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KAIST Proposes AI-Driven Strategy to Solve Long-Standing Mystery of Gene Function <(From Left) Distinguisehd Professor Sang Yup Lee, Dr. Gi Bae Kim, Professor Bernhard O. Palsson> “We know the genes, but not their functions.” To resolve this long-standing bottleneck in microbial research, a joint research team has proposed a cutting-edge research strategy that leverages Artificial Intelligence (AI) to drastically accelerate the discovery of microbial gene functions. KAIST announced on January 12th that a research team led by Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering, in collaboration with Professor Bernhard Palsson from the Department of Bioengineering at UCSD, has published a comprehensive review paper. The study systematically analyzes and organizes the latest AI-based research approaches aimed at revolutionizing the speed of gene function discovery. Since the early 2000s, when whole-genome sequencing became a reality, there were high expectations that the genetic blueprint of life would be fully decoded. However, even twenty years later, the roles of a significant portion of genes within microbial genomes remain unknown. While various experimental methods—such as gene deletion, analysis of gene expression profiles, and in vitro activity assays—have been employed, discovering gene functions remains a time-consuming and costly endeavor. This is primarily due to the limitations of large-scale experimentation, complex biological interactions, and the discrepancy between laboratory results and actual in vivo responses. To overcome these hurdles, the research team emphasized that an AI-driven approach combining computational biology with experimental biology is essential. In this paper, the team provides a comprehensive overview of computational biology approaches that have facilitated gene function discovery, ranging from traditional sequence similarity analysis to the latest deep-learning-based AI models. Notably, 3D protein structure prediction technologies such as AlphaFold (developed by Google DeepMind) and RoseTTAFold (developed by the University of Washington) have opened new doors. These tools go beyond simple functional estimation, offering the potential to understand the underlying mechanisms of how gene functions operate. Furthermore, generative AI is now extending research boundaries toward designing proteins with specifically desired functions. Focusing on transcription factors (proteins that act as genetic switches) and enzymes (proteins that catalyze chemical reactions), the team presented various application cases and future research directions that integrate gene sequence analysis, protein structure prediction, and diverse metagenomic analyses. <Schematic illustration of computational biology methods for enzyme function prediction>
2026.01.12 View 373
Jaewook Myung, First Korean Selected as '40 Under 40 Recognition Program' Next Generation Environmental Engineering Leader < Professor Jaewook Myung of KAIST Department of Civil and Environmental Engineering > KAIST announced on December 12th that Professor Jaewook Myung of the Department of Civil and Environmental Engineering was selected as the first Korean recipient of the '40 Under 40 Recognition Program' for Next Generation Environmental Engineering Leaders, organized by the American Academy of Environmental Engineers and Scientists (AAEES). < The '40 Under 40 Recognition Program' is an international award program selecting next-generation leaders in the field of Environmental Engineering and Science > This award is presented annually by AAEES to select next-generation environmental engineering researchers who demonstrate innovative research achievements, social contribution, and educational leadership. Professor Myung's selection is particularly significant as he is the first Korean to be chosen since the program's inception. The award ceremony is scheduled to be held in Washington D.C. in April 2026. AAEES is the world's highest-authority professional organization leading the global environmental engineering sector through operating the Professional Environmental Engineer (PEE) certification system, policy consultation, and international academic exchange. This award is highly regarded for greatly enhancing the international standing of domestic environmental engineering and sustainability research. Amid the deepening problems of plastic waste increase and greenhouse gas emissions, where existing technologies are showing limitations in providing solutions, Professor Jaewook Myung has garnered significant attention from academia and industry by developing technology to convert greenhouse gases such as methane ($CH_4$) and carbon dioxide ($CO_2$) into biodegradable plastics. His research is highly praised for presenting a new industrial paradigm that fuses environmental microbiology and materials science to convert greenhouse gases into high-value bio-materials. Professor Myung's research team secured microbial metabolic control technology to transform greenhouse gases into materials, an accelerated process that simultaneously enhances the synthesis and decomposition efficiency of plastics, and pilot process design and engineering technology applicable in industrial settings. This established a sustainable circular technology model capable of simultaneously addressing greenhouse gas reduction and plastic pollution issues. Furthermore, the research team expanded these foundational technologies to develop various application products, such as biodegradable coating materials that naturally decompose in the ocean, biocompatible bio-based electronic materials, and industrial 3D printing filaments, realizing full-cycle innovation from basic research to application and industrialization. These achievements are recognized as world-class sustainable technology alternatives that can simultaneously overcome the problems of plastic downcycling and the economic limitations of greenhouse gas utilization technology. Professor Myung also shows excellent performance in nurturing talent. His advised students are growing into next-generation environmental and sustainability researchers, having won major awards both domestically and internationally, including the American Chemical Society (ACS) Environmental Chemistry Graduate Student Award, the Presidential Science Scholarship, the Merck Innovation Cup Prize, and the Republic of Korea Talent Award. He is also establishing himself as a leading researcher in the commercialization of sustainable technology by expanding his research achievements into the social and industrial ecosystem through technology collaboration with industries, patents, and consultation with public institutions. The AAEES Selection Committee evaluated Professor Jaewook Myung as "a researcher possessing technical excellence, social responsibility, and educational leadership, and an innovator who has pioneered new areas of environmental engineering." Professor Myung expressed his thoughts, saying, "This award is a result made possible by the students who researched and challenged alongside me and the collaborative research culture of KAIST," and added, "I will contribute to brightening the future of humanity and the planet through sustainable resource circulation technology."
