of hidden or inscrutable inner workings.

Additionally, Lakoff and Johnson say that this type of metaphor can also hide some aspects of a concept by focusing attention on others. For example, the kill switch metaphor hides the complexity of advanced AI models by focusing on the fact that a model can be on or off.

What this means is that framing the operation of AI in terms of a black box and kill switch relies on the straightforward ideas of “box” and “switch” to conceal the complex operations of AI. Reining in AI becomes difficult on a policy level when discussions about AI regulation fail to account for the infrastructural and human aspects of advanced models.

Centralization and ‘Rogue’ AI

There are two problems with the structural metaphor of an AI kill switch, both demonstrating that it misses the mark for a national safeguard. The first is that a kill switch makes the global infrastructure supporting AI appear centralized, when in reality it is a web of interconnected technologies spread across the globe.

The advanced models that companies like OpenAI and Anthropic are concerned about releasing rely on multiple computers and data centers that are spread out nationally. It is not as simple as pulling a plug, opening a box, or flipping a switch. The kill switch metaphor reduces the infrastructural entanglements of AI to outdated ideas about technology. For instance, in proposing his since-failed “AI Emergency Button Act” bill, Sen. John Kennedy compared the concept to kill switches in machines such as jet skis.

The second problem is that the kill switch idea keeps hidden the human decision-making involved in the training and deployment of frontier models. Rather than serving as a reminder that people are involved, the kill switch positions the human outside of the loop and ready to flip the switch during a catastrophic emergency. Human intervention is invoked as an afterthought, instead of a process that involves training, supervision, and approval.

For instance, accounts of how OpenAI’s model went “rogue” and hacked AI company Hugging Face depict the AI agents as though they operated on their own agenda. But experts have explained that not only did OpenAI personnel turn off safety mechanisms, but they also gave the model impossible tasks and left a door open to the internet. The role people played counters the “rogue” agent framing.

The kill switch metaphor is ill-suited to the challenge of regulating AI because there is no way to switch off an AI model without accounting for its distributed infrastructure and human influence. There is no big red button, emergency brake, or glass box hiding a lever. Effective national regulation of AI will not begin with any single switch or any one person’s finger.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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A trio of papers showcases AI-designed molecular scissors and an upgraded tool that builds proteins atom by atom.

The protein universe is about to get bigger.

Scientists can now use AI to build proteins never before seen in nature. These designer proteins may become therapeutics, environmental sensors, or even the building blocks of synthetic life.

Despite enormous potential, however, AI tools still struggle to design complex proteins, like enzymes, from scratch. Unlike small proteins or peptide binders that disrupt cellular signaling, enzymes are shape shifters with a catalytic core. Getting the right shape is only half of the battle. The components must also quickly move, align, and interact at just the right moment.

But effective AI-designed enzymes may be here soon. This month, Nobel laureate David Baker’s lab at the University of Washington unveiled two types of AI-designed proteases. Proteases are like molecular scissors that cut other proteins at specific sites. The enzymes are notoriously tough to engineer, but designer versions could be made into powerful medications.

In test tubes, Baker’s synthetic scissors chopped up proteins associated with Alzheimer’s and ALS, a common motor disease. In cells, they acted like switches for inflammation-related proteins, activating them only after a precise snip and, in turn, changing how the cells behaved.

The findings open the door to destroying disease-related proteins long considered undruggable. “Proteases are just the beginning,” said study author Anqi Chen in a press release.

There’s another reason for optimism too.

In a separate paper, the Baker lab introduced RFdiffusion3, the lab’s latest generative AI model for protein design. Rather than mainly working at the level of amino acids, the tool zooms in on individual atoms, giving scientists finer control over their synthetic creations.

“The field is maturing from predicting and accurately building protein shapes and interactions to designing machinery that physically changes matter at the atomic level, in targeted and programmable ways,” said Baker in the press release.

Tailor-Made

Proteins are finicky things. Strings of molecules called amino acids twist into helices and sheets, then fold into intricate 3D shapes.

Not all amino acids are built the same. Some are relatively sleek; others sport sprawling branches called side chains. These branches influence how proteins fold and interact with their surroundings, affecting how drugs rev up or dampen their activity or change DNA and RNA behavior. Add in a protein’s ability to change shape and engineering one to behave predicably seems like an impossible dream.

With generative AI, the dream is becoming reality. Algorithms are expanding the protein universe, allowing scientists to engineer smaller or wholly new gene-editing tools, for example, or tiny antibodies that fight viruses or serve as antidotes to snake venom.

Enzymes, however, are tougher to crack. Many enzymes orchestrate an intricate dance between metals, target molecules, and other ingredients—such as water—to make a reaction happen. Any hiccups can torpedo the whole process.

Take proteases. These molecular scissors sever the chemical connections linking amino acids. Their core does the cutting, while other parts orient them so they dock to the target or otherwise control activity.

In two new studies, the Baker lab team designed two proteases. One, a zinc protease, uses a metal to drive the reaction. The other, a cysteine protease, forms a vulnerable chemical intermediate with the target, making the bond easier to break. Both strategies depend on getting specific amino acids into precise positions.

The team took inspiration from existing enzymes found in a crayfish’s digestive system and in yeast. With RFdiffusion2, their in-house design tool, the researchers built new protein backbones around each enzyme’s catalytic core to help it dock to the target. They used AI tools to select the best amino acids and built the protein around those structural constraints.

After repeating the process dozens of times, the team turned to AlphaFold3 to predict the resulting structures. Most looked nothing like known proteins, suggesting the AI wasn’t simply borrowing nature’s existing blueprints.

Thirteen of the synthetic cysteine proteases worked in a test tube test, a success rate of nineteen percent. But all were less active than the original yeast enzyme. The zinc proteases were even trickier. The team had to tweak amino acids near the catalytic core to accommodate another molecular player in the reaction. They found 36 percent of the 135 candidates could precisely cut their targets. One of these targets was amyloid-beta, a protein that forms toxic clumps in Alzheimer’s disease. The best designs made the reaction roughly 100 million times faster than when the protein target naturally broke down.

Designer proteases have a leg up compared to other protein-based medicines. Antibodies, for example, require a high dose because they only latch onto one target at a time. But with proteases, “you just need a small amount because each scissor molecule can cut many, many target molecules,” Baker said in a video provided to journalists.

But the overall low efficacy of the enzymes the team designed exposed an uncomfortable truth: Current AI tools still struggle to create more complex molecular machines.

A Magnifying Glass

To build better proteins, the lab developed RFdiffusion3, a protein design tool that zooms into the atomic world. Protein modeling algorithms, like AlphaFold3, already operate at this level, and some design tools can tweak atoms around catalytic cores. But most still focus on shuffling amino acids.

RFdiffusion3 uses atoms as the basic unit when generating the protein backbone and side chains, better accounting for the precise interactions that make enzymes work.

That’s not to say the model fully maps every atom. Each amino acid is represented by 14 atom slots—four for the backbone and 10 for side chains—that capture its structure and dynamics. Think of dots on a motion-capture suit. The dots don’t track every muscle twitch, but they do provide enough information to reconstruct movement.

