Kaist

RAIBO Runs over Walls with Feline Agility... Ready..

  < Photo 1. Research Team Photo (Professor Jemin Hwangbo, second from right in the front row) >   KAIST's quadrupedal robot, RAIBO, can now move at high speed across discontinuous and complex terrains such as stairs, gaps, walls, and debris. It has demonstrated its ability to run on vertical walls, leap over 1.3-meter-wide gaps, sprint at approximately 14.4 km/h over stepping stones, and move quickly and nimbly on terrain combining 30° slopes, stairs, and stepping stones. RAIBO is expected to be deployed soon for practical missions such as disaster site exploration and mountain searches.   Professor Jemin Hwangbo's research team in the Department of Mechanical Engineering at our university announced on June 3rd that they have developed a quadrupedal robot navigation framework capable of high-speed locomotion at 14.4 km/h (4m/s) even on discontinuous and complex terrains such as walls, stairs, and stepping stones.   The research team developed a quadrupedal navigation system that enables the robot to reach its target destination quickly and safely in complex and discontinuous terrain.   To achieve this, they approached the problem by breaking it down into two stages: first, developing a planner for planning foothold positions, and second, developing a tracker to accurately follow the planned foothold positions.   First, the planner module quickly searches for physically feasible foothold positions using a sampling-based optimization method with neural network-based heuristics and verifies the optimal path through simulation rollouts.   While existing methods considered various factors such as contact timing and robot posture in addition to foothold positions, this research significantly reduced computational complexity by setting only foothold positions as the search space. Furthermore, inspired by the walking method of cats, the introduction of a structure where the hind feet step on the same spots as the front feet further significantly reduced computational complexity.       < Figure 1. High-speed navigation across various discontinuous terrains >   Second, the tracker module is trained to accurately step on planned positions, and tracking training is conducted through a generative model that competes in environments of appropriate difficulty.   The tracker is trained through reinforcement learning to accurately step on planned plots, and during this process, a generative model called the 'map generator' provides the target distribution.   This generative model is trained simultaneously and adversarially with the tracker to allow the tracker to progressively adapt to more challenging difficulties. Subsequently, a sampling-based planner was designed to generate feasible foothold plans that can reflect the characteristics and performance of the trained tracker.   This hierarchical structure showed superior performance in both planning speed and stability compared to existing techniques, and experiments proved its high-speed locomotion capabilities across various obstacles and discontinuous terrains, as well as its general applicability to unseen terrains.   Professor Jemin Hwangbo stated, "We approached the problem of high-speed navigation in discontinuous terrain, which previously required a significantly large amount of computation, from the simple perspective of how to select the footprint positions. Inspired by the placements of cat's paw, allowing the hind feet to step where the front feet stepped drastically reduced computation. We expect this to significantly expand the range of discontinuous terrain that walking robots can overcome and enable them to traverse it at high speeds, contributing to the robot's ability to perform practical missions such as disaster site exploration and mountain searches."   This research achievement was published in the May 2025 issue of the international journal Science Robotics.   Paper Title: High-speed control and navigation for quadrupedal robots on complex and discrete terrain, (https://www.science.org/doi/10.1126/scirobotics.ads6192)   YouTube Link: https://youtu.be/EZbM594T3c4?si=kfxLF2XnVUvYVIyk  

Professor Hyun Myung＇s Team Wins First Place in a ..

  < Photo 1. (From left) Daebeom Kim (Team Leader, Ph.D. student), Seungjae Lee (Ph.D. student), Seoyeon Jang (Ph.D. student), Jei Kong (Master's student), Professor Hyun Myung >   A team of the Urban Robotics Lab, led by Professor Hyun Myung from the KAIST School of Electrical Engineering, achieved a remarkable first-place overall victory in the Nothing Stands Still Challenge (NSS Challenge) 2025, held at the 2025 IEEE International Conference on Robotics and Automation (ICRA), the world's most prestigious robotics conference, from May 19 to 23 in Atlanta, USA.   The NSS Challenge was co-hosted by HILTI, a global construction company based in Liechtenstein, and Stanford University's Gradient Spaces Group. It is an expanded version of the HILTI SLAM (Simultaneous Localization and Mapping)＊ Challenge, which has been held since 2021, and is considered one of the most prominent challenges at 2025 IEEE ICRA.   ＊SLAM: Refers to Simultaneous Localization and Mapping, a technology where robots, drones, autonomous vehicles, etc., determine their own position and simultaneously create a map of their surroundings.       < Photo 2. A scene from the oral presentation on the winning team's technology (Speakers: Seungjae Lee and Seoyeon Jang, Ph.D. candidates of KAIST School of Electrical Engineering) >   This challenge primarily evaluates how accurately and robustly LiDAR scan data, collected at various times, can be registered in situations with frequent structural changes, such as construction and industrial environments. In particular, it is regarded as a highly technical competition because it deals with multi-session localization and mapping (Multi-session SLAM) technology that responds to structural changes occurring over multiple timeframes, rather than just single-point registration accuracy.   The Urban Robotics Lab team secured first place overall, surpassing National Taiwan University (3rd place) and Northwestern Polytechnical University of China (2nd place) by a significant margin, with their unique localization and mapping technology that solves the problem of registering LiDAR data collected across multiple times and spaces. The winning team will be awarded a prize of ＄4,000.       < Figure 1. Example of Multiway-Registration for Registering Multiple Scans >   The Urban Robotics Lab team independently developed a multiway-registration framework that can robustly register multiple scans even without prior connection information. This framework consists of an algorithm for summarizing feature points within scans and finding correspondences (CubicFeat), an algorithm for performing global registration based on the found correspondences (Quatro), and an algorithm for refining results based on change detection (Chamelion). This combination of technologies ensures stable registration performance based on fixed structures, even in highly dynamic industrial environments.       < Figure 2. Example of Change Detection Using the Chamelion Algorithm>   LiDAR scan registration technology is a core component of SLAM (Simultaneous Localization And Mapping) in various autonomous systems such as autonomous vehicles, autonomous robots, autonomous walking systems, and autonomous flying vehicles.   Professor Hyun Myung of the School of Electrical Engineering stated, "This award-winning technology is evaluated as a case that simultaneously proves both academic value and industrial applicability by maximizing the performance of precisely estimating the relative positions between different scans even in complex environments. I am grateful to the students who challenged themselves and never gave up, even when many teams abandoned due to the high difficulty."       < Figure 3. Competition Result Board, Lower RMSE (Root Mean Squared Error) Indicates Higher Score (Unit: meters)>   The Urban Robotics Lab team first participated in the SLAM Challenge in 2022, winning second place among academic teams, and in 2023, they secured first place overall in the LiDAR category and first place among academic teams in the vision category.