2025.12.12 View 838
KAIST K HERO Rides Nuri Rocket, Next Generation Micro Hall Thruster Technology Verified in Space < (From left) Ph.D candidate Jaehong Park, COSMOVY researcher Yoonsoo Kim, Professor Wonho Choe, Ph.D candidate Dongha Park, M.S candidate Seungbeom Heo > KAIST announced on the November 26th that the CubeSat 'K-HERO (KAIST Hall Effect Rocket Orbiter)', developed by the research team of Professor Wonho Choe from the Department of Nuclear and Quantum Engineering, is scheduled to launch into space aboard the 4th Nuri rocket launch vehicle on November 27th from the Naro Space Center in Goheung, Jeollanam-do. This 4th Nuri launch is the first to be managed by the private company Hanwha Aerospace, which received technology transfer from the Korea Aerospace Research Institute (KARI), marking a significant milestone in the transformation of the domestic space industry. Along with the main payload, the Next-Generation Medium Satellite 3, twelve CubeSats developed by industry, academia, and research institutions will be onboard, with K-HERO being one of them. The development of K-HERO was officially initiated when Professor Wonho Choe's research team was selected as the basic satellite development team in the '2022 CubeSat Competition' organized by KARI. The basic satellite is a technology verification satellite designed to confirm whether the design and core components operate normally in the space environment before proceeding with the flight model (FM) production. K-HERO is a 3U standard CubeSat with dimensions of $10\text{ cm}$ (width) $\times$ $10\text{ cm}$ (length) $\times$ $30\text{ cm}$ (height) and a weight of $3.9\text{ kg}$. It was designed to satisfy all stability, electrical specifications, and interface conditions with the launch vehicle. The core mission of K-HERO is to directly verify the in-space operation of the 150 W class micro-satellite Hall thruster developed by the research team. The Hall thruster can be simply described as a 'space engine powered by electricity'. It is an electric propulsion engine that moves the satellite slowly but very efficiently using electricity. Instead of burning a lot of fuel to generate instantaneous thrust, like a rocket, it works by using electricity to turn gas (Xenon) into a plasma state and rapidly accelerating it backward to push the satellite forward. Hall thrusters are considered a core technology for the era of small and constellation satellites due to their high fuel efficiency. < Image of plasma generation in the micro-satellite Hall thruster mounted on the K-HERO CubeSat > Hall thrusters are already a proven technology, having been used in large satellites and deep-space probes for over 20-30 years. However, their size and power requirements were large, so in the past, they were mainly operated on large geostationary (GEO) communication/broadcasting satellites and used by NASA and ESA deep-space probes for long-distance flights. Recently, the emergence of the SpaceX Starlink satellite constellation has led to a surge in demand for small and micro electric thrusters. As the global space industry shifts towards satellite constellations, 'small and efficient thrusters' have become essential technology. K-HERO is the first case of direct in-space demonstration of a micro Hall thruster made with domestic technology, and it is expected to be an important milestone in enhancing domestic technological competitiveness. Professor Wonho Choe's research team began research on Hall thrusters in Korea in 2003, securing original technology based on plasma physics. In 2013, they successfully mounted a 200 W class Hall thruster on the 'KAIST Science and Technology Satellite 3,' proving its practical utility. This time, they have improved the design to operate even at a lower power of 30 W, developing a next-generation model aimed at micro-satellites. COSMOVY Inc, a laboratory startup founded by Professor Wonho Choe's research team, also participated in the development of K-HERO, further strengthening the foundation for technology commercialization. < K-HERO CubeSat being loaded into the Nuri rocket's CubeSat dispenser (Photo source: Korea Aerospace Research Institute) > Professor Wonho Choe stated, "Starting with K-HERO, the number of small satellites equipped with electric thrusters will increase significantly in Korea. The Hall thruster being verified this time can be utilized for various missions, including low-Earth orbit constellation surveillance and reconnaissance satellites, 6G communication satellites, very-low-Earth orbit high-resolution satellites, and asteroid probes." President Kwang Hyung Lee stated, "The launch of K-HERO is a significant opportunity to directly verify KAIST's electric propulsion technology on a micro-satellite platform once again in space, and it will be an important turning point that will further enhance the technological competitiveness of small satellites in Korea. KAIST will continue to contribute to the development of our country's space technology.
2025.11.26 View 1227
KAIST team links early life epigenetic memory to adult brain inflammation <(From left) Professor Won-Suk Chung, Ph.D. Ph.D candidate Hyeonji Park Dr. Seongwan Park, Professor Inkyung Jung> Why do some people remain healthy through childhood yet become more vulnerable to brain disorders such as dementia later in life? A KAIST (President Kwang Hyung Lee) -led team has uncovered a key part of the answer: a developmental ‘switch’ in astrocytes—the brain’s most abundant support cells that shapes how strongly the brain’s immune system reacts in adulthood. The study identifies a gene, NR3C1 (encoding the glucocorticoid receptor), as a master regulator of this switch and shows how early-life epigenetic ‘memory’ can predispose the adult brain to excessive inflammation. The work was carried out by a joint team led by Professor Inkyung Jung (Department of Biological Sciences, KAIST) and Associate Director Won-Suk Chung (Center for Vascular Research, Institute for Basic Science; Professor, KAIST Biological Sciences). Using mouse models, the researchers mapped gene-regulatory programs across multiple stages of astrocyte development and found that NR3C1 acts during a brief early-postnatal window to enforce long-term immune restraint. <The schematic illustrates how the NR3C1 gene (glucocorticoid receptor) suppresses the immune response of astrocytes. In normal (control) astrocytes, NR3C1 binds to specific regulatory regions of DNA (nGRE) to inhibit the expression of immune-related genes, thereby maintaining brain homeostasis even under immune stimulation. In contrast, in NR3C1-deficient astrocytes (KO), this suppression is lost, leading to excessive activation of inflammation-related genes such as Gfap, Il6st, Stat2, and Cxcl10. As a result, in an autoimmune encephalomyelitis (EAE) model, pronounced neuroinflammation and clinical symptoms (paralysis and severe debilitation) are observed> To build this map, the team combined state-of-the-art 3D epigenome profiling with RNA sequencing and chromatin accessibility analyses, capturing how DNA folds and which regulatory elements contact target genes. They identified 55 stage-specific transcription factors that guide astrocyte maturation; among them, NR3C1 emerged as the critical ‘switch’ in early life. Notably, deleting NR3C1 in astrocytes did not disrupt normal development. However, when the adult mice were challenged with an autoimmune model of multiple sclerosis, animals lacking astrocytic NR3C1 mounted exaggerated inflammatory responses and developed more severe disease. Mechanistically, the study shows that early loss of NR3C1 epigenetically primes immune genes - keeping their regulatory elements open and ready - so that later in life these genes respond too strongly to inflammatory cues. In effect, NR3C1 serves as an early ‘brake’ that prevents over-activation of astrocyte immune programs in adulthood. “This is the first demonstration that astrocyte immune functions are governed by epigenetic memory,” said Professor Won-Suk Chung. “Our findings offer new clues to the origins of degenerative brain disorders, including Alzheimer’s disease.” “We reveal a temporal regulatory window in astrocyte development that can set the stage for disease vulnerability in adulthood,” added Professor Inkyung Jung. “Understanding the 3D genome logic behind these programs could open paths to therapies for immune-related brain disorders such as multiple sclerosis.” <The figure shows the three-dimensional genome structure of astrocytes at specific gene loci, illustrating how NR3C1 regulates their expression. In normal cells, NR3C1 binds to DNA and maintains the chromatin in a closed state, thereby preventing unnecessary activation between distal regulatory elements (enhancers) and gene promoters. In contrast, when NR3C1 is absent, the chromatin becomes open, creating a state in which enhancers and genes can be more easily activated. As a result, genes such as Mxi1 are overexpressed, triggering inflammatory responses. This clearly demonstrates that NR3C1 plays an essential role in maintaining immune homeostasis by stabilizing three-dimensional gene regulatory mechanisms.> The results of this study were published online on September 22 in the international journal Nature Communications (IF 15.7), with Dr. Seongwan Park and PhD student Hyeonji Park of KAIST’s Department of Biological Sciences as co-first authors. ※ Paper title: “NR3C1-mediated epigenetic regulation suppresses astrocytic immune responses in mice,” DOI: https://www.nature.com/articles/s41467-025-64088-5 In addition, on September 17, the journal published a commentary article introducing this research: https://www.nature.com/articles/s41467-025-64102-w This research was supported by the Suh Kyungbae Science Foundation, the Ministry of Health and Welfare, the Ministry of Science and ICT, and IBS. Glossary - Epigenetic priming: preparing genes for rapid future activation by altering chromatin without changing DNA sequence