This extra detail benefits enzyme design. Scientists can specify atomic positions around the catalytic core to better control the enzyme’s shape and chemical properties. The resulting blueprint more precisely maps out which amino acids to use and where to put them.

The team tested the tool on two types of newly designed enzymes in test tubes. One broke down paraoxon, an environmental pollutant. Using quantum chemistry data to model how the enzyme changes shape, the AI produced four active candidates on the first try, which the researchers further improved for speed and precision.

They also tested a zinc protease designed to cut a protein that, when misfolded and clumped, leads to kidney, heart, and nerve disease. Of 96 designs, 56 cleaved more than 10 percent of their target, a low but promising hit rate for enzymes designed from scratch.

Proteins don’t work in isolation. RFdiffusion3 can also design them in the context of other molecules—DNA, RNA, and drugs—accounting for the atomic details of their interactions. This could help researchers build final structures better tailored to their molecular partners.

As with all AI-designed proteins, each candidate will have to be vetted in cells. But the team is hopeful the upgraded tool could unlock more creative possibilities. “We anticipate RFD3 [RFdiffusion3] will be broadly useful for a wide range of biomolecule design challenges and applications,” they wrote.

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The treatment delivers a light-sensitive algae protein to the retina. Paired with special goggles, it can restore limited vision and lasts for years.

The patients sit perfectly still as a needle inches toward one of their eyes. After the injection, they’ll no longer be completely human. The syringe is filled with a gene therapy that installs a light-sensitive algae protein into failing retinas and could reboot some lost vision.

When scientists first proposed using algae proteins to restore human eyesight, the idea seemed like science fiction. Could alien proteins integrate into our intricate visual system? Even if they did, could the brain translate the resulting signals into sight, rather than nonsensical bursts of light?

A new study suggests yes, with caveats. The team treated people with retinitis pigmentosa, a genetic disease that gradually destroys light-sensing cells in the retina. Seven of ten patients showed improved light sensitivity. After patients donned goggles that deliver pulses of light to activate the algae proteins, the team tested their eyesight. Roughly half could reach for and touch objects or identify the orientation of a black bar, a common test of visual recovery.

The treatment is still far from restoring full vision. What patients saw was too blurry and the field of view too narrow to discern texture or fine detail. The boundaries of objects looked squiggly. But those who responded to the treatment continued to improve over roughly five years. One participant, whose story made headlines in 2021, can now recognize and count different objects, and white stripes on a crosswalk pop out.

The study, called PIONEER, is an ambitious clinical trial combining gene therapy with optogenetics. The first goal was to assess safety, and it met the mark. Most participants experienced a brief bout of inflammation in the eye receiving the injection. The only patient to suffer a severe side effect was quickly treated with medication.

The findings suggest the initial success wasn’t a fluke. The study “represents an important milestone for optogenetic vision restoration,” said study author Stefan Futterknecht at the Institute of Molecular and Clinical Ophthalmology Basel, in a press release.

Setting Sights High

Around two decades ago, Karl Deisseroth was hunting for new ways to control brain cells. Electrical bursts and chemicals, which are how neurons communicate, seemed like obvious choices. But nature had something stranger to offer: algae.

Algae produce a protein called channelrhodopsin. When activated by light, the protein changes neuron activity using a mechanism that works similarly in mammalian cells. In a foundational paper, Deisseroth’s team inserted the protein into rodent neurons, and voilá—the neurons could now be controlled using light.

The technology, called optogenetics, revolutionized neuroscience.

By genetically tagging specific neurons with channelrhodopsin, scientists can now dial the cells’ activity up or down like a dimmer switch. Optogenetic experiments have revealed brain circuits that shape feelings, memory, and cognition with unprecedented precision.

Researchers panning for more genes in different algae strains struck gold again. One channelrhodopsin variant they found, ChrimsonR, was particularly exciting. Unlike the classic version that’s activated by blue light, ChrimsonR responds to amber light, which penetrates tissues better and is less damaging. More recently, scientists have begun to use generative AI to redesign natural proteins to make them even more sensitive to light.

“Optogenetics provides opportunities for mapping the brain in a way that we could once only dream of,” said Per Svenningsson, Chair of the Nobel Committee for Physiology or Medicine, when the technology won the coveted prize this year.

Still, it has mainly been considered a research tool.

Light It Up

Retinitis pigmentosa gradually robs people of their eyesight. Researchers have linked over 100 genetic variations to the condition, which kills off light-sensitive cells in the retina.

“The replacement (or editing) of a single gene would not help most persons,” wrote Deisseroth in the New England Journal of Medicine. Instead, adding channelrhodopsin could offer a more universal solution by rewiring surviving neurons.

Early successes bolstered the idea. In one study, scientists equipped cones—a type of retinal light-sensing cell—with channelrhodopsin in mice with retinitis pigmentosa. The therapy boosted their light sensitivity. It also reactivated cones that had stopped responding to light in human retina samples.

The new study took a different approach. In advanced disease, cones may have already degenerated beyond the point at which they could be saved. So instead, the gene therapy targeted ganglion cells. These cells process signals from light-sensitive cells and pass that information to the brain, but don’t detect light themselves. Ganglion cells are, however, more resilient than cones. Add channelrhodopsin, and they could become substitute light sensors.

The team packed genetic instructions for ChrimsonR into a harmless viral delivery system and injected it into the poorer seeing eye of 10 patients aged 40 to 73. All were legally blind, and each received one of three escalating doses.

As the patients gradually built up levels of the algae protein in their eyes, the team faced another challenge: Translating visual scenes from the outside world into amber light that the engineered ganglion cells could recognize.

They developed specialized goggles that look a bit like Apple’s Vision Pro. The goggles use a camera to scan the field of view thousands of times per second, monitoring for light changes in every pixel. When the device detects a change, it pulses amber light from that pixel into the eye. Because our eyes naturally dart around—often without us noticing—this dynamic method may better mimic normal vision.

With the goggles on, 6 of 10 participants showed clinically meaningful improvement in light sensitivity, with more than a fourfold boost over baseline readings. One participant improved over 62-fold. Of the eight who agreed to further eyesight tests, roughly half could touch a notebook, grab a small staple box, and detect changes in a rotating bar.

Of those who initially responded to the treatment, improvements lasted for years. Their brains also seemed to adapt to the alien light sensors. In one test, participants were asked to open or close their eyes while looking at a tumbler. Electrical brain signals related to vision were more coherent with eyes open, suggesting their perception depended on improved visual input.

Any gene therapy carries risk. In an already worsening eye, inflammation and other side effects could cause further damage. Overall, the injection appeared relatively safe. Most participants experienced a temporary increase in eye pressure, but no detectable side effects throughout the body. The retina’s overall structure remained unchanged. One person suffered a blockage in a major eye artery that cleared within minutes after treatment with an FDA-approved eye drop.

For now, the participants still can’t read or see faces. The therapy reaches only a fraction of their ganglion cells, making sharper vision harder to restore. More advanced versions of channelrhodopsin with greater light sensitivity may increase resolution.