KAIST to Develop a Korean-style ChatGPT Platform S..

On May 23rd, KAIST (President Kwang-Hyung Lee) announced that its Digital Bio-Health AI Research Center (Director: Professor JongChul Ye of KAIST Kim Jaechul Graduate School of AI) has been selected for the Ministry of Science and ICT's 'AI Top-Tier Young Researcher Support Program (AI Star Fellowship Project).' With a total investment of ₩11.5 billion from May 2025 to December 2030, the center will embark on the full-scale development of AI technology and a platform capable of independently inferring and determining the kinds of diseases, and discovering new drugs.       < Photo. On May 20th, a kick-off meeting for the AI Star Fellowship Project was held at KAIST Kim Jaechul Graduate School of AI’s Yangjae Research Center with the KAIST research team and participating organizations of Samsung Medical Center, NAVER Cloud, and HITS. [From left to right in the front row] Professor Jaegul Joo (KAIST), Professor Yoonjae Choi (KAIST), Professor Woo Youn Kim (KAIST/HITS), Professor JongChul Ye (KAIST), Professor Sungsoo Ahn (KAIST), Dr. Haanju Yoo (NAVER Cloud), Yoonho Lee (KAIST), HyeYoon Moon (Samsung Medical Center), Dr. Su Min Kim (Samsung Medical Center) >   This project aims to foster an innovative AI research ecosystem centered on young researchers and develop an inferential AI agent that can utilize and automatically expand specialized knowledge systems in the bio and medical fields.   Professor JongChul Ye of the Kim Jaechul Graduate School of AI will serve as the lead researcher, with young researchers from KAIST including Professors Yoonjae Choi, Kimin Lee, Sungsoo Ahn, and Chanyoung Park, along with mid-career researchers like Professors Jaegul Joo and Woo Youn Kim, jointly undertaking the project. They will collaborate with various laboratories within KAIST to conduct comprehensive research covering the entire cycle from the theoretical foundations of AI inference to its practical application.     Specifically, the main goals include: - Building high-performance inference models that integrate diverse medical knowledge systems to enhance the precision and reliability of diagnosis and treatment. - Developing a convergence inference platform that efficiently combines symbol-based inference with neural network models. - Securing AI technology for new drug development and biomarker discovery based on 'cell ontology.'     Furthermore, through close collaboration with industry and medical institutions such as Samsung Medical Center, NAVER Cloud, and HITS Co., Ltd., the project aims to achieve:   - Clinical diagnostic AI utilizing medical knowledge systems. - AI-based molecular target exploration for new drug development. - Commercialization of an extendible AI inference platform.   Professor JongChul Ye, Director of KAIST's Digital Bio-Health AI Research Center, stated, "At a time when competition in AI inference model development is intensifying, it is a great honor for KAIST to lead the development of AI technology specialized in the bio and medical fields with world-class young researchers." He added, "We will do our best to ensure that the participating young researchers reach a world-leading level in terms of research achievements after the completion of this seven-year project starting in 2025."   The AI Star Fellowship is a newly established program where post-doctoral researchers and faculty members within seven years of appointment participate as project leaders (PLs) to independently lead research. Multiple laboratories within a university and demand-side companies form a consortium to operate the program.   Through this initiative, KAIST plans to nurture bio-medical convergence AI talent and simultaneously promote the commercialization of core technologies in collaboration with Samsung Medical Center, NAVER Cloud, and HITS.

“For the First Time, We Shared a Meaningful Exchan..

• KAIST team up with NAVER AI Lab and Dodakim Child Development Center Develop ‘AAcessTalk’, an AI-driven Communication Tool bridging the gap Between Children with Autism and their Parents   • The project earned the prestigious Best Paper Award at the ACM CHI 2025, the Premier International Conference in Human-Computer Interaction   • Families share heartwarming stories of breakthrough communication and newfound understanding.       < Photo 1. (From left) Professor Hwajung Hong and Doctoral candidate Dasom Choi of the Department of Industrial Design with SoHyun Park and Young-Ho Kim of Naver Cloud AI Lab >   For many families of minimally verbal autistic (MVA) children, communication often feels like an uphill battle. But now, thanks to a new AI-powered app developed by researchers at KAIST in collaboration with NAVER AI Lab and Dodakim Child Development Center, parents are finally experiencing moments of genuine connection with their children.   On the 16th, the KAIST (President Kwang Hyung Lee) research team, led by Professor Hwajung Hong of the Department of Industrial Design, announced the development of ‘AAcessTalk,’ an artificial intelligence (AI)-based communication tool that enables genuine communication between children with autism and their parents.   This research was recognized for its human-centered AI approach and received international attention, earning the Best Paper Award at the ACM CHI 2025＊, an international conference held in Yokohama, Japan.   ＊ACM CHI (ACM Conference on Human Factors in Computing Systems) 2025: One of the world's most prestigious academic conference in the field of Human-Computer Interaction (HCI).   This year, approximately 1,200 papers were selected out of about 5,000 submissions, with the Best Paper Award given to only the top 1％. The conference, which drew over 5,000 researchers, was the largest in its history, reflecting the growing interest in ‘Human-AI Interaction.’   Called AACessTalk, the app offers personalized vocabulary cards tailored to each child’s interests and context, while guiding parents through conversations with customized prompts. This creates a space where children’s voices can finally be heard—and where parents and children can connect on a deeper level.   Traditional augmentative and alternative communication (AAC) tools have relied heavily on fixed card systems that often fail to capture the subtle emotions and shifting interests of children with autism. AACessTalk breaks new ground by integrating AI technology that adapts in real time to the child’s mood and environment.       < Figure. Schematics of AACessTalk system. It provides personalized vocabulary cards for children with autism and context-based conversation guides for parents to focus on practical communication. Large ‘Turn Pass Button’ is placed at the child’s side to allow the child to lead the conversation. >     Among its standout features is a large ‘Turn Pass Button’ that gives children control over when to start or end conversations—allowing them to lead with agency. Another feature, the “What about Mom/Dad?” button, encourages children to ask about their parents’ thoughts, fostering mutual engagement in dialogue, something many children had never done before.   One parent shared, “For the first time, we shared a meaningful exchange.” Such stories were common among the 11 families who participated in a two-week pilot study, where children used the app to take more initiative in conversations and parents discovered new layers of their children’s language abilities.   Parents also reported moments of surprise and joy when their children used unexpected words or took the lead in conversations, breaking free from repetitive patterns. “I was amazed when my child used a word I hadn’t heard before. It helped me understand them in a whole new way,” recalled one caregiver.   Professor Hwajung Hong, who led the research at KAIST’s Department of Industrial Design, emphasized the importance of empowering children to express their own voices. “This study shows that AI can be more than a communication aid—it can be a bridge to genuine connection and understanding within families,” she said.   Looking ahead, the team plans to refine and expand human-centered AI technologies that honor neurodiversity, with a focus on bringing practical solutions to socially vulnerable groups and enriching user experiences.   This research is the result of KAIST Department of Industrial Design doctoral student Dasom Choi's internship at NAVER AI Lab.   ＊ Thesis Title: AACessTalk: Fostering Communication between Minimally Verbal Autistic Children and Parents with Contextual Guidance and Card Recommendation   ＊ DOI: 10.1145/3706598.3713792   ＊ Main Author Information: Dasom Choi (KAIST, NAVER AI Lab, First Author), SoHyun Park (NAVER AI Lab) , Kyungah Lee (Dodakim Child Development Center), Hwajung Hong (KAIST), and Young-Ho Kim (NAVER AI Lab, Corresponding Author)   This research was supported by the NAVER AI Lab internship program and grants from the National Research Foundation of Korea: the Doctoral Student Research Encouragement Grant (NRF-2024S1A5B5A19043580) and the Mid-Career Researcher Support Program for the Development of a Generative AI-Based Augmentative and Alternative Communication System for Autism Spectrum Disorder (RS-2024-00458557).

