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Better Sleep, Better Life — KAIST’s Sleep Algorithm Comes to Samsung Galaxy Watches
<Professor Jae Kyoung Kim of KAIST's Department of Mathematical Sciences>
Did you know that over 80% of people worldwide have irregular sleep habits? These sleep issues don’t just leave us feeling tired — they affect our health, focus, and quality of life. Now, a new sleep algorithm developed by a team of Korean researchers is aiming to change that. And it’s available on Samsung Galaxy smartwatches around the world, including the newly launched Galaxy Watch8 series.
The personalized sleep guide, created by Professor Jae Kyoung Kim’s research team at KAIST and the Institute for Basic Science (IBS), doesn’t just tell you how long you slept. It actually recommends the best time for you to go to bed — helping you build healthy sleep habits and feel more refreshed every day.
What makes it special? Unlike most sleep features that focus only on the past (“You slept six hours last night”), this algorithm looks ahead. Using mathematical models and your body’s circadian rhythm, it suggests a personalized “sleep window” — like “Going to bed between 11:10 PM and 11:40 PM is ideal for you tonight.”
“It’s kind of like a weather forecast,” said Professor Kim. “Instead of just telling you what happened yesterday, it helps you prepare for tomorrow — so you can sleep better and feel better.”
<Conceptual Diagram of a Smart Sleep Algorithm>
The algorithm was developed over three years by a small team of mathematicians, not professional app developers. “We faced a lot of challenges trying to turn our research into a real product,” Kim admitted. “People kept asking us when they could try the algorithm, and we always felt bad that we couldn’t release it properly. Now, thanks to the support of KAIST’s Technology Commercialization Center and our partnership with Samsung, our work will finally reach people around the world.”
The academic world is paying attention, too. Professor Kim’s presentation on the algorithm was selected for the Hot Topics session at SLEEP 2025, the world’s largest sleep conference held in the U.S., and will also be featured at World Sleep 2025 in Singapore this fall.
Professor Kim is also working with Professor Eun Yeon Joo’s team at Samsung Medical Center to develop even more advanced sleep recommendation technology. Together, they created “SLEEPS,” an algorithm that predicts sleep disorders (available at sleep-math.com). Meanwhile, development continues on their own sleep app — with the hope of bringing math-powered sleep science into more people’s everyday lives.
Professor Kim is a world-renowned expert in mathematical biology. In 2025, he became the first Korean scientist to give a keynote speech at the SIAM Annual Meeting, and the first Korean to join the editorial board of SIAM Review, one of the most prestigious journals in applied mathematics. His work shows how basic science and mathematics can lead to real solutions that help people live healthier, better lives.
2025.07.28 View 509 -
Approaches to Human-Robot Interaction Using Biosignals
<(From left) Dr. Hwa-young Jeong, Professor Kyung-seo Park, Dr. Yoon-tae Jeong, Dr. Ji-hoon Seo, Professor Min-kyu Je, Professor Jung Kim >
A joint research team led by Professor Jung Kim of KAIST Department of Mechanical Engineering and Professor Min-kyu Je of the Department of Electrical and Electronic Engineering recently published a review paper on the latest trends and advancements in intuitive Human-Robot Interaction (HRI) using bio-potential and bio-impedance in the internationally renowned academic journal 'Nature Reviews Electrical Engineering'.
This review paper is the result of a collaborative effort by Dr. Kyung-seo Park (DGIST, co-first author), Dr. Hwa-young Jeong (EPFL, co-first author), Dr. Yoon-tae Jeong (IMEC), and Dr. Ji-hoon Seo (UCSD), all doctoral graduates from the two laboratories. Nature Reviews Electrical Engineering is a review specialized journal in the field of electrical, electronic, and artificial intelligence technology, newly launched by Nature Publishing Group last year. It is known to invite world-renowned scholars in the field through strict selection criteria. Professor Jung Kim's research team's paper, titled "Using bio-potential and bio-impedance for intuitive human-robot interaction," was published on July 18, 2025. (DOI: https://doi.org/10.1038/s44287-025-00191-5)
This review paper explains how biosignals can be used to quickly and accurately detect movement intentions and introduces advancements in movement prediction technology based on neural signals and muscle activity. It also focuses on the crucial role of integrated circuits (ICs) in maximizing low-noise performance and energy efficiency in biosignal sensing, covering thelatest development trends in low-noise, low-power designs for accurately measuring bio-potential and impedance signals.