2025.09.25 View 2283
Simultaneous On and Off Gene Control with Gene Scissors <(From left to right) Dr. Soo Young Moon, KAIST Institute of Life Science,Professor Ju Young Lee, Graduate School of Engineering Biology (Adjunct Professor of Biological Sciences),Dr. Myung Hyun Noh, Korea Research Institute of Chemical Technology (KRICT),Researcher Nan-Yeong An, Department of Biological Sciences> Turning genes on and off is like flipping a light switch, controlling whether genes in a cell are active. When a gene is turned on, the production of proteins or other substances is promoted; when it's turned off, production is suppressed. Korean researchers have gone beyond the limitations of existing CRISPR technology, which focused primarily on "off" functions, by developing the world's first innovative system that can simultaneously turn genes on and off, opening a new paradigm for the synthetic biology-based bio-industry. A joint research team led by Professor Ju Young Lee of KAIST Graduate School of Biological Engineering (Adjunct Professor of Biological Sciences) and Dr. Myung Hyun Noh of the Korea Research Institute of Chemical Technology (KRICT), an organization under the National Research Council of Science & Technology (NST) , announced on the 21st that they have developed a new dual-mode CRISPR gene editing system that can simultaneously turn on and off desired genes in E. coli. E. coli is a representative microorganism that is easy to experiment with and can be directly applied to industrial uses. Meanwhile, CRISPR technology is considered one of the most innovative tools in 21st-century biotechnology. In particular, bacteria, which are the foundation of synthetic biology, have a simple structure and multiply rapidly, while also being able to produce a variety of useful substances. Therefore, gene activation in bacteria is a key technology for designing "microbial factories," and its industrial value is very high. The core of synthetic biology is to design the genetic circuits of living organisms like programming a circuit board to perform a desired function. Just as switches are turned on and off in an electronic circuit, a technology is needed to optimize metabolic pathways by activating certain genes while suppressing others. The dual-mode gene scissors developed by the research team are the key tool that enables this precise gene regulation. Existing CRISPR gene scissors were primarily specialized for the "off" function (repression) and were excellent at blocking gene expression, but their ability to turn genes on was very limited. Furthermore, for CRISPR to work, a specific DNA recognition sequence (PAM, protospacer adjacent motif) is required, and the narrow range of PAM recognition in existing systems limited the scope of genes that could be controlled. In addition, while CRISPR-based activation (CRISPRa) has been somewhat developed in eukaryotic cells (human, plant, and animal cells), there were limitations in bacteria where the "on" function did not work properly due to differences in their internal transcription regulation mechanisms. To overcome these limitations, the research team expanded the target range to access more genes and significantly improved gene activation performance by utilizing E. coli proteins. As a result, the gene scissors, which were previously "mainly for turning off," have evolved into a system that can simultaneously control both "on" and "off." The performance verification results of the developed system were very impressive. In gene activation experiments, expression levels increased by up to 4.9 times, and in repression experiments, they could be suppressed by up to 83%. Even more astonishing was the ability to control two different genes simultaneously. The team successfully activated one gene by 8.6 times while simultaneously repressing another by 90%. < (Left) The principle of the dual-mode CRISPR gene scissors. When the guide RNA (gRNA) binds to the target sequence, dxCas9-CRP either promotes (CRISPRa) or inhibits (CRISPRi) the binding of RNA polymerase near the transcription start site, precisely controlling gene expression. (Center) A large-scale screening of the entire E. coli genome is conducted to identify key regulatory targets for optimizing target substance production. The metabolic pathway for producing the target substance is then re-engineered by simultaneously regulating gene expression through activation and repression. (Right) The dual-mode CRISPR gene scissors system enables systematic redesign of cell metabolism, precise reconfiguration of gene expression, and the construction of microbial strains that can perform various functions, ultimately leading to a significant increase in target substance productivity. In this study, the dual-mode CRISPR system was applied to E. coli to demonstrate the enhanced production of 'violacein,' a purple functional biopigment with anticancer effects, and its potential for expansion to other bacterial species was also confirmed. > To demonstrate the practicality of this technology, the research team challenged themselves to increase the production of 'violacein,' a purple pigment with anticancer properties. Through large-scale experiments on all genes of E. coli, they identified genes that help in violacein production. As a result, production increased by 2.9 times when the 'rluC' gene, which helps protein production, was turned on, and by 3.0 times when the 'ftsA' gene, which helps cell division, was turned off. When both genes were controlled simultaneously, a greater synergistic effect was observed, achieving a remarkable 3.7-fold increase in production. Dr. Myung Hyun Noh of KRICT stated, "Precise gene activation is now possible in bacteria," and "This will greatly contribute to the development of the synthetic biology-based bio-industry." Professor Ju Young Lee said, "This research is a successful outcome of combining gene scissors with synthetic biology to significantly enhance the efficiency of microbial production platforms," and "The ability to control a complex genetic network with a single system presents a new research paradigm." He added, "This technology has also been confirmed to work in other bacterial species and can be utilized in various fields such as the production of biopharmaceuticals, chemicals, and fuels." < (A) A diagram of the violacein biosynthesis pathway, a functional biopigment produced from the starting material L-tryptophan through several enzymatic reactions. Violacein is a functional substance with broad applications in various industries and research fields, including medicine, healthcare, dyes, textiles, food and beverage, and cosmetics. (B) The results of a large-scale screening of gRNAs for gene activation and repression using the dual-mode CRISPR gene scissors system confirmed a 2.9-fold increase in violacein production (mg/L) upon rluC activation and a 3.0-fold increase upon ftsA repression compared to the control group. > The results of this research, with Dr. Soo Young Moon, a postdoctoral researcher at our university's Institute of Life Science, as the first author, were published online in 'Nucleic Acids Research,' a top-tier journal in the field of molecular biology, on August 21st. Paper Title: Dual-mode CRISPRa/i for genome-scale metabolic rewiring in Escherichia coli Author Information: Soo Young Moon (KAIST, First Author), Mi Ri Kim (KRICT), Nan-Yeong An (KAIST), Myung Hyun Noh (KRICT, Corresponding Author), Ju Young Lee (KAIST, Corresponding Author) (Total of 5 authors) DOI: 10.1093/nar/gkad818 This research was supported by the joint research and development program of the Ministry of Science and ICT, the National Research Foundation of Korea, and Boston Korea.