Decoding Fear： KAIST Identifies An Affective Brain..

Even after the COVID-19 pandemic, various new infectious diseases continue to emerge, posing ongoing viral threats that demand robust and sustained immune defenses. However, excessive immune reactions can also harm body tissues, causing significant health issues. KAIST and an international research team have discovered a critical protein that acts as a 'switch' regulating immune responses to viruses. This breakthrough is expected to lay the groundwork for future infectious disease responses and autoimmune disease treatment strategies.   KAIST (President Kwang-Hyung Lee) announced on May 14 that a joint research team led by Professor Yoosik Kim from the Department of Chemical and Biomolecular Engineering at KAIST and Professor Seunghee Cha from University of Florida has discovered the mechanism by which double-stranded RNA derived from mitochondria amplifies immune responses. They identified the protein SLIRP as an 'immune switch' that regulates this process, playing a crucial role in both viral infections and autoimmune diseases.       < (From left) Master's candidate Yewon Yang, Professor Yoosik Kim and Ph.D. candidate Doyeong Ku of the Department of Chemical and Biomolecular Engineering >   Autoimmune diseases arise when the immune system fails to differentiate between external pathogens and the body's own molecules, leading to self-directed attacks. Despite extensive research, the precise causes of excessive inflammatory conditions like Sjögren’s syndrome and systemic lupus erythematosus remain unclear, and effective treatments are still limited.   To uncover the molecular mechanisms driving immune hyperactivation and to identify potential regulatory factors, the research team led by Professor Yoosik Kim focused on mitochondrial double-stranded RNA (mt-dsRNA), a genetic immunogenic material produced within cellular organelles. Since mt-dsRNA structurally resembles viral RNA, it can mistakenly trigger immune responses even in the absence of an actual viral infection.   The team discovered that SLIRP, a key regulator of mt-dsRNA, amplifies immune responses by stabilizing the RNA. They confirmed that SLIRP expression increases in experimental models simulating the tissues of autoimmune disease patients and viral infections. Conversely, suppressing SLIRP significantly reduced the immune response, underscoring its role as a critical factor in immune amplification.   This study also demonstrated the dual function of SLIRP in different contexts. In cells infected with human beta coronavirus OC43 and encephalomyocarditis virus (EMCV), SLIRP suppression led to reduced antiviral responses and increased viral replication. Meanwhile, in the blood and salivary gland cells of Sjögren’s syndrome patients, where both SLIRP and mt-dsRNA levels were elevated, suppressing SLIRP alleviated the abnormal immune response.   These findings highlight SLIRP as a key molecular switch that regulates immune responses in both infections and autoimmune diseases.       < Figure 1. Schematic diagram of antiviral signal amplification by SLIRP: SLIRP-based mt-dsRNA induction, cytoplasmic accumulation, and strong interferon response induction by positive feedback of immune response activation. Confirmation of the immune regulatory function of SLIRP in defense against autoimmune diseases Sjögren's syndrome, coronavirus, and encephalomyocarditis virus infection. >   Professor Yoosik Kim remarked, "Through this study, we have identified SLIRP as a crucial protein that drives immune amplification via mt-dsRNAs. Given its dual role in autoimmune diseases and viral infections, SLIRP presents a promising target for immune regulation therapies across various inflammatory disease contexts."   The study, with Ph.D. student Do-Young Ku (first author) and M.S. student Ye-Won Yang (second author) from the Department of Chemical and Biomolecular Engineering at KAIST as primary contributors, was published online in the journal Cell Reports on April 19, 2025.   ※ Paper title: SLIRP amplifies antiviral signaling via positive feedback regulation and contributes to autoimmune diseases   ※ Main authors: Do-Young Ku (KAIST, first author), Ye-Won Yang (KAIST, second author), Seunghee Cha (University of Florida, corresponding author), Yoosik Kim (KAIST, corresponding author)   This study was supported by the Ministry of Health and Welfare's Public Health Technology Research Program and the National Institutes of Health (NIH) through Research Project (R01) funding.

KAIST ＆ CMU Unveils Amuse, a Songwriting AI-Collab..

