Blog

CORVALLIS, Ore. (KVAL) — Oregon State University (OSU) researchers are advancing efforts to equip dog-like robots for scientific exploration on Mars, following a series of experiments conducted this month at White Sands National Park, OSU shared in a news release on Thursday.

The park serves as a Mars analog environment, where scientists are testing scenarios to inform future Mars operations involving astronauts, quadruped robots, rovers, and Mission Control on Earth.

Cristina Wilson, a robotics researcher at OSU, emphasized the commitment to deploying quadrupeds on the Moon and Mars.

“It’s the next frontier and takes advantage of the unique capabilities of legged robots,” she said.

The NASA-funded project, part of the Moon to Mars program, aims to develop tools for long-term lunar exploration and future crewed missions to Mars. The Legged Autonomous Surface Science in Analog Environments (LASSIE) Project includes experts from several universities and NASA Johnson Space Center.

This month’s field work at White Sands marked the team’s second visit to the park, following an initial trip in 2023. The team also conducted experiments on Mount Hood in Oregon, simulating lunar landscapes. During these sessions, scientists collected data from the quadruped robots’ feet to measure mechanical responses to foot-surface interactions.

In the same way that the human foot standing on ground can sense the stability of the surface as things shift, legged robots are capable of potentially feeling the exact same thing,” Wilson said. “So each step the robot takes provides us information that will help its future performance in places like the Moon or Mars.”

Despite challenging conditions, including triple-digit temperatures, the team made significant progress. For the first time, the robot acted autonomously, making its own decisions, which is crucial for independent operation alongside astronauts on Mars.

The team also tested advancements in the robot’s movement based on surface conditions, potentially increasing energy efficiency. “There is certainly a lot more research to do, but these are important steps in realizing the goal of sending quadrupeds to the Moon and Mars,” Wilson said.

The research is funded by NASA’s Planetary Science and Technology through Analog Research (PSTAR) program and Mars Exploration Program.

Other leaders of the project include Feifei Qian, USC; Ryan Ewing and Kenton Fisher, NASA Johnson Space Center; Marion Nachon, Texas A&M; Frances Rivera-Hernández, Georgia Tech; Douglas Jerolmack and Daniel Koditschek, University of Pennsylvania; and Thomas Shipley, Temple University.

New research, published today in the journal Science, shows how lysosomes – organelles that act like cells’ waste disposal system – respond to stress by becoming abnormally bloated, a process called lysosomal vacuolation that is associated with numerous diseases. 

Essential for cellular health, well-functioning lysosomes are also linked with healthy aging, so better understanding of the steps involved in vacuolation could eventually inform new therapies to treat diseases or promote healthy aging, according to senior author Jay Xiaojun Tan, Ph.D., assistant professor in the Department of Cell Biology at the University of Pittsburgh School of Medicine and member of the Aging Institute, a partnership between Pitt and UPMC. He elaborates on the study findings in the following Q&A. 

What is a vacuole, and what is lysosomal vacuolation? 

A vacuole is a membrane-bound compartment inside cells, like a water balloon, that stores water, molecules or waste. In plant cells, the central vacuole is large and helps store nutrients, regulate pressure and maintain structural rigidity. Animal cells don’t usually have vacuoles, but they contain related compartments called lysosomes. Lysosomal vacuolation refers to a condition in which lysosomes become abnormally enlarged like overinflated balloons, resembling plant vacuoles. 

What role do lysosomes play in cells, and how does lysosomal vacuolation affect their function? 

Lysosomes are essential for cell health. Like a waste disposal system, they digest damaged proteins, worn-out parts and invading microbes. By degrading a broad range of macromolecules, lysosomes preserve cellular function and longevity. Lysosomal vacuolation is thought to be an indication of stress or dysfunction of lysosomes, and these vacuoles are found in a large spectrum of medical conditions, including lysosomal storage disorders, aging, infection, chemotherapy, cataracts, cadmium toxicity, prion diseases and other neurodegenerative conditions such as Parkinson’s and Alzheimer’s disease. 

Why are you interested in understanding lysosomal stress? 

Since lysosomes are “longevity-promoting” organelles, we are particularly interested in processes that could enhance lysosomal integrity and activity. One of my lab’s guiding visions in recent years is that mild lysosomal stress may actually be beneficial by triggering adaptive responses that improve lysosomal quality and function. We are exploring how cells respond to various forms of lysosomal stress, with the hope that some of these protective mechanisms can eventually be harnessed to promote human health and longevity. 