As for the hardware, the team is working on versions of the goggles that deliver more accurate and timely stimulation. Potential upgrades include an eye tracker, software to preprocess visual scenes, and even holographs.

“Overall, although optogenetic methods have been developed primarily for basic science discovery,” wrote Deisseroth, they’re now heading towards “diverse therapeutic approaches.”

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Using a system called BootLoops, scientists produced 36 papers in three months. Paired with experts, the tool could pollinate ideas across fields.

Some major scientific advances involve taking a technique developed in one field and applying it to another, but interdisciplinary research is tricky and time-consuming. Now a new AI research system has automated the process, generating 36 papers in three months.

Most researchers work within a narrow specialty, and few have time to comb through unrelated fields for tools that might help them. As a result, researchers can end up banging their heads against a problem scientists in another discipline have already solved.

But Harvard physicist Matthew Schwartz stumbled across a potential solution while working as a visiting researcher at AI firm Anthropic. After getting the company’s AI, Claude, to develop a suite of tools for mathematical physics, Schwartz discovered they could help solve quantitative problems in a host of other areas.

Called BootLoops, the tools, which are model agnostic, help AI search scientific literature, write code, and solve mathematical problems. Working with 19 coauthors, Schwartz used BootLoops to produce 36 manuscripts on topics ranging from ecology and linguistics to health insurance and sunspots.

“Once Claude had BootLoops, it kept noticing the same pattern: many fields have problems that a technique from mathematics, physics, or computer science would solve outright if anyone knew it existed,” Schwartz wrote in an Anthropic blog post about the system.

Schwartz first started using Claude as a general-purpose research assistant last December, and while he was able to do some useful work with it, he says it required constant handholding. So he decided to instead point it at problems that were a good match for the model’s strengths—what he calls “Claude-shaped problems.”

He started with the equations physicists use to interpret particle collisions at the Large Hadron Collider. The best methods were scattered across software in several programming languages or existed only on paper, so he got Claude to combine them into a single framework. Using the tools it had built itself, the model reproduced the results of one of his papers in about 20 minutes.

Schwartz then moved onto a harder class of these equations, and within a few weeks, the model had solved 30 of them, including 15 that had never been calculated before. It didn’t take long for Schwartz to realize that many of the methods the AI had developed could be used in neighboring areas of physics such as cosmology and string theory.

But it was Claude itself that made the bigger leap, pointing out that the same kinds of equations turn up in population genetics and evolutionary biology. The problem for Schwartz, however, was that he found it harder to tell when Claude was overselling its findings in fields he was less familiar with. So he brought in experts from each field to judge which of Claude’s findings were worth pursuing, and from some 400 candidate problems, 36 ended up as papers.

One project examined the star ratings used to grade private health plans under Medicare, which determine the bonuses insurers receive. The rules say the boundaries between ratings levels should be drawn so each plan’s score sits as close as possible to the average of its group, but the algorithm Medicare actually uses is a shortcut that doesn’t guarantee this.

Using a statistical method dating back to 1958 to calculate the exact answer, BootLoops found drawing precise boundaries would push 34 plan ratings across the bonus line, 16 up and 18 down. This would shift an estimated $0.9 to $1.4 billion in bonus payments between 2025 and 2027.

Another project tackled a 150-year-old puzzle about what kills sunspots. Because the sun’s rotation hides sunspots from view for much of their lives, astronomers have never been able to agree on how long they last or why they disappear.

Borrowing a tool doctors use to measure how the risk of dying changes with age and applying it to every sunspot logged since 1874, the team found that sunspots age. At a given size, their risk of “dying” rises about 20 percent a day, something none of the published theories could account for.

For each new problem, Claude built new tools, so the BootLoops toolkit kept growing, and it now spans 22 fields, according to the project’s website. Other problems it’s tackled include how Earth’s atmosphere first filled with oxygen, how quickly tree species come and go in a Panamanian rainforest, and how natural selection weeds out harmful mutations in humans.

The kinds of cross-disciplinary leaps BootLoops can make are extremely rare, Georgia Channing, artificial intelligence for science lead at Hugging Face, told Science. “A physics researcher wouldn’t spend six months reading linguistics research in order to figure out how to repurpose linguistics methods,” Channing said.

That’s not to say the system isn’t without flaws. Schwartz says many of Claude’s early findings were technically correct but scientifically unremarkable until experts steered them, and he warns that the model “loves to declare victory” too early.

But the results suggest AI may be a powerful way to pollinate ideas across research fields, even if we still need humans to decide which ones are really worth pursuing.

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Scientists want to create cells from scratch, to unveil life’s origins or provide specialized functions. Are they close?

This article was originally published on Undark. Read the original article.

On a lonely planet, billions of years ago, a soup of chemicals—in the right place, at the right time, and with the right conditions—shifted from an unalive assemblage into something scientists would call life.

Billions of years and a lot of evolution later, researchers are now attempting to reproduce that shift in their labs. They are, put simply, trying to create life. If they mix Goldilocks ingredients together in a Goldilocks way, the ingredients will hopefully coalesce into cells, with all the parts cells have and the functions they perform. And they’re getting awfully close.

Scientists working toward this endpoint have different aims and generally fall into two main groups. The first wants to understand how life came to be in the first place by replicating the conditions of this long-ago lonely planet; the second want to design cells from scratch that can potentially perform specialized jobs for humans, from medical treatment to manufacturing without the billions of years of evolutionary baggage that can have unpredictable effects.

Both sides of the effort are growing, with a coordinated program funded by organizations like the National Science Foundation and research supported by NASA. There has also been more regulatory scrutiny—a sure sign of scientific progression, and the reality of the technology’s risks.

Kate Adamala, a synthetic biologist at the University of Minnesota, was in the former research group, focusing on the origin of life, but decamped to the latter after graduate school. “I want something that’s still very foundational, very discovery-driven, but it would be nice if there were actual real-life applications,” she said. She found it in synthetic biology.

Today, she uses the field’s modern tools to try to make life in the lab—and she recently made a breakthrough: In July, she announced that she had built a synthetic cell she dubbed SpudCell. It can feed, compete for food, grow, divide, and replicate its DNA—showing, in other words, a cell’s complete life cycle.

Still, it’s not quite “alive,” scientists tend to agree: It can’t whip up its own protein-makers, the full genome doesn’t carry through more than a few generations, and it can’t evolve on its own. The paper also has not yet been peer-reviewed or published in an academic journal.

If they mix Goldilocks ingredients together in a Goldilocks way, the ingredients will hopefully coalesce into cells, with all the parts cells have and the functions they perform. And they’re getting awfully close.

If the SpudCell were alive, it wouldn’t be life as it arose on ancient Earth, necessarily a basic and limited form. But research on it and related work could illuminate the basics of biological functioning, while also yielding ginned-up cells that humans could put to work by, say, creating chemicals that now come from petroleum. Adamala liked the combination of basic science and application. “That turned out to be jackpot,” she said in an interview with Undark in February.