2025.09.22 View 1328
A Breakthrough in Parkinson's Research: Precision Diagnosis and Treatment with AI and Optogenetics <Research team photo (from top left) Dr. Bobae Hyeon, Professor Daesoo Kim, Director Chang-joon Lee, (right) Professor Won Do Heo> Globally recognized figures like Muhammad Ali and Michael J. Fox have long suffered from Parkinson's disease. The disease presents a complex set of motor symptoms, including tremors, rigidity, bradykinesia, and postural instability. However, traditional diagnostic methods have struggled to sensitively detect changes in the early stages, and drugs targeting brain signal regulation have had limited clinical effectiveness. Recently, Korean researchers successfully demonstrated the potential of a technology that integrates AI and optogenetics as a tool for precise diagnosis and therapeutic evaluation of Parkinson's disease in mice. They have also proposed a strategy for developing next-generation personalized treatments. KAIST (President Kwang Hyung Lee) announced on the 22nd of September that a collaborative research team—comprising Professor Won Do Heo's team from the Department of Biological Sciences, Professor Daesoo Kim's team from the Department of Brain and Cognitive Sciences, and Director Chang-Jun Lee's team from the Institute for Basic Science (IBS) Center for Cognition and Sociality—achieved a preclinical research breakthrough by combining AI analysis with optogenetics. Their work simultaneously demonstrated the possibility of early and precise diagnosis and treatment in an animal model of Parkinson's disease. The research team created a Parkinson's disease mouse model with two stages of severity. These were male mice with alpha-synuclein protein abnormalities, a standard model used to simulate human Parkinson's disease for diagnostic and therapeutic research. In collaboration with Professor Kim's team at KAIST, they introduced AI-based 3D pose estimation for behavioral analysis. The team analyzed over 340 behavioral features—such as gait, limb movements, and tremors—from the Parkinson's mice and condensed them into a single metric: the AI-predicted Parkinson's disease score (APS). The analysis showed that the APS exhibited a significant difference from the control group as early as two weeks after the disease was induced. It also proved more sensitive in assessing the disease's severity than traditional motor function tests. The study identified key diagnostic features, including changes in stride, asymmetrical limb movements, and chest tremors. The top 20 behavioral features included hand/foot asymmetry, changes in stride and posture, and an increase in high-frequency chest movement. To confirm that these behavioral indicators were not just general motor decline but specific to Parkinson's, the team applied the same analysis to a mouse model of Amyotrophic Lateral Sclerosis (ALS), also known as Lou Gehrig's disease, in collaboration with Director Lee's team at IBS. Since both Parkinson's and ALS cause motor function problems, if the APS simply reflected poor motor skills, a high score should have appeared in both diseases. However, the analysis of the ALS animal model showed that despite a decline in motor function, the mice did not exhibit the high APS seen in the Parkinson's model. Instead, their scores remained low, and their behavioral changes were distinctly different. This demonstrates that APS is directly related to specific, characteristic changes that only appear in Parkinson's disease. For treatment, the research team used optoRET, an optogenetics technology that precisely controls neurotrophic signals with light. This technique proved effective in the animal model, leading to smoother gait and limb movements and a reduction in tremors. Specifically, a regimen of shining light on alternate days was found to be the most effective, and it also showed a tendency to protect dopamine-producing neurons in the brain. Professor Won Do Heo of KAIST stated, "This is the first time in the world that a preclinical framework has been implemented that connects early diagnosis, treatment evaluation, and mechanism verification of Parkinson's disease by combining AI-based behavioral analysis with optogenetics." He added, "This lays a crucial foundation for future personalized medicine and customized treatments for patients." The study, with Dr. Bobae Hyeon, a postdoctoral researcher at the KAIST Institute for Biological Science, as the first author, was published online in the international journal Nature Communications on August 21st. Dr. Hyeon is conducting follow-up research to advance Parkinson's cell therapy at McLean Hospital, Harvard Medical School, supported by the "Global Physician-Scientist Training Program" of the Korea Health Industry Development Institute. This research was supported by the KAIST Global Singularity Project, the Ministry of Science and ICT/National Research Foundation of Korea, the IBS Center for Cognition and Sociality, and the Ministry of Health and Welfare/Korea Health Industry Development Institute. Paper Title: Integrating artificial intelligence and optogenetics for Parkinson's disease diagnosis and therapeutics in male mice DOI: https://doi.org/10.1038/s41467-025-63025-w
2025.09.22 View 1355
KAIST succeeds in controlling complex altered gene networks to restore them to normal Previously, research on controlling gene networks has been carried out based on a single stimulus-response of cells. More recently, studies have been proposed to precisely analyze complex gene networks to identify control targets. A KAIST research team has succeeded in developing a universal technology that identifies gene control targets in altered cellular gene networks and restores them. This achievement is expected to be widely applied to new anticancer therapies such as cancer reversibility, drug development, precision medicine, and reprogramming for cell therapy. KAIST (President Kwang Hyung Lee) announced on the 28th of August that Professor Kwang-Hyun Cho’s research team from the Department of Bio and Brain Engineering has developed a technology to systematically identify gene control targets that can restore the altered stimulus-response patterns of cells to normal by using an algebraic approach. The algebraic approach expresses gene networks as mathematical equations and identifies control targets through algebraic computations. The research team represented the complex interactions among genes within a cell as a "logic circuit diagram" (Boolean network). Based on this, they visualized how a cell responds to external stimuli as a "landscape map" (phenotype landscape). By applying a mathematical method called the "semi-tensor product,*" they developed a way to quickly and accurately calculate how the overall cellular response would change if a specific gene were controlled. *Semi-tensor product: a method that calculates all possible gene combinations and control effects in a single algebraic formula However, because the key genes that determine actual cellular responses number in the thousands, the calculations are extremely complex. To address this, the research team applied a numerical approximation method (Taylor approximation) to simplify the calculations. In simple terms, they transformed a complex problem into a simpler formula while still yielding nearly identical results. Through this, the team was able to calculate which stable state (attractor) a cell would reach and predict how the cell’s state would