The review emphasizes the importance of hybrid and multi-modal sensing approaches, presenting the possibility of building robust, intuitive, and scalable HRI systems. The research team stressed that collaboration between sensor and IC design fields is essential for the practical application of biosignal-based HRI systems and stated that interdisciplinary collaboration will play a significant role in the development of next-generation HRI technology. Dr. Hwa-young Jeong, a co-first author of the paper, presented the potential of bio-potential and impedance signals to make human-robot interaction more intuitive and efficient, predicting that it will make significant contributions to the development of HRI technologies such as rehabilitation robots and robotic prostheses using biosignals in the future. This research was supported by several research projects, including the Human Plus Project of the National Research Foundation of Korea.
2025.07.24 View 575 -
KAIST School of Transdisciplinary Studies Is Driving Innovation in Korean Education
<(From Left) Professor Jaeseung Jeong, haed of the School of Transdiciplinary Studies, Dr, Albert Chau, Vice President of Hong Kong Baptist University>
KAIST (President Kwang Hyung Lee) announced on the 24th of July that its School of Transdisciplinary Studies has been consistently showcasing the results of its experiments and practices for educational innovation both domestically and abroad.
On June 27, Professor Jaeseung Jeong, head of the School of Transdisciplinary Studies, was invited to speak at the “Pacific Asia Summit on Transdisciplinary Education 2025 (PASTE 2025)” held at Hong Kong Baptist University. He presented the Korean model of transdisciplinary education under the title “The Philosophy and Achievements of the KAIST School of Transdisciplinary Studies.”
In his talk, Professor Jeong pointed out the limitations of conventional education systems that rely on answer-centered evaluation, perfectionism, and competitiveness, claiming that they hinder creativity and integrative thinking. He then introduced the philosophy and operational practices of the School of Transdisciplinary Studies, which was established in 2019 to overcome these issues.
Professor Jeong outlined five key principles that define the school's educational philosophy: ①a broad and integrative academic foundation, ②student-driven and customized education, ③creativity and execution, ④a sense of social responsibility and global citizenship, and ⑤learning driven by intrinsic motivation and curiosity. He explained that students are admitted without a declared major, allowed to design their own learning plans, and evaluated under a P/NR system* that focuses on growth rather than competition.
*P/NR system: A non-competitive grading system led by KAIST’s School of Transdisciplinary Studies. Instead of traditional letter grades (A/B/C/Fail), students receive Pass (P) or No Record (NR), with the latter not appearing as a failure and not affecting GPA.
Professor Jeong emphasized, “This experiment at KAIST represents a new educational paradigm that values questions over knowledge, culture over structure, and inquiry over competition. Students are bridging academic learning and real-world practice by addressing societal challenges through technology, which could lead to a fundamental shift in global higher education.”
His presentation provided an opportunity to spotlight how KAIST’s experimental approach to nurturing transdisciplinary talent is pointing to new directions for the global education community beyond Korea.
< Hyungjoon Jang, a student at the School of Transdisciplinary Studies>
The achievements of KAIST’s transdisciplinary education model are also reflected in students’ academic accomplishments. Hyungjoon Jang, a student at the School of Transdisciplinary Studies, participated in a collaborative study led by his mentor, Professor Jaekyung Kim in the Department of Mathematical Sciences, along with researchers from Chungnam National University and the Institute for Basic Science (IBS). Their groundbreaking analytical method enables the accurate estimation of inhibition constants using only a single inhibitor concentration. The paper was published in the prestigious journal Nature Communications in June, with Jang listed as co–first author.