She’s been working toward that, building what those in the field call “synthetic minimal cells,” cells constructed with known ingredients, to accomplish specific tasks (like metabolizing); they, according to Adamala’s university webpage, have “some, but not all functions of live natural cells.” The version she has recently worked on, SpudCells, are essentially tiny fat bubbles with DNA or RNA inside. That genetic material can instruct the fat bubble to make proteins, as it does in natural cells. Her creations can also merge, a capability that could allow them to interact with human cells later and send their protein products to the outside world. That would make them useful to the medical or manufacturing communities, since the molecules that become plastic or medicine don’t do any good if they remain locked inside a cell’s boundaries. SpudCell is an advanced version of these ideas, using 90,000 genetic base pairs and a premade mixture of enzymes, borrowed from actual life. The cell can copy DNA, feed using proteins it produces, and divide once it’s fed sufficiently.

As synthetic biology develops, the field’s quest to make life in the lab could help lead to targeted medical treatments, but that pursuit could also someday—without proper regulation and oversight—pose safety and containment concerns, if the technology were weaponized to deliver harmful material or if a leak allowed the cells to travel beyond their intended target. And while Adamala’s work replicates—as do the Frankenstein creations of other researchers—some aspects of life, its products can’t, say, replicate themselves, as a normal cell would.

To say someone has created life in a lab, they would need to get a synthetic cell to do all the things life does, at once. But getting the parts to be more than their sum has proven difficult. “Up until now, the conversation has been, ‘Well, you put all these things in physical proximity at the same time, and then magic happens, right? Life happens,’” said Elizabeth Strychalski, head of the Cellular Engineering Group at the National Institute of Standards and Technology.

But life hasn’t happened—at least not yet.

Even if a scientist were to succeed, determining that’s the case might be complicated, because there is no official definition of what makes something alive. Adamala wants to make life in a lab. But what, actually, does that mean? “I can’t tell you, because I don’t have a definition of life,” she said.

Adamala, for one, is sanguine about that uncertainty. “We quietly ignore that problem and just move on with ‘I want to make something that looks like a cell,’” she said in the February interview conducted before she had released the SpudCell paper. “And I will know it when I see it.”

The dictionary version of life might be squishy, but many scientists do generally agree on a few of its characteristics: It has to reproduce, turn fuel into energy through metabolism, respond to that environment, maintain its own internal environment, and evolve.

How some chemicals in Earth’s younger years came to accomplish all those things remains one of science’s biggest mysteries. Life, in many ways, comes down to chemistry; studying the interaction between certain molecules can reveal how they may have come together and undergone chemical reactions that eventually became biological.

But finding the answer is one goal of the Origins of Life Initiative at Harvard University, a collaborative research group that began around 20 years ago. “I met and started talking to people who were working on the deep history of Earth,” said Dimitar Sasselov, an astronomer and the organization’s founding director. “What was the Earth like when life came about?” And what, he wondered, did the early evolution of that life look like?

Sasselov decided that understanding that tipping point—how life, once it emerged, transformed and took over the planet—deserved more study. But it required a big tent, with people from many disciplines: thus, the Origins of Life Initiative. The researchers all had to become mini-experts in each other’s fields. For his part, Sasselov jokes that he got his initial knowledge of chemistry from reading the “Complete Idiot’s Guide” to the topic.

As the Origins of Life Initiative was spinning up, Sasselov helped spearhead the Kepler Space Telescope launch in 2009. The project’s goal was to discover planets a bit like Earth, orbiting their stars in ways that might make them habitable. The telescope went on to discover thousands of planets of all sorts. At the same time, attempts to create life, or its pieces and parts, in labs across the world accelerated. Sasselov thought that was an interesting way (perhaps the only way) to find out how life might have arisen here or on those other worlds that Kepler saw.

Sasselov, for instance, looks at how RNA—which, among other tasks, acts as a messenger to deliver DNA’s genetic instructions—interacts with small amino acids and peptides. He’s not himself trying to build a full cell, but rather trying to understand how initial cells’ building blocks may have stacked together.

Much of Sasselov’s work focuses on shining (literal) light at such foundational molecules and seeing which can preferentially survive and even harness the ultraviolet bombardment, which the sun would have blasted at early Earth. Knowing more about that could reveal key ingredients in life’s assembly.

In fact, many experts suspect that life may have started after RNA formed, spurred on nearby chemical reactions, and spontaneously started to replicate; only later would the more complicated genetic material, DNA, have formed from its simpler upstart. Earlier this year, researchers at the MRC Laboratory of Molecular Biology and the Inria Lyon Centre published a Science paper outlining their discovery of a short strand of RNA that came close to replicating itself.

Many experts suspect that life may have started after RNA formed, spurred on nearby chemical reactions, and spontaneously started to replicate.

Other researchers recently combined water and silica, and shot electricity at the mixture to simulate lightning. Almost like magic, their concoction formed a solid organic film, made of polymers—large molecules made of similar units bonded together—of hydrogen cyanide. The film contained little spherical structures the research team deemed “protocells.” The structures and the ingredients could plausibly have existed together eons ago, and with further chemical evolution—way back when, and perhaps soon in the lab—could organize into something more complex.

Work at the Origins of Life Institute and other beginning-of-life-focused research groups complements work done by scientists like Nobel Prize-winner Jack Szostak, at the University of Chicago, one of the leaders in investigating the idea that RNA may have preceded, and become key to, the first cells.

Szostak has worked on creating cells whose boundaries are fatty acids, with RNA inside—similar in idea to Adamala’s minimal cells. (Adamala did her graduate work in Szostak’s lab.) “Experimentally, we’re trying to just build a system that we think, in some ways, mimics what the first simplest cells, otherwise known as protocells, would have looked like,” said Szostak.

In the right conditions, Szostak’s creations can divide and split the RNA so that each of the pair gets some. The lab has also made cell-like structures whose RNA can create a copy of itself internally. Recently, his team has investigated whether RNA segments arranged into a circle might point the way toward actual genetic replication. They came up with this idea, he says, during the early months of the Covid-19 pandemic, when they couldn’t go into the lab but could talk to each other.

All of these parts need to come together, from the soup to the cell, for the astrobiologists to declare they’ve made life in the lab. That could happen in a year, Sasselov said—or a decade. “It is not that we need a bigger lab. It is not that we need a bigger computer. It is not that we need a specific reaction to work or not to work,” he said. “It is really more of a puzzle which has to suddenly come together.”

Adamala, meanwhile, is trying to put the puzzle together in a different way—one with more tools available. “When you’re trying to do it the proper way, from the astrobiology point of view, you’re basically not allowed to use anything that works well,” she said.

By that she means that scientists like Szostak limit themselves to the conditions of an old Earth; Adamala, on the other hand, can work to create a living cell using, essentially, any means necessary; she can print or clone strands of DNA, or design a protein with AI, for example. In creating the SpudCell, for instance, Adamala began with a mixture of the molecules a cell would need, already formed. She also took some chemical ingredients from a virus, and E. coli, and added the lipids that would form the cell’s membrane. She gave the cells specialized food that allowed them to grow, and used a protein that made the cell bend inward when its surface became crowded from feeding. That bend became a split, producing a new cell that could also eat and grow. When the research team created a mutant SpudCell that was a more efficient feeder, and mixed it with the original-flavored cell, it gobbled up more of the food than the predecessor and came to dominate the population, a compelled proto-evolution.