change when a particular gene was controlled. As a result, they were able to identify core gene control targets that could restore abnormal cellular responses to states most similar to normal. Professor Cho’s team applied the developed control technology to various gene networks and verified that it can accurately predict gene control targets that restore altered stimulus-response patterns of cells back to normal. In particular, by applying it to bladder cancer cell networks, they identified gene control targets capable of restoring altered responses to normal. They also discovered gene control targets in large-scale distorted gene networks during immune cell differentiation that are capable of restoring normal stimulus-response patterns. This enabled them to solve problems that previously required only approximate searches through lengthy computer simulations in a fast and systematic way. Professor Cho said, “This study is evaluated as a core original technology for the development of the Digital Cell Twin model*, which analyzes and controls the phenotype landscape of gene networks that determine cell fate. In the future, it is expected to be widely applicable across the life sciences and medicine, including new anticancer therapies through cancer reversibility, drug development, precision medicine, and reprogramming for cell therapy.” *Digital Cell Twin model: a technology that digitally models the complex reactions occurring within cells, enabling virtual simulations of cellular responses instead of actual experiments KAIST master’s student Insoo Jung, PhD student Corbin Hopper, PhD student Seong-Hoon Jang, and PhD student Hyunsoo Yeo participated in this study. The results were published online on August 22 in Science Advances, an international journal published by the American Association for the Advancement of Science (AAAS). ※ Paper title: “Reverse Control of Biological Networks to Restore Phenotype Landscapes” ※ DOI: https://www.science.org/doi/10.1126/sciadv.adw3995 This research was supported by the Mid-Career Researcher Program and the Basic Research Laboratory Program of the National Research Foundation of Korea, funded by the Ministry of Science and ICT.
2025.08.29 View 3130
“Why are we depressed?” KAIST is identifying the cause of depression and uncovering clues for treatment Major depressive disorder (MDD) is one of the most common psychiatric illnesses worldwide, but its molecular causes* have still not been clearly identified. A domestic research team has discovered that depression may not simply be caused by neuronal damage, but can also arise from the dysregulation of specific neural signaling pathways. In particular, they identified the molecular reason why elderly patients with depression do not respond to conventional antidepressants. This study suggests the possibility of therapeutic approaches using optogenetic technology to regulate neural signaling, and it provides clues for the development of new treatment strategies targeting the protein ‘Numb’ protein for elderly patients with depression. *Molecular causes: explanations for the origin of a disease at the level of molecules, proteins, or genes in the brain. KAIST (President Kwang Hyung Lee) announced on the 19th of August that a research team led by Distinguished Professor Won Do Heo of the Department of Biological Sciences at KAIST, in collaboration with forensic pathologist Minju Lee of the National Forensic Service (Director Bong Woo Lee) and Professor Seokhwi Kim of the Department of Pathology at Ajou University Medical Center (Director Sangwook Han), identified a new molecular mechanism for depression through RNA sequencing and the immunohistochemical analysis of brain tissue from patients who had committed suicide. Furthermore, they demonstrated in animal models that antidepressant effects can be restored by regulating the signaling pathway that induces neural recovery using optogenetic technology. The research team focused on the hippocampus, the brain region responsible for memory and emotion, and in particular on the dentate gyrus (DG). The DG is the entry point of information into the hippocampus, playing a role in new memory formation, neurogenesis, and emotional regulation, and is closely linked with depression. Using two representative mouse models for depression (the corticosterone stress model and the chronic unpredictable stress model), the team found that stress induced a striking increase in the signaling receptor FGFR1 (Fibroblast Growth Factor Receptor 1) in the DG. FGFR1 receives growth factor (FGF) signals and transmits growth and differentiation commands within cells. Subsequently, using conditional knockout (cKO) mice in which the FGFR1 gene was deleted, the researchers revealed that the absence of FGFR1 made mice more vulnerable to stress and led them to exhibit depressive symptoms more quickly. This indicates that FGFR1 plays a critical role in proper neural regulation and stress resistance. The team then developed an ‘optoFGFR1 system’ using optogenetics, enabling FGFR1 —essential for stress resistance—to be activated by light. They observed that activating FGFR1 in depression mouse models lacking FGFR1 restored antidepressant effects. In other words, they experimentally demonstrated that the activation of FGFR1 signaling alone could improve depressive behavior. Surprisingly, however, in aged depression mouse models, the activation of FGFR1 signaling through the optoFGFR1 system did not yield antidepressant effects. Investigating further, the researchers found that in the aged brains, a protein called ‘Numb’ was excessively expressed and interfered with FGFR1 signaling. Indeed, analysis of postmortem human brain tissue also showed the specific overexpression of Numb protein only in elderly patients with depression. When the researchers suppressed Numb using a gene regulatory tool (shRNA) while simultaneously activating FGFR1 signaling in mouse models, neurogenesis and behavior—previously unrecoverable—returned to normal even in aged depression models. This shows that the Numb protein acts as a “blocker” of FGFR1 signaling and is a key factor preventing the hippocampus from executing antidepressant mechanisms. Distinguished Professor Won Do Heo of KAIST said, “This study is meaningful in that it revealed that depression may not only result from simple neuronal damage, but can also arise from the dysregulation of specific neural signaling pathways. In particular, we identified the molecular reason why antidepressants are less effective in elderly patients, and we expect this to provide a clue for the development of new therapeutic strategies targeting the Numb protein.” He added, “Moreover, this interdisciplinary study, which combined KAIST’s expertise in neuroscience with the National Forensic Service’s forensic brain analysis technologies, is expected to serve as a bridge between basic research on psychiatric disorders and clinical applications.” This study, led by first author Jongpil Shin, a PhD student in the Department of Biological Sciences at KAIST, was published on August 15, 2025, in the international journal Experimental & Molecular Medicine. Paper title: “Dysregulation of FGFR1 signaling in the hippocampus facilitates depressive disorder” DOI: https://doi.org/10.1038/s12276-025-01519-9 This research was supported by the Ministry of Science and ICT’s National Research Foundation of Korea through the ASTRA program and the Bio-Medical Technology Development project.