Wouldn't it be great if music creators had someone to brainstorm with, help them when they're stuck, and explore different musical directions together? Researchers of KAIST and Carnegie Mellon University (CMU) have developed AI technology similar to a fellow songwriter who helps create music.   KAIST (President Kwang-Hyung Lee) has developed an AI-based music creation support system, Amuse, by a research team led by Professor Sung-Ju Lee of the School of Electrical Engineering in collaboration with CMU. The research was presented at the ACM Conference on Human Factors in Computing Systems (CHI), one of the world’s top conferences in human-computer interaction, held in Yokohama, Japan from April 26 to May 1. It received the Best Paper Award, given to only the top 1％ of all submissions.       < (From left) Professor Chris Donahue of Carnegie Mellon University, Ph.D. Student Yewon Kim and Professor Sung-Ju Lee of the School of Electrical Engineering >   The system developed by Professor Sung-Ju Lee’s research team, Amuse, is an AI-based system that converts various forms of inspiration such as text, images, and audio into harmonic structures (chord progressions) to support composition.   For example, if a user inputs a phrase, image, or sound clip such as “memories of a warm summer beach”, Amuse automatically generates and suggests chord progressions that match the inspiration.   Unlike existing generative AI, Amuse is differentiated in that it respects the user's creative flow and naturally induces creative exploration through an interactive method that allows flexible integration and modification of AI suggestions.   The core technology of the Amuse system is a generation method that blends two approaches: a large language model creates music code based on the user's prompt and inspiration, while another AI model, trained on real music data, filters out awkward or unnatural results using rejection sampling.       < Figure 1. Amuse system configuration. After extracting music keywords from user input, a large language model-based code progression is generated and refined through rejection sampling (left). Code extraction from audio input is also possible (right). The bottom is an example visualizing the chord structure of the generated code. >   The research team conducted a user study targeting actual musicians and evaluated that Amuse has high potential as a creative companion, or a Co-Creative AI, a concept in which people and AI collaborate, rather than having a generative AI simply put together a song.   The paper, in which a Ph.D. student Yewon Kim and Professor Sung-Ju Lee of KAIST School of Electrical and Electronic Engineering and Carnegie Mellon University Professor Chris Donahue participated, demonstrated the potential of creative AI system design in both academia and industry.   ※ Paper title: Amuse: Human-AI Collaborative Songwriting with Multimodal Inspirations DOI: https://doi.org/10.1145/3706598.3713818   ※ Research demo video: https://youtu.be/udilkRSnftI?si=FNXccC9EjxHOCrm1   ※ Research homepage: https://nmsl.kaist.ac.kr/projects/amuse/   Professor Sung-Ju Lee said, “Recent generative AI technology has raised concerns in that it directly imitates copyrighted content, thereby violating the copyright of the creator, or generating results one-way regardless of the creator’s intention. Accordingly, the research team was aware of this trend, paid attention to what the creator actually needs, and focused on designing an AI system centered on the creator.”   He continued, “Amuse is an attempt to explore the possibility of collaboration with AI while maintaining the initiative of the creator, and is expected to be a starting point for suggesting a more creator-friendly direction in the development of music creation tools and generative AI systems in the future.”   This research was conducted with the support of the National Research Foundation of Korea with funding from the government (Ministry of Science and ICT). (RS-2024-00337007)

KAIST Identifies Master Regulator Blocking Immunot..

Immune checkpoint inhibitors, a class of immunotherapies that help immune cells attack cancer more effectively, have revolutionized cancer treatment. However, fewer than 20％ of patients respond to these treatments, highlighting the urgent need for new strategies tailored to both responders and non-responders.   KAIST researchers have discovered that 'DEAD-box helicases 54 (DDX54)', a type of RNA-binding protein, is the master regulator that hinders the effectiveness of immunotherapy—opening a new path for lung cancer treatment. This breakthrough technology has been transferred to faculty startup BioRevert Inc., where it is currently being developed as a companion therapeutic and is expected to enter clinical trials by 2028.       < Photo 1. (From left) Researcher Jungeun Lee, Professor Kwang-Hyun Cho and Postdoctoral Researcher Jeong-Ryeol Gong of the Department of Bio and Brain Engineering at KAIST >   KAIST (represented by President Kwang-Hyung Lee) announced on April 8 that a research team led by Professor Kwang-Hyun Cho from the Department of Bio and Brain Engineering had identified DDX54 as a critical factor that determines the immune evasion capacity of lung cancer cells. They demonstrated that suppressing DDX54 enhances immune cell infiltration into tumors and significantly improves the efficacy of immunotherapy.   Immunotherapy using anti-PD-1 or anti-PD-L1 antibodies is considered a powerful approach in cancer treatment. However, its low response rate limits the number of patients who actually benefit.   To identify likely responders, tumor mutational burden (TMB) has recently been approved by the FDA as a key biomarker for immunotherapy. Cancers with high mutation rates are thought to be more responsive to immune checkpoint inhibitors. However, even tumors with high TMB can display an “immune-desert” phenotype—where immune cell infiltration is severely limited—resulting in poor treatment responses.       < Figure 1. DDX54 was identified as the master regulator that induces resistance to immunotherapy by orchestrating suppression of immune cell infiltration through cancer tissues as lung cancer cells become immune-evasive >   Professor Kwang-Hyun Cho's research team compared transcriptome and genome data of lung cancer patients with immune evasion capabilities through gene regulatory network analysis (A) and discovered DDX54, a master regulator that induces resistance to immunotherapy (B-F).   This study is especially significant in that it successfully demonstrated that suppressing DDX54 in immune-desert lung tumors can overcome immunotherapy resistance and improve treatment outcomes.   The team used transcriptomic and genomic data from immune-evasive lung cancer patients and employed systems biology techniques to infer gene regulatory networks. Through this analysis, they identified DDX54 as a central regulator in the immune evasion of lung cancer cells.   In a syngeneic mouse model, the suppression of DDX54 led to significant increases in the infiltration of anti-cancer immune cells such as T cells and NK cells, and greatly improved the response to immunotherapy.   Single-cell transcriptomic and spatial transcriptomic analyses further showed that combination therapy targeting DDX54 promoted the differentiation of T cells and memory T cells that suppress tumors, while reducing the infiltration of regulatory T cells and exhausted T cells that support tumor growth.       < Figure 2. In the syngeneic mouse model made of lung cancer cells, it was confirmed that inhibiting DDX54 reversed the immune-evasion ability of cancer cells and enhanced the sensitivity to anti-PD-1 therapy >   In a syngeneic mouse model made of lung cancer cells exhibiting immunotherapy resistance, the treatment applied after DDX54 inhibition resulted in statistically significant inhibition of lung cancer growth (B-D) and a significant increase in immune cell infiltration into the tumor tissue (E, F).   The mechanism is believed to involve DDX54 suppression inactivating signaling pathways such as JAK-STAT, MYC, and NF-κB, thereby downregulating immune-evasive proteins CD38 and CD47. This also reduced the infiltration of circulating monocytes—which promote tumor development—and promoted the differentiation of M1 macrophages that play anti-tumor roles.   Professor Kwang-Hyun Cho stated, “We have, for the first time, identified a master regulatory factor that enables immune evasion in lung cancer cells. By targeting this factor, we developed a new therapeutic strategy that can induce responsiveness to immunotherapy in previously resistant cancers.”   He added, “The discovery of DDX54—hidden within the complex molecular networks of cancer cells—was made possible through the systematic integration of systems biology, combining IT and BT.”   The study, led by Professor Kwang-Hyun Cho, was published in the Proceedings of the National Academy of Sciences of the United States of America (PNAS) on April 2, 2025, with Jeong-Ryeol Gong being the first author, Jungeun Lee, a co-first author, and Younghyun Han, a co-author of the article.       < Figure 3. Single-cell transcriptome and spatial transcriptome analysis confirmed that knockdown of DDX54 increased immune cell infiltration into cancer tissues >   In a syngeneic mouse model made of lung cancer cells that underwent immunotherapy in combination with DDX54 inhibition, single-cell transcriptome (H-L) and spatial transcriptome (A-G) analysis of immune cells infiltrating inside cancer tissues were performed. As a result, it was confirmed that anticancer immune cells such as T cells, B cells, and NK cells actively infiltrated the core of lung cancer tissues when DDX54 inhibition and immunotherapy were concurrently administered.   (Paper title: “DDX54 downregulation enhances anti-PD1 therapy in immune-desert lung tumors with high tumor mutational burden,” DOI: https://doi.org/10.1073/pnas.2412310122)   This work was supported by the Ministry of Science and ICT and the National Research Foundation of Korea through the Mid-Career Research Program and Basic Research Laboratory Program.       < Figure 4. The identified master regulator DDX54 was confirmed to induce CD38 and CD47 expression through Jak-Stat3, MYC, and NF-κB activation. >   DDX54 activates the Jak-Stat3, MYC, and NF-κB pathways in lung cancer cells to increase CD38 and CD47 expression (A-G). This creates a cancer microenvironment that contributes to cancer development (H) and ultimately induces immune anticancer treatment resistance.       < Figure 5. It was confirmed that an immune-inflamed environment can be created by combining DDX54 inhibition and immune checkpoint inhibitor (ICI) therapy. >   When DDX54 inhibition and ICI therapy are simultaneously administered, the cancer cell characteristics change, the immune evasion ability is restored, and the environment is transformed into an ‘immune-activated’ environment in which immune cells easily infiltrate cancer tissues. This strengthens the anticancer immune response, thereby increasing the sensitivity of immunotherapy even in lung cancer tissues that previously had low responsiveness to immunotherapy.