What was the goal of this study? 

Lysosomal vacuolation has been observed in many diseases and has puzzled scientists for decades. However, we still don’t know whether it is harmful or beneficial, largely because the mechanisms behind vacuole formation remain poorly understood. Without a molecular handle, it has not been possible to selectively remove these swollen lysosomes in order to test their physiological and pathological roles. Our goal was to uncover the underlying mechanism of lysosomal vacuolation – an advance that now allows us to investigate what these vacuoles actually do in disease and whether targeting the vacuolation process could offer therapeutic potential for disease treatment or healthy aging. 

What were the main findings? 

We found that cells have a well-developed system to drive lysosomal vacuolation. In response to many different types of stress, lysosomes become filled up with solutes, which draws in water and stretches the lysosomal membrane – like inflating a balloon. The potential risk of lysosomal rupture is detected by a protein we named LYVAC, or lysosomal vacuolator. LYVAC attaches to these stressed lysosomes, where it delivers lipids, which serve as membrane building blocks to allow lysosomal expansion in a controlled way. This process of lysosomal vacuolation is a natural, highly regulated response. LYVAC plays a central role in this process, helping cells adapt to stress and maintain lysosomal stability. 

Were you surprised by any of the results? 

Yes! We were especially surprised to find that lysosomal swelling isn’t just a passive defect of the cell – it’s actually a tightly controlled process. The cell seems to know exactly which lysosomes are in trouble and sends the protein LYVAC to help only those. LYVAC does two things: it sticks to the stressed lysosome and then helps move lipids to it so the membrane can grow and form a vacuole. Both steps need two separate signals to happen, which keeps everything precise and prevents LYVAC from acting on healthy parts of the cell. We didn’t expect this kind of accuracy, and it was really exciting to see. 

What are the implications of these findings? 

By targeting LYVAC, we can begin to understand the exact roles that lysosomal vacuoles play in different diseases. If vacuole formation turns out to be a key driver of disease, then blocking LYVAC could offer a promising new treatment strategy. 

What’s next for this research? 

We’re continuing to explore how LYVAC is controlled – what turns it on, and how it recognizes which lysosomes are under stress. We’re also testing whether LYVAC is helpful or harmful in a genetic model of neurodegeneration, where large lysosomal vacuoles naturally appear. Our goal is to find ways to adjust how lipids move into lysosomes, either to prevent harmful swelling or to help lysosomes recover in stressed or diseased cells. This research builds on our previous discovery of the PITT pathway, which showed how cells can rapidly repair lysosomes after damage. Together, these studies suggest that cells have multiple lipid-based systems to respond to different types of lysosomal stress. By better understanding these processes, we hope to uncover new strategies to protect cells and promote healthy aging. 

Other authors on the study were Haoxiang Yang, Jinrui Xun, M.D., Awishi Mondal, M.S., Bo Lv, Ph.D., and Simon Watkins, Ph.D., all of Pitt and UPMC; and Yajuan Li, Ph.D., and Lingyan Shi, Ph.D., both of the University of California San Diego. 

This research was supported by the National Institutes of Health (1K01AG075142, R35GM150506, R01NS111039, R01 GM149976, R21NS125395 and U01AI167892), start-up funding from the Aging Institute and a UPMC Competitive Medical Research Fund award. 

When a disease-causing virus or other organism is transmitted from one species to another, most of the time the infection sputters and dies out. On rare occasions, the infection can perpetuate transmission in the new host species and cause a pandemic. For example, scientists are keeping a close eye on H5N1 highly pathogenic avian influenza, which causes bird flu and has been found in cows and humans. But is there a way to anticipate when infections will die out on their own and when they will persist?

New research, led by scientists at Penn State and the University of Minnesota Duluth, identified certain characteristics that could help predict whether the pathogen will stick around. Understanding how a virus spreads and what influences its spread soon after it spills over to a new population could provide information to help stop new diseases from spreading, the team said.

The study was published today (Aug. 21) in the journal PLOS Biology.

Pandemic prevention efforts largely focus on identifying the next pandemic pathogen, but that’s like finding a needle in the haystack. This work helps us figure out which outbreaks to worry about so that we can direct our public health resources where they need to go to prevent and respond to disease emergence.”