2025.08.19 View 2464
KAIST Takes the Lead in Developing Core Technologies for Generative AI National R&D Project KAIST (President Kwang Hyung Lee) is leading the transition to AI Transformation (AX) by advancing research topics based on the practical technological demands of industries, fostering AI talent, and demonstrating research outcomes in industrial settings. In this context, KAIST announced on the 13th of August that it is at the forefront of strengthening the nation's AI technology competitiveness by developing core AI technologies via national R&D projects for generative AI led by the Ministry of Science and ICT. In the 'Generative AI Leading Talent Cultivation Project,' KAIST was selected as a joint research institution for all three projects—two led by industry partners and one by a research institution—and will thus be tasked with the dual challenge of developing core generative AI technologies and cultivating practical, core talent through industry-academia collaborations. Moreover, in the 'Development of a Proprietary AI Foundation Model' project, KAIST faculty members are participating as key researchers in four out of five consortia, establishing the university as a central hub for domestic generative AI research. Each project in the Generative AI Leading Talent Cultivation Project will receive 6.7 billion won, while each consortium in the proprietary AI foundation model development project will receive a total of 200 billion won in government support, including GPU infrastructure. As part of the 'Generative AI Leading Talent Cultivation Project,' which runs until the end of 2028, KAIST is collaborating with LG AI Research. Professor Noseong Park from the School of Computing will participate as the principal investigator for KAIST, conducting research in the field of physics-based generative AI (Physical AI). This project focuses on developing image and video generation technologies based on physical laws and developing a 'World Model.' In particular, research being conducted by Professor Noseong Park's team and Professor Sung-Eui Yoon's team proposes a model structure designed to help AI learn the real-world rules of the physical world more precisely. This is considered a core technology for Physical AI. Professors Noseong Park, Jae-gil Lee, Jiyoung Hwang, Sung-Eui Yoon, and Hyun-Woo Kim from the School of Computing, who have been globally recognized for their achievements in the AI field, are jointly participating in this project. This year, they have presented work at top AI conferences such as ICLR, ICRA, ICCV, and ICML, including: ▲ Research on physics-based Ollivier Ricci-flow (ICLR 2025, Prof. Noseong Park) ▲ Technology to improve the navigation efficiency of quadruped robots (ICRA 2025, Prof. Sung-Eui Yoon) ▲ A multimodal large language model for text-video retrieval (ICCV 2025, Prof. Hyun-Woo Kim) ▲ Structured representation learning for knowledge generation (ICML 2025, Prof. Jiyoung Whang). In the collaboration with NC AI, Professor Tae-Kyun Kim from the School of Computing is participating as the principal investigator to develop multimodal AI agent technology. The research will explore technologies applicable to the entire gaming industry, such as 3D modeling, animation, avatar expression generation, and character AI. It is expected to contribute to training practical AI talents by giving them hands-on experience in the industrial field and making the game production pipeline more efficient. As the principal investigator, Professor Tae-Kyun Kim, a renowned scholar in 3D computer vision and generative AI, is developing key technologies for creating immersive avatars in the virtual and gaming industries. He will apply a first-person full-body motion diffusion model, which he developed through a joint research project with Meta, to VR and AR environments. Professor Tae-Kyun Kim, Minhyeok Seong, and Tae-Hyun Oh from the School of Computing, and Professors Sung-Hee Lee, Woon-Tack Woo, Jun-Yong Noh, and Kyung-Tae Lim from the Graduate School of Culture Technology, are participating in the NC AI project. They have presented globally recognized work at CVPR 2025 and ICLR 2025, including: ▲ A first-person full-body motion diffusion model (CVPR 2025, Prof. Tae-Kyun Kim) ▲ Stochastic diffusion synchronization technology for image generation (ICLR 2025, Prof. Minhyeok Seong) ▲ The creation of a large-scale 3D facial mesh video dataset (ICLR 2025, Prof. Tae-Hyun Oh) ▲ Object-adaptive agent motion generation technology, InterFaceRays (Eurographics 2025, Prof. Sung-Hee Lee) ▲ 3D neural face editing technology (CVPR 2025, Prof. Jun-Yong Noh) ▲ Research on selective search augmentation for multilingual vision-language models (COLING 2025, Prof. Kyung-Tae Lim). In the project led by the Korea Electronics Technology Institute (KETI), Professor Seungryong Kim from the Kim Jae-chul Graduate School of AI is participating in generative AI technology development. His team recently developed new technology for extracting robust point-tracking information from video data in collaboration with Adobe Research and Google DeepMind, proposing a key technology for clearly understanding and generating videos. Each industry partner will open joint courses with KAIST and provide their generative AI foundation models for education and research. Selected outstanding students will be dispatched to these companies to conduct practical research, and KAIST faculty will also serve as adjunct professors at the in-house AI graduate school established by LG AI Research. Meanwhile, KAIST showed an unrivaled presence by participating in four consortia for the Ministry of Science and ICT's 'Proprietary AI Foundation Model Development' project. In the NC AI Consortium, Professors Tae-Kyun Kim, Sung-Eui Yoon, Noseong Park, Jiyoung Hwang, and Minhyeok Seong from the School of Computing are participating, focusing on the development of multimodal foundation models (LMMs) and robot-based models. They are particularly concentrating on developing LMMs that learn common sense about space, physics, and time. They have formed a research team optimized for developing next-generation, multimodal AI models that can understand and interact with the physical world, equipped with an 'all-purpose AI brain' capable of simultaneously understanding and processing diverse information such as text, images, video, and sound. In the Upstage Consortium, Professors Jae-gil Lee and Hyeon-eon Oh from the School of Computing, both renowned scholars in data AI and NLP (natural language processing), along with Professor Kyung-Tae Lim from the