KAIST Develops Eco-Friendly, Nylon-Like Plastic Us..

Poly(ester amide) amide is a next-generation material that combines the advantages of PET (polyester) and nylon (polyamide), two widely used plastics. However, it could only be produced from fossil fuels, which posed environmental concerns. Using microorganisms, KAIST researchers have successfully developed a new bio-based plastic to replace conventional plastic.       < Photo 1. (From left) Professor Sang Yup Lee, Dr. Tong Un Chae, Dr. So Young Choi, and Ph.D. candidate Da-Hee Ahn of the Department of Chemical and Biomolecular Engineering >   KAIST (represented by President Kwang Hyung Lee) announced on the 20th of March that a research team led by Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering has developed microbial strains through systems metabolic engineering to produce various eco-friendly, bio-based poly(ester amide)s. The team collaborated with researchers from the Korea Research Institute of Chemical Technology (KRICT, President Young-Kook Lee) to analyze and confirm the properties of the resulting plastic.   Professor Sang Yup Lee’s research team designed new metabolic pathways that do not naturally exist in microorganisms, and developed a platform microbial strain capable of producing nine different types of poly(ester amide)s, including poly(3-hydroxybutyrate-ran-3-aminopropionate) and poly(3-hydroxybutyrate-ran-4-aminobutyrate).   Using glucose derived from abundant biomass sources such as waste wood and weeds, the team successfully produced poly(ester amide)s in an eco-friendly manner. The researchers also confirmed the potential for industrial-scale production by demonstrating high production efficiency (54.57 g/L) using fed-batch fermentation of the engineered strain.   In collaboration with researchers Haemin Jeong and Jihoon Shin from KRICT, the KAIST team analyzed the properties of the bio-based plastic and found that it exhibited characteristics similar to high-density polyethylene (HDPE). This means the new plastic is not only eco-friendly but also strong and durable enough to replace conventional plastics.       < Figure 1. New-to-nature metabolic pathways for the production of poly(ester amide)s (PEAs). >   The engineered strains and strategies developed in this study are expected to be useful not only for producing various poly(ester amide)s but also for constructing metabolic pathways for the biosynthesis of other types of polymers.   Professor Sang Yup Lee stated, “This study is the first to demonstrate the possibility of producing poly(ester amide)s (plastics) through a renewable bio-based chemical process rather than relying on the petroleum-based chemical industry. We plan to further enhance the production yield and efficiency through continued research.”   The study was published online on March 17 in the international journal Nature Chemical Biology.   ·Title: Biosynthesis of poly(ester amide)s in engineered Escherichia coli   ·DOI: 10.1038/s41589-025-01842-2   ·Authors: A total of seven authors including Tong Un Chae (KAIST, first author), So Young Choi (KAIST, second author), Da-Hee Ahn (KAIST, third author), Woo Dae Jang (KAIST, fourth author), Haemin Jeong (KRICT, fifth author), Jihoon Shin (KRICT, sixth author), and Sang Yup Lee (KAIST, corresponding author).   This research was supported by the Ministry of Science and ICT (MSIT) under the Eco-Friendly Chemical Technology Development Project as part of the "Next-Generation Biorefinery Technology Development to Lead the Bio-Chemical Industry" initiative (project led by Distinguished Professor Sang Yup Lee at KAIST).

KAIST Develops World-Leading Ammonia Catalyst for ..