David Kennedy, associate professor of biology at Penn State and senior author on the paper

While pandemics are extremely rare, spillover events – where viruses move between different host species – happen all the time, according to the research team. With so much viral transmission occurring, it’s nearly impossible for scientists to pinpoint which spillover events to pay attention to.

“We wanted to know if there is anything we can measure directly after a spillover event or if there are characteristics of a spillover event that would be predictive of whether the virus would or would not persist in a new population,” said Clara Shaw, lead author of the study. Shaw was a postdoctoral scholar in biology at Penn State at the time the research was conducted and is now assistant professor of biology at the University of Minnesota Duluth.

The researchers studied viral spillover in a worm model system, which allowed the team to examine disease transmission and emergence at a population level rather than within individual animals, Shaw said. They studied eight strains of worms that belong to seven species of the Caenorhabditis nematode, a model system for disease that shares a large number of genes with humans.

To induce a spillover event, the worms were exposed to Orsay virus, a nematode virus. The species of worms assessed in the study are at least partially susceptible to Orsay virus but vary in their ability to transmit it. The worm populations reproduced and grew for between five to 13 days. Then, the researchers transferred 20 adult worms to a new, virus-free Petri dish where the worms could reproduce and grow again. They repeated this process, transferring worms to new Petri dishes up to 10 times or until the virus was no longer detected in the worms.

The researchers then measured specific traits of the population of worms remaining on the initial plate – what fraction of the population is infected; how much virus is inside of each infected worm; how much virus do they shed; and how susceptible are they to the virus? Using mathematical models, the scientists looked at each trait individually and then together to determine if any of the characteristics were linked to virus emergence as the worms were transferred to new plates.

The researchers found that the dynamics of how the virus spreads during the few days after transmission are important for predicting long-term viral persist. For example, three factors were all positively correlated with whether a virus will take off in the new host population – infection prevalence or the fraction of the exposed population that’s infected; viral shedding or the ability to release copies of the virus into the environment; and infection susceptibility or how vulnerable the hosts are to the virus.

Infection prevalence and viral shedding were of particular significance, the researchers said. More than half of the differences seen in whether the virus persists in the worms can be linked to these characteristics that were detected in the initial plate.

“That means these early traits can actually tell us quite a bit about what’s going to happen way off in the future,” Kennedy said.

The researchers also found infection intensity, or the severity of the infection, did not predict virus persistence.

The researchers said they plan to build on this work. Next, they will explore how pathogens adapt to new hosts to understand the evolutionary changes that occur at the genetic level. For instance, Kennedy said they’re interested in understanding what genetic changes allowed the pathogen to persist and when those changes occurred.

Funding from the U.S. National Science Foundation supported this work.

The Chan Zuckerberg Initiative announced Thursday the launch of rBio, the first artificial intelligence model trained to reason about cellular biology using virtual simulations rather than requiring expensive laboratory experiments — a breakthrough that could dramatically accelerate biomedical research and drug discovery.

The reasoning model, detailed in a research paper published on bioRxiv, demonstrates a novel approach called “soft verification” that uses predictions from virtual cell models as training signals instead of relying solely on experimental data. This paradigm shift could help researchers test biological hypotheses computationally before committing time and resources to costly laboratory work.

“The idea is that you have these super powerful models of cells, and you can use them to simulate outcomes rather than testing them experimentally in the lab,” said Ana-Maria Istrate, senior research scientist at CZI and lead author of the research, in an interview. “The paradigm so far has been that 90% of the work in biology is tested experimentally in a lab, while 10% is computational. With virtual cell models, we want to flip that paradigm.”

How AI finally learned to speak the language of living cells

The announcement represents a significant milestone for CZI’s ambitious goal to “cure, prevent, and manage all disease by the end of this century.” Under the leadership of pediatrician Priscilla Chan and Meta CEO Mark Zuckerberg, the $6 billion philanthropic initiative has increasingly focused its resources on the intersection of artificial intelligence and biology.

rBio addresses a fundamental challenge in applying AI to biological research. While large language models like ChatGPT excel at processing text, biological foundation models typically work with complex molecular data that cannot be easily queried in natural language. Scientists have struggled to bridge this gap between powerful biological models and user-friendly interfaces.

“Foundation models of biology — models like GREmLN and TranscriptFormer — are built on biological data modalities, which means you cannot interact with them in natural language,” Istrate explained. “You have to find complicated ways to prompt them.”