Jang played a leading role throughout the research process by developing the experimental methodology, creating a software package to support the method, drafting the manuscript, and engaging in peer review. He also effectively communicated mathematical and statistical models to pharmaceutical experts by mastering presentation techniques and visual explanation strategies, thereby setting a strong example of interdisciplinary collaboration.
He emphasized that “the School of Transdisciplinary Studies’ mentor system allowed regular research feedback and the systematic acquisition of essential knowledge and analytical skills through courses in biochemistry and computational neuroscience.”
This example demonstrates how undergraduate students at the School of Transdisciplinary Studies can take leading roles in cutting-edge interdisciplinary research.
The school’s educational philosophy is also reflected in students’ practical actions. Inseo Jeong, a current student and founder of the startup MPAge Inc., made a meaningful donation to help establish a creative makerspace in the school.
<Inseo Jeong, founder of MPAG>
Inseo Jeong explained that the decision was made to express gratitude for the knowledge gained and the mentorship received from professors, saying that at the School of Transdisciplinary Studies, she learned not only how to solve problems with technology but also how to view society, and that learning has helped her grow. She added, “The deep understanding of humanity and the world emphasized by Professor Jaeseung Jeong will be a great asset not only to entrepreneurs but to all students pursuing diverse paths,” expressing support for her fellow students.
Inseo Jeong collaborated for over two years with Professor Hyunwook Ka of the School of Transdisciplinary Studies on software research for individuals with hearing impairments. After numerous algorithm designs and experimental iterations, their work, which considered the social scalability of technology, was presented at the world-renowned CSUN Assistive Technology Conference held at California State University, Northridge. The project has filed for a patent under KAIST’s name.
※ Presentation title: Evidence-Based Adaptive Transcription for Sign Language Users
KAIST is now working to complete the makerspace on the third floor of the Administrative Annex (N2) in Room 314 with a size of approximately 33 m2 during the summer. The makerspace is expected to serve as a hands-on, integrative learning environment where various ideas can be realized and implemented, playing a key role in fostering students’ creative problem-solving and integrative thinking skills.
KAIST President Kwang Hyung Lee stated, “The School of Transdisciplinary Studies is both an experimental ground and a practical field for overcoming the limitations of traditional education and nurturing global talents with creative problem-solving skills and integrative thinking, which are essential for the future.” He added, “KAIST will continue to lead efforts to cultivate question-asking, inquiry-driven, transdisciplinary talents and propose new paradigms for education and research.”
2025.07.24 View 402 -
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 386 -
Why Do Plants Attack Themselves? The Secret of Genetic Conflict Revealed
<Professor Ji-Joon Song of the KAIST Department of Biological Sciences>
Plants, with their unique immune systems, sometimes launch 'autoimmune responses' by mistakenly identifying their own protein structures as pathogens. In particular, 'hybrid necrosis,' a phenomenon where descendant plants fail to grow healthily and perish after cross-breeding different varieties, has long been a difficult challenge for botanists and agricultural researchers. In response, an international research team has successfully elucidated the mechanism inducing plant autoimmune responses and proposed a novel strategy for cultivar improvement that can predict and avoid these reactions.
Professor Ji-Joon Song's research team at KAIST, in collaboration with teams from the National University of Singapore (NUS) and the University of Oxford, announced on the 21st of July that they have elucidated the structure and function of the 'DM3' protein complex, which triggers plant autoimmune responses, using cryo-electron microscopy (Cryo-EM) technology.
This research is drawing attention because it identifies defects in protein structure as the cause of hybrid necrosis, which occurs due to an abnormal reaction of immune receptors during cross-breeding between plant hybrids.
This protein (DM3) is originally an enzyme involved in the plant's immune response, but problems arise when the structure of the DM3 protein is damaged in a specific protein combination called 'DANGEROUS MIX (DM)'.
Notably, one variant of DM3, the 'DM3Col-0' variant, forms a stable complex with six proteins and is recognized as normal, thus not triggering an immune response. In contrast, another 'DM3Hh-0' variant has improper binding between its six proteins, causing the plant to recognize it as an 'abnormal state' and trigger an immune alarm, leading to autoimmunity.