Adamala’s recent work embraces tools that allow her to construct cells from the ground up because her research’s scientific motivation is not to understand how the first life came to be, but how life fundamentally works and can be made to work for humans today. For instance, she imagines using synthetic organisms to produce petrochemicals that currently only come from oil, or to enable new cancer therapies. She and a collaborator have started a new nonprofit organization called Biotic, which intends to make the SpudCell an open-source system that others can license to do their own work.

SpudCell’s ingredients are all known. That’s true of any synthetic minimal cell, and such creations could tell us, Adamala said, which ingredients are strictly necessary for something to be alive—many of natural cells’ components may actually be extraneous to the basic “life or not” question.

Despite the differences in the two groups’ approaches, Sasselov sees a unity in their motivations. “We both want to understand at a fundamental level what life is,” he said. And to do so by building life. “But how we get there is different, and this is why we have so many astronomers and planetary scientists, geologists involved in this in our group,” he continued. “The other groups are mostly composed of biochemists, chemists, biologists.”

But the two camps meet and talk regularly, and discuss their progress. Sasselov thinks that once either group gets close to the ultimate goal, their similarities will increase. “We will essentially merge, because some of the techniques that they have discovered and developed will be useful to us, and many of the things that we have discovered will be useful to them,” he said. “And then we’ll build something which is the same, but with both of our efforts. So despite the fact that right now, we seem to be going on different trajectories, eventually it will be a big overlap.”

“We both want to understand at a fundamental level what life is.”

Scientists on Adamala’s side of the fence have succeeded in replicating some of life’s basic functions. Some synthetic biological creations can send and respond to signals in the environment. Some can communicate with each other. Others can make fats or protein building blocks; metabolize; replicate genetic material; and even introduce mutations that count as evolution. SpudCell can eat, grow, replicate, and do that rudimentary-but-not-quite-there version of evolution. But it can’t do all the things a normal biological cell can, like actually evolve, or make its own protein factories.

“The biggest thing that’s left to do, and it sounds simple, but it really is not, is to put all of it together,” said Adamala in the February interview. SpudCell is an attempt at that.

One key figure in this endeavor was J. Craig Venter, who led the effort to sequence the whole genome of an organism, back in the 1990s. Venter, who founded the J. Craig Venter Institute, passed away at the end of April at age 79. His goal in that early effort wasn’t just the sequencing: It was finding out what the genes did. In the 2000s, he and collaborators made synthetic genomes for a virus and a bacterium. For the latter, they used what they had discovered about genes’ functions. In 2010, they followed that up by putting a synthetic genome—designed in a computer and assembled from real DNA fragments—into a normal, living cell.

Six years later, his institute published the results on their synthesis of the first minimal synthetic bacterial cell, also by implanting synthetic material into a preexisting cell. But it replicated strangely, growing as a “giant blob,” in Venter’s words, with varying shapes, rather than staying small and similar as expected. By 2021, the group added 19 new genes, seven of which are linked to division; the cell split like normal.

Shortly before his death, Venter said he didn’t know exactly what would come next. “I have my hope list, what we’d like it to be,” he said in March. For one, making a synthetic cell without having to wipe a living one and stick new DNA in it; creating it more from the ground up.

Once truly synthetic life exists, Venter said it could enable the kinds of things that brought Adamala to this side of the research, by giving a cell specific genetic instructions—making, say, drugs, chemicals, construction materials, and even food.

Together, fundamental researchers focused on the origin of life and synthetic biologists could show how life came to be, how its pieces and parts work together, and how making new life could change the world’s workings today as it did billions of years ago.

Not everybody fits neatly into those two boxes, though. Take Juan Pérez-Mercader, a senior research fellow at Harvard University. He’s approaching this central question of biology and experimental chemistry as a trained physicist. Before he tried to make some semblance of life in a lab, he worked on things like unifying the fundamental forces of the universe.

But Pérez-Mercader eventually tired of those particular equations and turned, instead, to the fundamental properties of living systems—handling information, self-reproducing, evolving, and metabolizing. Could they also be represented with equations? With the idea, he founded and directed Madrid’s Centro de Astrobiología, and then later took his work to Harvard.

He began by making equations to model the general properties of life that scientists agree on, and turned those into computer solutions. Out popped swimming, digital single-celled beings. “If I do this in a computer, I can do this with chemistry,” he said.

His chemistry started with a compartment—a kind of container for the cell’s parts, like Adamala and Szostak’s lipid bubbles. But Pérez-Mercader wanted to do things a little differently: He wanted to show that you could use his equations to make something that qualified as “alive” without being beholden to particular molecules that make up life as we know it—to show, in other words, that the properties we use to define life aren’t actually ingredient-specific. “In my lab, upstairs from this office, there is a sign that says, ‘No biochemistry, no biochemicals allowed,’” Pérez-Mercader said.

A breakthrough came when his team found out about a technique called polymerization-induced self-assembly, used in other fields. In short, they could put carbon-based molecules that are hydrophilic—meaning they like water—into H2O with carbon-based monomers and photo initiator molecules that convert energy from light into chemical energy; hydrophilic molecules, by virtue of how their electrons are arranged (unevenly), are attracted to water molecules, whose electrons are arranged similarly.

“In my lab, upstairs from this office, there is a sign that says, ‘No biochemistry, no biochemicals allowed.’”

Exposed to light, these molecules link to each other to form polymers. When they do, they gain a hydrophobic, or water-resistant, part that has evenly distributed electrons. Their water-loving ends then naturally turn to face the water, while the water-fearing ends fold themselves inward, away from the water. They thus form themselves into little spheres, called micelles. These micelles are filled with fluid and separated from the environment.

The molecules continue to link to each other, making the micelles thicker. The pressure inside grows, and eventually a portion of the structure breaks off, to form a new micelle, which then itself does the same—reproduction. The new micelles are not clones of each other, but slight variants—a primitive kind of evolution. And because they’re using light to sustain themselves, they perform a sort of metabolism.

The system, then, exhibits versions of the characteristics of life, integrated into one system, without ingredients like RNA, DNA, lipids, sugars, or proteins.

Pérez-Mercader’s micelles are not made with typical biochemistry, like DNA or RNA, so not everyone agrees they would qualify as alive. The journal Cell reportedly rejected the paper about SpudCell after one peer reviewer said it was not real biology. Adamala told Undark she doesn’t usually comment on the review process. Many, if not most papers, she wrote via email, go through more than one submission. “For people outside of science it sounds like a big deal, for us it’s Tuesday,” she wrote.

Synthetic micelles in Juan Pérez-Mercader’s lab form autonomously and grow in the presence of light and food, making the micelles thicker until they mature into vesicles. The vesicles eventually release what Pérez-Mercader calls “spores” that grow into new micelles and then into new vesicles, thus performing a reproductive cycle.