The new model solves this problem by distilling knowledge from CZI’s TranscriptFormer — a virtual cell model trained on 112 million cells from 12 species spanning 1.5 billion years of evolution — into a conversational AI system that researchers can query in plain English.

The ‘soft verification’ revolution: Teaching AI to think in probabilities, not absolutes

The core innovation lies in rBio’s training methodology. Traditional reasoning models learn from questions with unambiguous answers, like mathematical equations. But biological questions involve uncertainty and probabilistic outcomes that don’t fit neatly into binary categories.

CZI’s research team, led by Senior Director of AI Theofanis Karaletsos and Istrate, overcame this challenge by using reinforcement learning with proportional rewards. Instead of simple yes-or-no verification, the model receives rewards proportional to the likelihood that its biological predictions align with reality, as determined by virtual cell simulations.

“We applied new methods to how LLMs are trained,” the research paper explains. “Using an off-the-shelf language model as a scaffold, the team trained rBio with reinforcement learning, a common technique in which the model is rewarded for correct answers. But instead of asking a series of yes/no questions, the researchers tuned the rewards in proportion to the likelihood that the model’s answers were correct.”

This approach allows scientists to ask complex questions like “Would suppressing the actions of gene A result in an increase in activity of gene B?” and receive scientifically grounded responses about cellular changes, including shifts from healthy to diseased states.

Beating the benchmarks: How rBio outperformed models trained on real lab data

In testing against the PerturbQA benchmark — a standard dataset for evaluating gene perturbation prediction — rBio demonstrated competitive performance with models trained on experimental data. The system outperformed baseline large language models and matched performance of specialized biological models in key metrics.

Particularly noteworthy, rBio showed strong “transfer learning” capabilities, successfully applying knowledge about gene co-expression patterns learned from TranscriptFormer to make accurate predictions about gene perturbation effects—a completely different biological task.

“We show that on the PerturbQA dataset, models trained using soft verifiers learn to generalize on out-of-distribution cell lines, potentially bypassing the need to train on cell-line specific experimental data,” the researchers wrote.

When enhanced with chain-of-thought prompting techniques that encourage step-by-step reasoning, rBio achieved state-of-the-art performance, surpassing the previous leading model SUMMER.

From social justice to science: Inside CZI’s controversial pivot to pure research

The rBio announcement comes as CZI has undergone significant organizational changes, refocusing its efforts from a broad philanthropic mission that included social justice and education reform to a more targeted emphasis on scientific research. The shift has drawn criticism from some former employees and grantees who saw the organization abandon progressive causes.

However, for Istrate, who has worked at CZI for six years, the focus on biological AI represents a natural evolution of long-standing priorities. “My experience and work has not changed much. I have been part of the science initiative for as long as I have been at CZI,” she said.

The concentration on virtual cell models builds on nearly a decade of foundational work. CZI has invested heavily in building cell atlases — comprehensive databases showing which genes are active in different cell types across species — and developing the computational infrastructure needed to train large biological models.

“I’m really excited about the work that’s been happening at CZI for years now, because we’ve been building up to this moment,” Istrate noted, referring to the organization’s earlier investments in data platforms and single-cell transcriptomics.

Building bias-free biology: How CZI curated diverse data to train fairer AI models

One critical advantage of CZI’s approach stems from its years of careful data curation. The organization operates CZ CELLxGENE, one of the largest repositories of single-cell biological data, where information undergoes rigorous quality control processes.

“We’ve generated some of the flagship initial data atlases for transcriptomics, and those were generated with diversity in mind to minimize bias in terms of cell types, ancestry, tissues, and donors,” Istrate explained.

This attention to data quality becomes crucial when training AI models that could influence medical decisions. Unlike some commercial AI efforts that rely on publicly available but potentially biased datasets, CZI’s models benefit from carefully curated biological data designed to represent diverse populations and cell types.

Open source vs. big tech: Why CZI is giving away billion-dollar AI technology for free

CZI’s commitment to open-source development distinguishes it from commercial competitors like Google DeepMind and pharmaceutical companies developing proprietary AI tools. All CZI models, including rBio, are freely available through the organization’s Virtual Cell Platform, complete with tutorials that can run on free Google Colab notebooks.