Hydrogen production using renewable energy is a key technology for eco-friendly energy and chemical production. However, storing and transporting hydrogen remains a challenge. To address this, researchers worldwide are investigating methods to store hydrogen in the form of ammonia (NH₃), which is carbon-free and easier to liquify. A research team at KAIST has successfully developed a high-performance catalyst that enables ammonia synthesis at very low temperatures and pressures without energy loss.   KAIST (represented by President Kwang Hyung Lee) announced on the 11th of March that a research team led by Professor Minkee Choi from the Department of Chemical and Biomolecular Engineering has developed an innovative catalytic system that significantly enhances ammonia production while drastically reducing energy consumption and CO₂ emissions.       < (From left) Baek Ye-jun, Ph.D. candidate in the Department of Biochemical Engineering, Professor Choi Min-ki >     Currently, ammonia is produced using the Haber-Bosch process, a technology over a century old that relies on iron (Fe)-based catalysts. This method requires extreme conditions—temperatures above 500°C and pressures exceeding 100 atmospheres—resulting in enormous energy consumption and contributing significantly to global CO₂ emissions. Additionally, ammonia is primarily produced in large-scale industrial plants, leading to high distribution costs.   As an alternative, there is growing interest in an eco-friendly process that synthesizes ammonia using green hydrogen—produced via water electrolysis—under mild conditions (300°C, 10 atmospheres). However, developing catalysts that can achieve high ammonia productivity at such low temperatures and pressures is essential, as current technologies struggle to maintain efficiency under these conditions.   The research team developed a novel catalyst by incorporating ruthenium (Ru) nanoparticles and highly basic barium oxide (BaO) particles onto a conductive carbon surface, allowing it to function like a chemical capacitor＊.   ＊Capacitor: A device that stores electrical energy by separating positive and negative charges.   During ammonia synthesis, hydrogen molecules (H₂) first dissociate into hydrogen atoms (H) on the ruthenium catalyst. These hydrogen atoms are further split into protons (H⁺) and electrons (e⁻). The study revealed that the acidic protons are stored in the strongly basic BaO, while the remaining electrons are separated and stored in ruthenium and carbon.   This unique chemical capacitor effect significantly enhances the ruthenium catalyst's electron density, accelerating nitrogen (N₂) dissociation—the rate-limiting step of ammonia synthesis—thereby dramatically increasing catalytic activity.   Furthermore, the team discovered that optimizing the nanostructure of the carbon material further boosts the electron density of ruthenium, maximizing catalytic performance. As a result, the new catalyst demonstrated over seven times higher ammonia synthesis performance compared to state-of-the-art catalysts under mild conditions (300°C, 10 atm).       < Schematic diagram showing the mechanism of ruthenium catalyst activity enhancement by barium oxide cocatalyst >     Professor Minkee Choi stated, “This research has garnered significant attention for demonstrating that catalytic activity can be greatly enhanced by controlling electron transfer within a thermal catalytic reaction system, not just in electrochemical processes.”   He further explained, “Our findings confirm that high-performance catalysts can enable efficient ammonia synthesis under low-temperature and low-pressure conditions. This could shift ammonia production from centralized, large-scale industrial plants to decentralized, small-scale production, making the hydrogen economy more sustainable and flexible.”   The study was led by Professor Minkee Choi as corresponding author and Yaejun Baik, a Ph.D. candidate, as first author. The research findings were published in Nature Catalysis on February 24.   (Paper title: “Electron and proton storage on separate Ru and BaO domains mediated by conductive low-work-function carbon to accelerate ammonia synthesis,” https://doi.org/10.1038/s41929-025-01302-z)   This research was supported by the Korea Institute of Energy Research and the National Research Foundation of Korea.

No More Touch Issues on Rainy Days！ KAIST Develops..

  < Photo 1. (From left) Professor Jun-Bo Yoon and Dr. Jae-Soon Yang of KAIST with (top left) Myung-Kun Chung, a student of integrated master >   Recent advancements in robotics have enabled machines to handle delicate objects like eggs with precision, thanks to highly integrated pressure sensors that provide detailed tactile feedback. However, even the most advanced robots struggle to accurately detect pressure in complex environments involving water, bending, or electromagnetic interference. A research team at KAIST has successfully developed a pressure sensor that operates stably without external interference, even on wet surfaces like a smartphone screen covered in water, achieving human-level tactile sensitivity.   KAIST (represented by President Kwang Hyung Lee) announced on the 10th of March that a research team led by Professor Jun-Bo Yoon from the School of Electrical Engineering has developed a high-resolution pressure sensor that remains unaffected by external interference such as "ghost touches" caused by moisture on touchscreens.   Capacitive pressure sensors, widely used in touch systems due to their simple structure and durability, are essential components of human-machine interface (HMI) technologies in smartphones, wearable devices, and robots. However, they are prone to malfunctions caused by water droplets, electromagnetic interference, and curves.       < Figure 1. (Left) Schematic diagram of a smartphone surface that does not respond well to touch when wet on a rainy day. (Center) Schematic diagram of an unintended sensor malfunction in a situation where interference exists. (Right) Simulation results of electric field distribution in normal situations and situations where interference exists. When interference exists, distortion of the fringe field occurs. >   To address these issues, the research team investigated the root causes of interference in capacitive pressure sensors. They identified that the "fringe field" generated at the sensor’s edges is particularly susceptible to external disturbances.   The researchers concluded that, to fundamentally resolve this issue, suppressing the fringe field was necessary. Through theoretical analysis, they determined that reducing the electrode spacing to the nanometer scale could effectively minimize the fringe field to below a few percent.       < Figure 2. (Left) Photograph of the nano-gap pressure sensor developed in this study. (Center) Schematic diagram showing that the fringe field is suppressed due to the nano-gap design, effectively blocking external interference. (Right) Electron microscope image of the actually manufactured nano-gap pressure sensor. >   Utilizing proprietary micro/nanofabrication techniques, the team developed a nanogap pressure sensor with an electrode spacing of 900 nanometers (nm). This newly developed sensor reliably detected pressure regardless of the material exerting force and remained unaffected by bending or electromagnetic interference.   Furthermore, the team successfully implemented an artificial tactile system utilizing the developed sensor’s characteristics. Human skin contains specialized pressure receptors called Merkel’s disks. To artificially mimic them, the exclusive detection of pressure was necessary, but hadn’t been achieved by conventional sensors.   Professor Yoon’s research team overcame these challenges, developing a sensor achieving a density comparable to Merkel’s discs and enabling wireless, high-precision pressure sensing.       < Figure 3. (Left) Schematic diagram of a nano-gap pressure sensor that is free from interference and has high resolution to simulate the pressure detection method of the human body. (Right) A wireless artificial tactile system implemented using a nano-gap pressure sensor to pick up a wet object. It does not react even when water gets on the surface and only precisely detects pressure. >   To explore potential applications, the researcher also developed a force touch pad system, demonstrating its ability to capture pressure magnitude and distribution with high resolution and without interference.   Professor Yoon stated, “Our nanogap pressure sensor operates reliably even in rainy conditions or sweaty environments, eliminating common touch malfunctions. We believe this innovation will significantly enhance everyday user experiences.”   He added, “This technology has the potential to revolutionize various fields, including precision tactile sensors for robotics, medical wearable devices, and next-generation augmented reality (AR) and virtual reality (VR) interfaces.”       < Figure 4. (Left) Schematic diagram of the force touch pad system implemented using a nano gap pressure sensor and the situation where water is on the sensor. (Middle) Multi-touch measurement results using the force touch pad system in the situation where water is on the sensor. (Right) 3D measurement results that precisely show the size and distribution of pressure without interference or cross-interference by water on the sensor. >   The study was led by Jae-Soon Yang (Ph.D.), Myung-Kun Chung (Ph.D. candidate), and Jae-Young Yoo (Assistant Professor at Sungkyunkwan University, a KAIST Ph.D. graduate). The research findings were published in Nature Communications on February 27, 2025. (Paper title: “Interference-Free Nanogap Pressure Sensor Array with High Spatial Resolution for Wireless Human-Machine Interface Applications”, DOI: 10.1038/s41467-025-57232-8)   This study was supported by the National Research Foundation of Korea’s Mid-Career Researcher Program and Leading Research Center Support Program.