Graduate School of Culture Technology, an LLM expert, are responsible for developing vertical models for industries such as finance, law, and manufacturing. The KAIST researchers will concentrate on developing practical AI models that are directly applicable to industrial settings and tailored to each specific industry. The Naver Consortium includes Professor Tae-Hyun Oh from the School of Computing, who has developed key technology for multimodal learning and compositional language-vision models, Professor Hyun-Woo Kim, who has proposed video reasoning and generation methods using language models, and faculty from the Kim Jae-chul Graduate School of AI and the Department of Electrical Engineering. In the SKT Consortium, Professor Ki-min Lee from the Kim Jae-chul Graduate School of AI, who has achieved outstanding results in text-to-image generation, human preference modeling, and visual robotic manipulation technology development, is participating. This technology is expected to play a key role in developing personalized services and customized AI solutions for telecommunications companies. This outcome is considered a successful culmination of KAIST's strategy for developing AI technology based on industry demand and centered on on-site demonstrations. KAIST President Kwang Hyung Lee said, "For AI technology to go beyond academic achievements and be connected to and practical for industry, continuous government support, research, and education centered on industry-academia collaboration are essential. KAIST will continue to strive to solve problems in industrial settings and make a real contribution to enhancing the competitiveness of the AI ecosystem." He added that while the project led by Professor Sung-Ju Hwang from the Kim Jae-chul Graduate School of AI, which had applied as a lead institution for the proprietary foundation model development project, was unfortunately not selected, it was a meaningful challenge that stood out for its original approach and bold attempts. President Lee further commented, "Regardless of whether it was selected or not, such attempts will accumulate and make the Korean AI ecosystem even richer."
2025.08.13 View 3344
KAIST Team Develops Optogenetic Platform for Spatiotemporal Control of Protein and mRNA Storage and Release <Dr. Chaeyeon Lee, Professor Won Do Heo from Department of Biological Sciences> A KAIST research team led by Professor Won Do Heo (Department of Biological Sciences) has developed an optogenetic platform, RELISR (REversible LIght-induced Store and Release), that enables precise spatiotemporal control over the storage and release of proteins and mRNAs in living cells and animals. Traditional optogenetic condensate systems have been limited by their reliance on non-specific multivalent interactions, which can lead to unintended sequestration or release of endogenous molecules. RELISR overcomes these limitations by employing highly specific protein–protein (nanobody–antigen) and protein–RNA (MCP–MS2) interactions, enabling the selective and reversible compartmentalization of target proteins or mRNAs within engineered, membrane-less condensates. In the dark, RELISR stably sequesters target molecules within condensates, physically isolating them from the cellular environment. Upon blue light stimulation, the condensates rapidly dissolve, releasing the stored proteins or mRNAs, which immediately regain their cellular functions or translational competency. This allows for reversible and rapid modulation of molecular activities in response to optical cues. < Figure 1. Overview of the Artificial Condensate System (RELISR). The artificial condensate system, RELISR, includes "Protein-RELISR" for storing proteins and "mRNA-RELISR" for storing mRNA. These artificial condensates can be disassembled by blue light irradiation and reassembled in a dark state> The research team demonstrated that RELISR enables temporal and spatial regulation of protein activity and mRNA translation in various cell types, including cultured neurons and mouse liver tissue. Comparative studies showed that RELISR provides more robust and reversible control of translation than previous systems based on spatial translocation. While previous optogenetic systems such as LARIAT (Lee et al., Nature Methods, 2014) and mRNA-LARIAT (Kim et al., Nat. Cell Biol., 2019) enabled the selective sequestration of proteins or mRNAs into membrane-less condensates in response to light, they were primarily limited to the trapping phase. The RELISR platform introduced in this study establishes a new paradigm by enabling both the targeted storage of proteins and mRNAs and their rapid, light-triggered release. This approach allows researchers to not only confine molecular function on demand, but also to restore activity with precise temporal control. < Figure 2. Cell shape change using the artificial condensate system (RELISR). A target protein, Vav2, which contributes to cell shape, was stored within the artificial condensate and then released after light irradiation. This release activated the target protein Vav2, causing a change in cell shape. It was confirmed that the storage, release, and activation of various proteins were effectively achieved> Professor Heo stated, “RELISR is a versatile optogenetic tool that enables the precise control of protein and mRNA function at defined times and locations in living systems. We anticipate this platform will be broadly applicable for studies of cell signaling, neural circuits, and therapeutic development. Furthermore, the combination of RELISR with genome editing or tissue-targeted delivery could further expand its utility for molecular medicine.” < Figure 3. Expression of a target mRNA using the artificial condensate system (RELISR) in mice. The genetic material for the artificial condensate system, RELISR, was injected into a living mouse. Using this system, a target mRNA was stored within the mouse's liver. Upon light irradiation, the mRNA was released, which induced the translation of a luminescent protein> This research was conducted by first author Dr. Chaeyeon Lee, under the supervision of Professor Heo, with contributions from Dr. Daseuli Yu (co-corresponding author) and Professor YongKeun Park (co-corresponding author, Department of Physics), whose group performed quantitative imaging analyses of biophysical changes induced by RELISR in cells. The findings were published in Nature Communications (July 7, 2025; DOI: 10.1038/s41467-025-61322-y). This work was supported by the Samsung Future Technology Foundation and the National Research Foundation of Korea.