“I do think the open source piece is very important, because that’s a core value that we’ve had since we’ve started CZI,” Istrate said. “One of the main goals for our work is to accelerate science. So everything we do is we want to make it open source for that purpose only.”

This strategy aims to democratize access to sophisticated biological AI tools, potentially benefiting smaller research institutions and startups that lack the resources to develop such models independently. The approach reflects CZI’s philanthropic mission while creating network effects that could accelerate scientific progress.

The end of trial and error: How AI could slash drug discovery from decades to years

The potential applications extend far beyond academic research. By enabling scientists to quickly test hypotheses about gene interactions and cellular responses, rBio could significantly accelerate the early stages of drug discovery — a process that typically takes decades and costs billions of dollars.

The model’s ability to predict how gene perturbations affect cellular behavior could prove particularly valuable for understanding neurodegenerative diseases like Alzheimer’s, where researchers need to identify how specific genetic changes contribute to disease progression.

“Answers to these questions can shape our understanding of the gene interactions contributing to neurodegenerative diseases like Alzheimer’s,” the research paper notes. “Such knowledge could lead to earlier intervention, perhaps halting these diseases altogether someday.”

The universal cell model dream: Integrating every type of biological data into one AI brain

rBio represents the first step in CZI’s broader vision to create “universal virtual cell models” that integrate knowledge from multiple biological domains. Currently, researchers must work with separate models for different types of biological data—transcriptomics, proteomics, imaging—without easy ways to combine insights.

“One of our grand challenges is building these virtual cell models and understanding cells, as I mentioned over the next couple of years, is how to integrate knowledge from all of these super powerful models of biology,” Istrate said. “The main challenge is, how do you integrate all of this knowledge into one space?”

The researchers demonstrated this integration capability by training rBio models that combine multiple verification sources — TranscriptFormer for gene expression data, specialized neural networks for perturbation prediction, and knowledge databases like Gene Ontology. These combined models significantly outperformed single-source approaches.

The roadblocks ahead: What could stop AI from revolutionizing biology

Despite its promising performance, rBio faces several technical challenges. The model’s current expertise focuses primarily on gene perturbation prediction, though the researchers indicate that any biological domain covered by TranscriptFormer could theoretically be incorporated.

The team continues working on improving the user experience and implementing appropriate guardrails to prevent the model from providing answers outside its area of expertise—a common challenge in deploying large language models for specialized domains.

“While rBio is ready for research, the model’s engineering team is continuing to improve the user experience, because the flexible problem-solving that makes reasoning models conversational also poses a number of challenges,” the research paper explains.

The trillion-dollar question: How open source biology AI could reshape the pharmaceutical industry

The development of rBio occurs against the backdrop of intensifying competition in AI-driven drug discovery. Major pharmaceutical companies and technology firms are investing billions in biological AI capabilities, recognizing the potential to transform how medicines are discovered and developed.

CZI’s open-source approach could accelerate this transformation by making sophisticated tools available to the broader research community. Academic researchers, biotech startups, and even established pharmaceutical companies can now access capabilities that would otherwise require substantial internal AI development efforts.

The timing proves significant as the Trump administration has proposed substantial cuts to the National Institutes of Health budget, potentially threatening public funding for biomedical research. CZI’s continued investment in biological AI infrastructure could help maintain research momentum during periods of reduced government support.

A new chapter in the race against disease

rBio’s launch marks more than just another AI breakthrough—it represents a fundamental shift in how biological research could be conducted. By demonstrating that virtual simulations can train models as effectively as expensive laboratory experiments, CZI has opened a path for researchers worldwide to accelerate their work without the traditional constraints of time, money, and physical resources.

As CZI prepares to make rBio freely available through its Virtual Cell Platform, the organization continues expanding its biological AI capabilities with models like GREmLN for cancer detection and ongoing work on imaging technologies. The success of the soft verification approach could influence how other organizations train AI for scientific applications, potentially reducing dependence on experimental data while maintaining scientific rigor.

For an organization that began with the audacious goal of curing all diseases by the century’s end, rBio offers something that has long eluded medical researchers: a way to ask biology’s hardest questions and get scientifically grounded answers in the time it takes to type a sentence. In a field where progress has traditionally been measured in decades, that kind of speed could make all the difference between diseases that define generations—and diseases that become distant memories.

Daily insights on business use cases with VB Daily

If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI.

Read our Privacy Policy

Thanks for subscribing. Check out more VB newsletters here.

An error occured.