People who come from the origins of life side of the equation are sometimes criticized for assuming all life will be like familiar life on Earth, said Robert Dorit, a biologist at Smith College. But they’re also criticized if they consider less Earth-chemistry-specific options. “I think you can defend an argument that says our definition of life should also not be unnecessarily tethered to what we know here,” Dorit said. But, to him, “alive or not” is not ultimately the most interesting question. Any life made in a lab won’t be familiar life, which formed itself thanks to eons of evolutionary pressure. And trying to create weird life, not quite like a typical cell, can reveal things about life that formed naturally.

Pérez-Mercader, for one, says that his work is proving two things at once: that you can turn nonlife to life in the lab, and that what we think of as life doesn’t depend on a particular chemistry. And so, his thinking goes, it could have arisen on planets very different from ours.

Pérez-Mercader’s research, though, swings back around to a central point, as does Adamala’s: If scientists don’t have consensus on what life is, how will they know when they’ve created it?

The answer falls partly to Strychalski, of the Cellular Engineering Group at the National Institute of Standards and Technology. NIST’s governmental duty is to measure things. Sometimes that’s how long a meter is. Sometimes it’s how alive a fake cell is. It’s different from measuring the materials in nanotechnology, or devising metrics for cybersecurity. Instead, in this field, “we’re building with the stuff that we are also made of,” she said.

“It’s not going to get up and walk out of your petri dish.”

Strychalski also does research in this scientific field, having worked with Venter’s institute on a recent project, for instance. And she recently attended a workshop that Adamala organizes, under a research network called Build-a-Cell, which since 2017 has been gathering researchers twice a year who are trying to make life in the lab. “That’s my favorite thing about this community, is that it actually is a community,” Adamala said.

One thing was clear at that Build-a-Cell workshop: “Some folks are getting really close to assembling living cells from nonliving material,” Strychalski said. But that pesky part about getting all the nonliving materials to work together on their own still dogs them.

Strychalski is still investigating what measurements are needed to troubleshoot cell-building and what measurements are needed to decide that ultimate question: Does it live? “It’s not going to get up and walk out of your petri dish,” said Strychalski.

The answer to the question is important to determine not only who will be the first to create true life in the lab, and what is needed to do so, but also for safety. As SpudCell develops, Adamala plans to actively pursue biosafety measures, she wrote in a follow-up email. “Once it becomes more robust and able to grow and divide faster and more efficiently, we’ll need safeguards,” she wrote, which are designed to stop the cell from replicating outside of a controlled environment.

“If someone does make something that’s alive, or alive in a new way, in the laboratory, I want to make sure that they realize that’s happened,” Strychalski said, “so they can bleach the bench.”

Strychalski isn’t alone in being mindful of synthetic biology’s risks: Adamala, Szostak, Venter, and dozens of others penned a 2024 Science paper, about the potential for creating something called “mirror life.” On Earth, all known biological molecules of a given type face a single direction: All genetic material is “right-handed,” for instance. The amino acids making up proteins face the other way and are “left-handed.” But some scientists have begun work on left-handed DNA, or right-handed proteins. Maybe someday—at least a decade from now, the authors estimate—there could be whole mirror cells.

Life and lifelike elements like this, though, could pose huge dangers, according to the authors. They might be pathogens against which we have no defenses, or they could act like invasive species. The field is working to get ahead of those possibilities. “We’re seeing basically a global push for the moratorium,” said Adamala, discussing a kind of synthetic cell deemed particularly dangerous. While there aren’t yet amendments to international conventions, the wheels of bureaucracy are turning. The UNESCO International Bioethics Committee, for instance, recommended a moratorium on creating mirror life, and a group convened by the UK Government Office for Science recommended potential regulation against the development of mirror genomes that can self-replicate. The UN Secretary-General’s Scientific Advisory Board suggested defining “red lines” in mirror-life research policy.

Strychalski points to this as an example of where getting ahead isn’t just good—it may be necessary. “There might not be a second chance,” she said. And she thinks more monitoring could be good for synthetic biology research in general. “Right now, there’s like no requirement for monitoring your laboratory or office space for accidental release of engineered biological anything,” she said.

“If someone does make something that’s alive, or alive in a new way, in the laboratory, I want to make sure that they realize that’s happened,” she said, “so they can bleach the bench.”

She grew up in science thinking about such potential bad outcomes, since she, like Pérez-Mercader, came from physics. Physicists tend to keep their field’s Manhattan Project roots and potential for dual-use technology—technology that is useful for both benign civilian and potentially malign military purposes—in mind, she said, remembering how quantum mechanics was weaponized into an atomic bomb by the very people who were in the process of discovering it.

Just because you can do something doesn’t mean you should, Strychalski notes. “I’m telling my toddler this constantly,” she said, “but I think it holds in the laboratory as well.”

The post The Evolving Quest to Build Life in a Lab appeared first on SingularityHub.

Jonathan the giant tortoise was born when Darwin was still a young man. Youthful gene activity may account for his exceptionally long life.

Getting a DNA sample is simple. Rub a Q-tip a few times inside your cheek, and voilà.

Not so with a 400-pound Aldabra giant tortoise. At the age of roughly 194, Jonathan is the oldest known living land animal on Earth. He’s spent most of his life lounging on the manicured lawns of the governor’s residence on the island of St. Helena, where he arrived as an adult. Though blinded by cataracts, Jonathan still chows down on grub and has excellent hearing.

Even among long-lived tortoises, Jonathan is exceptional. He’s lived around a century longer than others of his kind. Now, thanks to his DNA, an intrepid team of scientists may know why.

Getting a sample was tricky and dangerous. Jonathan’s veterinarian, Joe Hollins, lured him in with fresh greens, then quickly wedged his mouth open to scrape cells from inside his cheek. The tortoise tolerated the unusual procedure for only a few seconds. Any longer, and Hollins might have faced the wrath of Jonathan’s razor-sharp beak.

But the maneuver paid off. After sequencing the sample, the team found that Jonathan’s genome was better maintained compared to tortoises roughly a century younger.

As animals age, their DNA accumulates mutations. Telomeres, the protective end caps of chromosomes, shrink. Chemical tags that control which genes are turned on or off also go awry, straying from their normal patterns and settling on unexpected spots across the genome.

For study author Stephen Clark, founder of the nonprofit Kallel Foundation, this growing disorganization, called entropy, is central to aging. Yet Jonathan has weathered plenty of biological wear and tear despite his advanced age.

“Nature has already solved the puzzle of aging in remarkable ways, and Jonathan’s genome provides a blueprint for cellular resilience,” said Clark in a press release.

Aging Outliers

Animal research has given us clues to how we might live healthier, longer lives.

At one end of the spectrum are short-lived creatures. Studies of lowly roundworms and fruit flies—often squished or swatted—have uncovered genes and molecular pathways that let cells sense nutrients, withstand stress, and clear cellular waste. These processes falter with age.