KAIST Develops Wearable Carbon Dioxide Sensor to E..

- Professor Seunghyup Yoo’s research team of the School of Electrical Engineering developed an ultralow-power carbon dioxide (CO2) sensor using a flexible and thin organic photodiode, and succeeded in real-time breathing monitoring by attaching it to a commercial mask   - Wearable devices with features such as low power, high stability, and flexibility can be utilized for early diagnosis of various diseases such as chronic obstructive pulmonary disease and sleep apnea       < Photo 1. From the left, School of Electrical Engineering, Ph.D. candidate DongHo Choi, Professor Seunghyup Yoo, and Department of Materials Science and Engineering, Bachelor’s candidate MinJae Kim >   Carbon dioxide (CO2) is a major respiratory metabolite, and continuous monitoring of CO2 concentration in exhaled breath is not only an important indicator for early detection and diagnosis of respiratory and circulatory system diseases, but can also be widely used for monitoring personal exercise status. KAIST researchers succeeded in accurately measuring CO2 concentration by attaching it to the inside of a mask.   KAIST (President Kwang-Hyung Lee) announced on the 10th that Professor Seunghyup Yoo's research team in the Department of Electrical and Electronic Engineering developed a low-power, high-speed wearable CO2 sensor capable of stable breathing monitoring in real time.   Existing non-invasive CO2 sensors had limitations in that they were large in size and consumed high power. In particular, optochemical CO2 sensors using fluorescent molecules have the advantage of being miniaturized and lightweight, but due to the photodegradation phenomenon of dye molecules, they are difficult to use stably for a long time, which limits their use as wearable healthcare sensors.   Optochemical CO2 sensors utilize the fact that the intensity of fluorescence emitted from fluorescent molecules decreases depending on the concentration of CO2, and it is important to effectively detect changes in fluorescence light.   To this end, the research team developed a low-power CO2 sensor consisting of an LED and an organic photodiode surrounding it. Based on high light collection efficiency, the sensor, which minimizes the amount of excitation light irradiated on fluorescent molecules, achieved a device power consumption of 171 μW, which is tens of times lower than existing sensors that consume several mW.       < Figure 1. Structure and operating principle of the developed optochemical carbon dioxide (CO2) sensor. Light emitted from the LED is converted into fluorescence through the fluorescent film, reflected from the light scattering layer, and incident on the organic photodiode. CO2 reacts with a small amount of water inside the fluorescent film to form carbonic acid (H2CO3), which increases the concentration of hydrogen ions (H＋), and the fluorescence intensity due to 470 nm excitation light decreases. The circular organic photodiode with high light collection efficiency effectively detects changes in fluorescence intensity, lowers the power required light up the LED, and reduces light-induced deterioration. >   The research team also elucidated the photodegradation path of fluorescent molecules used in CO2 sensors, revealed the cause of the increase in error over time in photochemical sensors, and suggested an optical design method to suppress the occurrence of errors.   Based on this, the research team developed a sensor that effectively reduces errors caused by photodegradation, which was a chronic problem of existing photochemical sensors, and can be used continuously for up to 9 hours while existing technologies based on the same material can be used for less than 20 minutes, and can be used multiple times when replacing the CO2 detection fluorescent film.       < Figure 2. Wearable smart mask and real-time breathing monitoring. The fabricated sensor module consists of four elements (①: gas-permeable light-scattering layer, ②: color filter and organic photodiode, ③: light-emitting diode, ④: CO2-detecting fluorescent film). The thin and light sensor (D1: 400 nm, D2: 470 nm) is attached to the inside of the mask to monitor the wearer's breathing in real time. >   The developed sensor accurately measured CO2 concentration by being attached to the inside of a mask based on the advantages of being light (0.12 g), thin (0.7 mm), and flexible. In addition, it showed fast speed and high resolution that can monitor respiratory rate by distinguishing between inhalation and exhalation in real time.       < Photo 2. The developed sensor attached to the inside of the mask >   Professor Seunghyup Yoo said, "The developed sensor has excellent characteristics such as low power, high stability, and flexibility, so it can be widely applied to wearable devices, and can be used for the early diagnosis of various diseases such as hypercapnia, chronic obstructive pulmonary disease, and sleep apnea." He added, "In particular, it is expected to be used to improve side effects caused by rebreathing in environments where dust is generated or where masks are worn for long periods of time, such as during seasonal changes."   This study, in which KAIST's Department of Materials Science and Engineering's undergraduate student Minjae Kim and School of Electrical Engineering's doctoral student Dongho Choi participated as joint first authors, was published in the online version of Cell's sister journal, Device, on the 22nd of last month. (Paper title: Ultralow-power carbon dioxide sensor for real-time breath monitoring) DOI: https://doi.org/10.1016/j.device.2024.100681       < Photo 3. From the left, Professor Seunghyup Yoo of the School of Electrical Engineering, MinJae Kim, an undergraduate student in the Department of Materials Science and Engineering, and Dongho Choi, a doctoral student in the School of Electrical Engineering >     This study was supported by the Ministry of Trade, Industry and Energy's Materials and Components Technology Development Project, the National Research Foundation of Korea's Original Technology Development Project, and the KAIST Undergraduate Research Participation Project. This work was supported by the (URP) program.