2025.07.23 View 2853
KAIST Researchers Unveil an AI that Generates "Unexpectedly Original" Designs < Photo 1. Professor Jaesik Choi, KAIST Kim Jaechul Graduate School of AI > Recently, text-based image generation models can automatically create high-resolution, high-quality images solely from natural language descriptions. However, when a typical example like the Stable Diffusion model is given the text "creative," its ability to generate truly creative images remains limited. KAIST researchers have developed a technology that can enhance the creativity of text-based image generation models such as Stable Diffusion without additional training, allowing AI to draw creative chair designs that are far from ordinary. Professor Jaesik Choi's research team at KAIST Kim Jaechul Graduate School of AI, in collaboration with NAVER AI Lab, developed this technology to enhance the creative generation of AI generative models without the need for additional training. < Photo 2. Gayoung Lee, Researcher at NAVER AI Lab; Dahee Kwon, Ph.D. Candidate at KAIST Kim Jaechul Graduate School of AI; Jiyeon Han, Ph.D. Candidate at KAIST Kim Jaechul Graduate School of AI; Junho Kim, Researcher at NAVER AI Lab > Professor Choi's research team developed a technology to enhance creative generation by amplifying the internal feature maps of text-based image generation models. They also discovered that shallow blocks within the model play a crucial role in creative generation. They confirmed that amplifying values in the high-frequency region after converting feature maps to the frequency domain can lead to noise or fragmented color patterns. Accordingly, the research team demonstrated that amplifying the low-frequency region of shallow blocks can effectively enhance creative generation. Considering originality and usefulness as two key elements defining creativity, the research team proposed an algorithm that automatically selects the optimal amplification value for each block within the generative model. Through the developed algorithm, appropriate amplification of the internal feature maps of a pre-trained Stable Diffusion model was able to enhance creative generation without additional classification data or training. < Figure 1. Overview of the methodology researched by the development team. After converting the internal feature map of a pre-trained generative model into the frequency domain through Fast Fourier Transform, the low-frequency region of the feature map is amplified, then re-transformed into the feature space via Inverse Fast Fourier Transform to generate an image. > The research team quantitatively proved, using various metrics, that their developed algorithm can generate images that are more novel than those from existing models, without significantly compromising utility. In particular, they confirmed an increase in image diversity by mitigating the mode collapse problem that occurs in the SDXL-Turbo model, which was developed to significantly improve the image generation speed of the Stable Diffusion XL (SDXL) model. Furthermore, user studies showed that human evaluation also confirmed a significant improvement in novelty relative to utility compared to existing methods. Jiyeon Han and Dahee Kwon, Ph.D. candidates at KAIST and co-first authors of the paper, stated, "This is the first methodology to enhance the creative generation of generative models without new training or fine-tuning. We have shown that the latent creativity within trained AI generative models can be enhanced through feature map manipulation." They added, "This research makes it easy to generate creative images using only text from existing trained models. It is expected to provide new inspiration in various fields, such as creative product design, and contribute to the practical and useful application of AI models in the creative ecosystem." < Figure 2. Application examples of the methodology researched by the development team. Various Stable Diffusion models generate novel images compared to existing generations while maintaining the meaning of the generated object. > This research, co-authored by Jiyeon Han and Dahee Kwon, Ph.D. candidates at KAIST Kim Jaechul Graduate School of AI, was presented on June 16 at the International Conference on Computer Vision and Pattern Recognition (CVPR), an international academic conference.* Paper Title: Enhancing Creative Generation on Stable Diffusion-based Models* DOI: https://doi.org/10.48550/arXiv.2503.23538 This research was supported by the KAIST-NAVER Ultra-creative AI Research Center, the Innovation Growth Engine Project Explainable AI, the AI Research Hub Project, and research on flexible evolving AI technology development in line with increasingly strengthened ethical policies, all funded by the Ministry of Science and ICT through the Institute for Information & Communications Technology Promotion. It also received support from the KAIST AI Graduate School Program and was carried out at the KAIST Future Defense AI Specialized Research Center with support from the Defense Acquisition Program Administration and the Agency for Defense Development.
2025.06.20 View 5243
KAIST Succeeds in Real-Time Carbon Dioxide Monitoring Without Batteries or External Power < (From left) Master's Student Gyurim Jang, Professor Kyeongha Kwon > KAIST (President Kwang Hyung Lee) announced on June 9th that a research team led by Professor Kyeongha Kwon from the School of Electrical Engineering, in a joint study with Professor Hanjun Ryu's team at Chung-Ang University, has developed a self-powered wireless carbon dioxide (CO2) monitoring system. This innovative system harvests fine vibrational energy from its surroundings to periodically measure CO2 concentrations. This breakthrough addresses a critical need in environmental monitoring: accurately understanding "how much" CO2 is being emitted to combat climate change and global warming. While CO2 monitoring technology is key to this, existing systems largely rely on batteries or wired power system, imposing limitations on installation and maintenance. The KAIST team tackled this by creating a self-powered wireless system that operates without external power. The core of this new system is an "Inertia-driven Triboelectric Nanogenerator (TENG)" that converts vibrations (with amplitudes ranging from 20-4000 ㎛ and frequencies from 0-300 Hz) generated by industrial equipment or pipelines into electricity. This enables periodic CO2 concentration measurements and wireless transmission without the need for batteries. < Figure 1. Concept and configuration of self-powered wireless CO2 monitoring system using fine vibration harvesting (a) System block diagram (b) Photo of fabricated system prototype > The research team successfully amplified fine vibrations and induced resonance by combining spring-attached 4-stack TENGs. They achieved stable power production of 0.5 mW under conditions of 13 Hz and 0.56 g acceleration. The generated power was then used to operate a CO2 sensor and a Bluetooth Low Energy (BLE) system-on-a-chip (SoC). Professor Kyeongha Kwon emphasized, "For efficient environmental monitoring, a system that can operate continuously without power limitations is essential." She explained, "In this research, we implemented a self-powered system that can periodically measure and wirelessly transmit CO2 concentrations based on the energy generated from an inertia-driven TENG." She added, "This technology can serve as a foundational technology for future self-powered environmental monitoring platforms integrating various sensors." < Figure 2. TENG energy harvesting-based wireless CO2 sensing system operation results (c) Experimental setup (d) Measured CO2 concentration results powered by TENG and conventional DC power source > This research was published on June 1st in the internationally renowned academic journal `Nano Energy (IF 16.8)`. Gyurim Jang, a master's student at KAIST, and Daniel Manaye Tiruneh, a master's student at Chung-Ang University, are the co-first authors of the paper.*Paper Title: Highly compact inertia-driven triboelectric nanogenerator for self-powered wireless CO2 monitoring via fine-vibration harvesting*DOI: 10.1016/j.nanoen.2025.110872 This research was supported by the Saudi Aramco-KAIST CO2 Management Center.
2025.06.09 View 54060