At the other extreme are animals that seemingly don’t age. Take the hydra, a tiny, carnivorous jellyfish-like animal. In long-term studies, some species showed no detectable rise in mortality or lower reproduction with age and retained the ability to regenerate after severe injuries. Their “immortality” may come from a steady supply of stem cells and resilience against senescent cells that no longer work properly.

Naked mole rats have also caught scientists’ attention. These nearly hairless rodents can live more than 30 years, roughly 10 times longer than a mouse of similar size. They resist many types of cancer, maintain strong hearts and muscle mass into old age, and rarely develop Alzheimer’s disease, even with toxic protein buildup. By studying these animals’ genetics, scientists have discovered ways they preserve gene expression machinery and maintain protein stability as the clock ticks on.

Tortoises offered more inspiration. In 2019, scientists sequenced the genome of Lonesome George, the last known Pinta Island giant tortoise, who died at an estimated 100 years old. His genome revealed beneficial adaptations in DNA repair, immune function, and inflammation. A subsequent study comparing multiple turtle lineages found shared genetic changes associated with cellular resiliency against stress and cancer protection.

“Each of these long-lived species has some kind of special trick,” biologist Steve Austad at the University of Alabama at Birmingham told The New York Times. “And some of those may turn out to be relevant to human aging.”

But as the oldest land creature, Jonathan is irreplaceable. Hidden in his DNA may be another set of tricks for keeping cells, and maybe the entire body, functioning for longer.

Spirited Supercentenarian

Collecting Jonathan’s DNA without getting bitten or kicked was only the first hurdle. Shipping the precious cargo across the ocean without damaging it was another. Then came the headache of piecing his genome together.

Cheek cells yield shorter DNA fragments that must be stitched together, and samples can be contaminated with bacteria. The first try at reconstructing Jonathan’s genomic blueprint had gaps, so the team, led by the Nashville-based nonprofit Kallel Foundation, used DNA from a younger tortoise to fill them in. Roughly 95 percent of the resulting Frankenstein genome was Jonathan’s.

A blood draw, the gold standard for DNA sequencing, might have yielded a clearer map. But St. Helena officials ruled it out because of the risk of infection.

Even so, Jonathan’s genes revealed intriguing differences compared to four other Aldabra tortoises roughly a century his junior. Of the 287 genes unique to Jonathan, many were involved in DNA repair or preventing telomeres from shortening—both of which help keep the genome stable. Roughly a dozen of Jonathans’ genes overlapped with genes implicated in naked mole rat longevity and that of other long-lived animals, hinting at shared evolutionary strategies.

But the surprise finding related to his epigenetics, the system of chemical tags, or DNA methylation, that regulates gene activity. Young animals tend to maintain relatively consistent methylation patterns between cells, coordinating which genes switch on or off. With age, some of these marks disappear while others attach chaotically, making gene activity less predictable.

Clark calls this growing disorganization entropy. As expected, Jonathan’s epigenome showed higher overall entropy than his younger peers. But some genes were spared.

Those involved in mitochondrial function kept a surprisingly youthful profile, comparable to that of a five-year-old Aldabra. Mitochondria, the cell’s energy producers, are closely linked to aging and age-related diseases. Maintaining mitochondrial health may have helped fuel DNA repair systems and warded off disarray in the epigenome—potentially creating a virtuous cycle to keep disorganization in check.

The findings echo genetic research on a woman who lived past 117. Although she harbored clusters of mutated blood cells and signs of DNA damage, her mitochondria appeared healthier than those of women decades younger.

How genes behave is just one piece of the aging picture. Diet, exercise, environment, and mental health all play roles. Jonathan mostly lived a life of luxury, with abundant fresh food, a safe home, and his veterinarian, Hollis, close at hand.

These comforts may have contributed to his longevity. But the results point to potential biological pathways that the body uses to stay spry for longer. The team plans to map the genomes of more Aldabra tortoises and other long-lived creatures. By studying these outliers, we may better grasp our own longevity and learn how to fight off age-related diseases.

“Our goal is to take these evolutionary insights and immediately translate them into practical, affordable treatments for everyday people,” said Clark.

The post The Oldest Living Land Animal Is 194. DNA May Now Reveal the Secret to His Longevity. appeared first on SingularityHub.

Cancer interception aims to prevent abnormal growths from developing into tumors.

Genetic mutations cause cancer. This idea has long driven strategies that aim to lower risk or determine when to look for early signs of disease. Don’t smoke. Minimize alcohol. Get vaccinated against viruses linked to cervical or liver cancer. Lifestyle changes can reduce the chances our cells accumulate DNA mutations that make them grow out of control.

But we might be missing part of the picture. Some people with cancer-related mutations never develop the disease. Others live healthy, have no family history, and suddenly learn they have cancer—and it’s already spread.

To Miriam Merad at Mount Sinai in New York, inflammation is the other half of the puzzle. Cells that acquire mutations and begin to take on cancer-like traits sometimes remain benign, likely because the immune system keeps them in check. By amping up the body’s own surveillance in people with abnormal growths—before they turn malignant—it might be possible to stop tumors from developing in the first place.

The new field, called cancer interception, is gaining steam. Clinical trials for lung cancer are in the works. Cancer vaccines, which train the immune system to destroy abnormal cells, have shown early promise. In one trial, roughly 90 percent of participants with pancreatic abnormalities generated immune cells better able to recognize mutated cells.

Other researchers are taking the idea further. In one study, they gave mice with precancerous pancreatic growths an approved drug to ward off the growths’ transformation into cancer, reshaping the immune landscape and extending survival.

“Yes, those mutations absolutely contribute to dysregulation of the cell that will become cancerous,” said Merad in an interview with Nature. But that’s not enough. The realization that inflammation is somehow “necessary for cancer initiation suggests that we can intervene.”

The Life of a Cancer

Cells don’t turn cancerous overnight. This is what makes the disease so insidious. With aging, environmental hazards, or simply replication, cells constantly pick up DNA changes. Most mutations are relatively harmless.

Occasionally, however, a mutation hits a driver gene that gives a cell an advantage over its neighbors. A gene called KRAS, for example, normally encourages growth. But mutated versions become stuck in the “on” position, continuously ordering cells to divide. Another gene, P53, often dubbed the “protector of the genome,” can lose its ability to eliminate damaged cells. And when DNA-repair genes go awry, mutations can accumulate even faster.

A driver mutation doesn’t necessarily make a cell cancerous. Evolutionarily, it gives the cell a leg up. Armed with the ability to divide and resist death, its descendants gradually take over the cellular neighborhood, competing with healthy cells for space, nutrients, and survival signals. Rapid expansion also means the cells pick up more mutations in a dangerous feedback cycle.

Eventually, some gain the genetic and molecular signatures of a precancerous cell. Most of these remain harmless for decades. Others rewire their surroundings, setting up a hostile environment that blocks immune cell surveillance and boosts inflammation, eventually allowing them to spread uncontrollably.

Researchers aim to halt that journey with cancer interception.

Going on Offense

For years, scientists have believed cancer causes local immune system changes. When they reach a tumor, some immune cells go rogue. Macrophages, rather than literally eating up the enemy, are reprogrammed by the tumor microenvironment to dampen other immune cells.