KAIST Discovers Molecular Switch that Reverses Can..

  < (From left) PhD student Seoyoon D. Jeong, (bottom) Professor Kwang-Hyun Cho, (top) Dr. Dongkwan Shin, Dr. Jeong-Ryeol Gong >   Professor Kwang-Hyun Cho’s research team has recently been highlighted for their work on developing an original technology for cancer reversal treatment that does not kill cancer cells but only changes their characteristics to reverse them to a state similar to normal cells. This time, they have succeeded in revealing for the first time that a molecular switch that can induce cancer reversal at the moment when normal cells change into cancer cells is hidden in the genetic network.   KAIST (President Kwang-Hyung Lee) announced on the 5th of February that Professor Kwang-Hyun Cho's research team of the Department of Bio and Brain Engineering has succeeded in developing a fundamental technology to capture the critical transition phenomenon at the moment when normal cells change into cancer cells and analyze it to discover a molecular switch that can revert cancer cells back into normal cells.   A critical transition is a phenomenon in which a sudden change in state occurs at a specific point in time, like water changing into steam at 100℃. This critical transition phenomenon also occurs in the process in which normal cells change into cancer cells at a specific point in time due to the accumulation of genetic and epigenetic changes.   The research team discovered that normal cells can enter an unstable critical transition state where normal cells and cancer cells coexist just before they change into cancer cells during tumorigenesis, the production or development of tumors, and analyzed this critical transition state using a systems biology method to develop a cancer reversal molecular switch identification technology that can reverse the cancerization process. They then applied this to colon cancer cells and confirmed through molecular cell experiments that cancer cells can recover the characteristics of normal cells.   This is an original technology that automatically infers a computer model of the genetic network that controls the critical transition of cancer development from single-cell RNA sequencing data, and systematically finds molecular switches for cancer reversion by simulation analysis. It is expected that this technology will be applied to the development of reversion therapies for other cancers in the future.   Professor Kwang-Hyun Cho said, "We have discovered a molecular switch that can revert the fate of cancer cells back to a normal state by capturing the moment of critical transition right before normal cells are changed into an irreversible cancerous state."       < Figure 1. Overall conceptual framework of the technology that automatically constructs a molecular regulatory network from single-cell RNA sequencing data of colon cancer cells to discover molecular switches for cancer reversion through computer simulation analysis. Professor Kwang-Hyun Cho's research team established a fundamental technology for automatic construction of a computer model of a core gene network by analyzing the entire process of tumorigenesis of colon cells turning into cancer cells, and developed an original technology for discovering the molecular switches that can induce cancer cell reversal through attractor landscape analysis. >   He continued, "In particular, this study has revealed in detail, at the genetic network level, what changes occur within cells behind the process of cancer development, which has been considered a mystery until now." He emphasized, "This is the first study to reveal that an important clue that can revert the fate of tumorigenesis is hidden at this very critical moment of change."       < Figure 2. Identification of tumor transition state using single-cell RNA sequencing data from colorectal cancer. Using single-cell RNA sequencing data from colorectal cancer patient-derived organoids for normal and cancerous tissues, a critical transition was identified in which normal and cancerous cells coexist and instability increases (a-d). The critical transition was confirmed to show intermediate levels of major phenotypic features related to cancer or normal tissues that are indicative of the states between the normal and cancerous cells (e). >   The results of this study, conducted by KAIST Dr. Dongkwan Shin (currently at the National Cancer Center), Dr. Jeong-Ryeol Gong, and doctoral student Seoyoon D. Jeong jointly with a research team at Seoul National University that provided the organoids (in vitro cultured tissues) from colon cancer patient, were published as an online paper in the international journal ‘Advanced Science’ published by Wiley on January 22nd.   (Paper title: Attractor landscape analysis reveals a reversion switch in the transition of colorectal tumorigenesis) (DOI: https://doi.org/10.1002/advs.202412503)       < Figure 3. Reconstruction of a dynamic network model for the transition state of colorectal cancer.   A new technology was established to build a gene network computer model that can simulate the dynamic changes between genes by integrating single-cell RNA sequencing data and existing experimental results on gene-to-gene interactions in the critical transition of cancer. (a). Using this technology, a gene network computer model for the critical transition of colorectal cancer was constructed, and the distribution of attractors representing normal and cancer cell phenotypes was investigated through attractor landscape analysis (b-e). >   This study was conducted with the support of the National Research Foundation of Korea under the Ministry of Science and ICT through the Mid-Career Researcher Program and Basic Research Laboratory Program and the Disease-Centered Translational Research Project of the Korea Health Industry Development Institute (KHIDI) of the Ministry of Health and Welfare.       < Figure 4. Quantification of attractor landscapes and discovery of transcription factors for cancer reversibility through perturbation simulation analysis. A methodology for implementing discontinuous attractor landscapes continuously from a computer model of gene networks and quantifying them as cancer scores was introduced (a), and attractor landscapes for the critical transition of colorectal cancer were secured (b-d). By tracking the change patterns of normal and cancer cell attractors through perturbation simulation analysis for each gene, the optimal combination of transcription factors for cancer reversion was discovered (e-h). This was confirmed in various parameter combinations as well (i). >       < Figure 5. Identification and experimental validation of the optimal target gene for cancer reversion. Among the common target genes of the discovered transcription factor combinations, we identified cancer reversing molecular switches that are predicted to suppress cancer cell proliferation and restore the characteristics of normal colon cells (a-d). When inhibitors for the molecular switches were treated to organoids derived from colon cancer patients, it was confirmed that cancer cell proliferation was suppressed and the expression of key genes related to cancer development was inhibited (e-h), and a group of genes related to normal colon epithelium was activated and transformed into a state similar to normal colon cells (i-j). >       < Figure 6. Schematic diagram of the research results. Professor Kwang-Hyun Cho's research team developed an original technology to systematically discover key molecular switches that can induce reversion of colon cancer cells through a systems biology approach using an attractor landscape analysis of a genetic network model for the critical transition at the moment of transformation from normal cells to cancer cells, and verified the reversing effect of actual colon cancer through cellular experiments. >