This process may start earlier than expected. Last year, Merad and colleagues found that tumors send cues to bone marrow precursor cells, nudging them towards a pro-cancer macrophage state. By the time they reach tumors, the protectors have already turned to the dark side.

“This work changes the way we think about the timing of immune suppression in cancer,” said study author Samarth Hegde in a press release. “We need strategies to intervene much earlier, while these cells are still developing, so we can stop them from becoming cancer’s allies in the first place.”

The team found one protein, NRF2, acted as a switch. Inflammation from the tumor environment changed the protein’s function, such that it encouraged nascent macrophages to become immune-suppressive. Blocking NRF2, either by disabling its gene or with drugs, led to a stronger immune attack against early lung cancer in mice.

The results inspired a new way to fight cancer. In addition to attacking a tumor, treatments should also work to change the environment that helps it grow.

Hegde and collaborators are now testing the idea with an existing drug from Novartis. Originally developed for heart and blood vessel disease, the drug, canakinumab, surprisingly slashed lung cancer incidence and death by over 67 percent in an earlier trial cohort of roughly 10,000. The unexpected findings inspired researchers to study inflammation as a driver of cancer.

In mice genetically engineered to develop lung cancer, blocking the same inflammatory pathway similarly reduced eventual tumors and shifted the lungs’ immune environment towards a stronger attack on budding tumors. And a new study found other potential targets for interception in immune cell networks as precancerous lung cells developed.

Merad’s team, alongside collaborators, are launching the Can-Prevent-Lung trial to test canakinumab in people with persistent, high-risk lung growths. CT scans can spot these abnormalities, but not all will become cancers. Figuring out who actually needs the treatment is an open question.

Medications have side effects, and “we never give a drug to someone that doesn’t need it,” said Merad.

Other teams are grappling with a similar dilemma. One solution may be catching cancer earlier. The PRECEDE trial aims to monitor over 20,000 people with a family history of pancreatic cancer or abnormal growths to see if frequent checkups improve survival. Like lung cancer, pancreatic cancer is notoriously dangerous. The diseased cells are often highly invasive, avoid most treatments, and cause vague symptoms that make diagnosis difficult.

The team plans to explore whether a blockbuster pancreatic drug, daraxonrasib, currently approved only for advanced cancer, can boost survival when given after pre-cancer growths are removed. They’ll also test whether AI can spot tiny abnormalities invisible to the naked eye in medical scans, flagging people who might benefit from interception.

Although experts are excited about the tactic, a thorny question remains: Will people take powerful drugs when they don’t actually have cancer? Daraxonrasib, for example, can cause rashes so severe that some patients stopped taking it, along with other side effects.

The best candidates may be identified by a marker often missed during checkups. We never monitor inflammation levels, said Merad. Her team is now evaluating a blood test and developing cheaper, easier strategies for cancer screening. Better risk prediction might help doctors decide whether to intercept cancer or leave benign growths alone, opening the treatment philosophy to more people.

It won’t be easy, but to Merad, the future is bright. “It’s an extraordinary time of optimism and possibilities,” she said. From biotechnology advancements to AI that can integrate biochemical and imaging datasets, “it is impossible not to be hopeful.”

The post Radical New Tactic Would Stop Deadly Cancers Before They Start appeared first on SingularityHub.

The technology could one day decode dreams—or reveal our most private thoughts.

Brain scans contain clues about what someone is looking at, but turning those signals back into recognizable images is difficult. Now, researchers in Israel have built an AI system that recreates what people are looking at with uncanny accuracy.

Functional MRI scans, which track blood flow in the brain, are a common tool for decoding what is going on in people’s minds. Previous research has shown they can reveal neural activity linked to what a person is seeing, and scientists have long been interested in using AI to convert these signals into a visual representation of the scene in front of them.

So far, this has proved difficult, with early attempts producing blurry images and AI image generators inventing details that bear little resemblance to what the person saw. But now, a team at the Weizmann Institute of Science has developed a system called Brain-IT that sets a new bar for accuracy and can be adapted to a new person using just an hour of scan data.

“All in all, really we outperformed the others by a significant margin,” Michal Irani, who led the research, told MIT Technology Review. She adds that the team hopes the tool could enable new discoveries about how the brain works and even help paralyzed people communicate.

Existing methods for reconstructing images from brain scans can look good, but they often get important details like the position or color of objects wrong, thanks largely to AI image generators’ willingness to fill gaps in the data with what they expect to see.

The success of the new system, which was presented at this year’s International Conference on Learning Representations, is down to a novel design choice. Rather than trying to translate the brain signals in one shot, it uses two neural networks to separately predict an image’s layout and its contents. The outputs from these models are then fed into an image generation model that produces a detailed picture.

The team trained the system on a publicly available dataset in which eight volunteers each viewed around 9,000 photographs in a high-resolution brain scanner. The researchers also used a previously developed brain encoder that predicts how the brain would respond to a given image to simulate scans for around 120,000 extra photographs.

When tested on data from four of the volunteers, Brain-IT outperformed ten other reconstruction methods on seven out of eight standard measures of image similarity. In particular, it did much better at capturing the layout of a scene—where objects sit, as well as their colors and shapes.

The system can also be adapted quickly to work with new people because the way it processes brain scans is built around groups of brain cells whose functions seem to be shared across individuals. Calibrating it for someone new largely comes down to working out where those groups sit in their brain.

With one hour of scan data from a new volunteer, the system matched the results of previous methods trained on a full 40 hours, which could make the approach far more practical for researchers. “None of us can afford 40 hours of imaging for a new subject,” Tommy Sprague, a neuroscientist at the University of California, Santa Barbara, told MIT Technology Review.

But the technology’s ability to extract information about what someone is thinking also raises concerns around mental privacy, Sprague added. The reliance on fMRI, which requires a willing subject to lie still inside a scanner, currently limits misuse, but Irani and others are working on decoding brain activity from simpler devices like EEG, which records electrical signals using electrodes on the scalp.

While Irani acknowledges the potential risks, she’s trying to focus on the positive potential of the technology. In the future, she hopes to move beyond still images to capturing video and even audio, which could help decode imagined scenes and dreams. “That’s something we don’t have yet,” she says. “But we’re striving to achieve it.”

True mind reading may still be some way off. But due to the power of AI, it looks increasingly likely that our innermost thoughts will no longer be as secure as we’ve imagined.

The post This AI Reconstructs What You’re Looking at From a Brain Scan appeared first on SingularityHub.

Artificial Intelligence

‘Breathtaking,’ ‘Devastating’: Mathematics Reels After New OpenAI ReleaseSiobhan Roberts | The New York Times ($)

“Talking to mathematicians after the release, it is clear that it is a highly conflicting environment, motivating a wild range of reactions. …’The solutions announcement, [Dr. Bridson] added, ‘will be exciting for many, frightening for others, and devastating for some. ‘In all cases,’ he said, ‘the struggle to wrestle human understanding from the machines’ formalities will unleash greater ambition for what we achieved (working with AI agents) in this new era of mathematics.'”

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