The research team visualized this structure using atomic-resolution cryo-electron microscopy (Cryo-EM) and revealed that the immune-inducing ability is not due to the enzymatic function of the DM3 protein, but rather to 'differences in protein binding affinity.'
<Figure 1. Mechanism of Plant Autoimmunity Triggered by the Collapse of the DM3 Protein Complex>
This demonstrates that plants can initiate an immune response by recognizing not only 'external pathogens' but also 'internal protein structures' when they undergo abnormal changes, treating them as if they were pathogens.
The study shows how sensitively the plant immune system changes and triggers autoimmune responses when genes are mixed and protein structures change during the cross-breeding of different plant varieties. It significantly advanced the understanding of genetic incompatibility that can occur during natural cross-breeding and cultivar improvement processes.
Dr. Gijeong Kim, the co-first author, stated, "Through international research collaboration, we presented a new perspective on understanding the plant immune system by leveraging the autoimmune phenomenon, completing a high-quality study that encompasses structural biochemistry, genetics, and cell biological experiments."
Professor Ji-Joon Song of the KAIST Department of Biological Sciences, who led the research, said, "The fact that the immune system can detect not only external pathogens but also structural abnormalities in its own proteins will set a new standard for plant biotechnology and crop breeding strategies. Cryo-electron microscopy-based structural analysis will be an important tool for understanding the essence of gene interactions."
This research, with Professor Ji-Joon Song and Professor Eunyoung Chae of the University of Oxford as co-corresponding authors, Dr. Gijeong Kim (currently a postdoctoral researcher at the University of Zurich) and Dr. Wei-Lin Wan of the National University of Singapore as co-first authors, and Ph.D candidate Nayun Kim, as the second author, was published on July 17th in Molecular Cell, a sister journal of the international academic journal Cell.
This research was supported by the KAIST Grand Challenge 30 project.
Article Title: Structural determinants of DANGEROUS MIX 3, an alpha/beta hydrolase that triggers NLR-mediated genetic incompatibility in plants DOI: https://doi.org/10.1016/j.molcel.2025.06.021
2025.07.21 View 550 -
KAIST Holds '2025 KAIST Science Frontier Camp' for Multicultural Youth
<2025 KAIST Science Frontier Camp Activities>
KAIST (President Kwang Hyung Lee) announced on the 18th of July that it hosted the '2025 KAIST Science Frontier Camp' for multicultural youth from the 15th for three days and two nights at the Creative Learning Building on its main campus in Daejeon.
This event was organized in accordance with the 'Multicultural Talent Nurturing Agreement' signed by KAIST and GS Caltex in 2024. It marks the first year of a mid-to-long-term project in which 100 million KRW in development funds will be contributed annually for four years. The Global Institute for Talented Education organized the camp, and approximately 30 middle school students from multicultural families affiliated with the 'Hanmaum Educational Volunteer Group' (Director, Honorary Professor Byung Kyu Choi), a mentoring and volunteer organization for multicultural students, participated.
The camp participants enjoyed developing their scientific thinking skills and problem-solving abilities, and broadening their understanding of STEM (Science, Technology, Engineering, and Mathematics) career paths through a variety of science activity programs, including: △'Black Box: Record the Egg's Last Moment!' △'Find the Best Strategy! Heuristic Algorithm Challenge' △'Future Society and AI, Finding Career Directions' △'Distance Dominates the World!' and △'Career Talk Concert.'
During the opening ceremony, Director Byung Kyu Choi delivered a congratulatory speech. Additionally, Yong Hyun Kim, Dean of Admissions at KAIST, gave a special lecture titled 'La La Land KAIST – A Story of Chasing the Dream of a Young Scientist,' sharing honest stories about careers and dreams as a scientist.
Gi Jung Yoo, a freshman from the Division of Undeclared Majors who participated in the camp as a student mentor, shared that he had a very meaningful time mentoring the participating students, who are future STEM hopefuls, sharing vivid experiences as well as insights on metric functions. He added his hope that more students would be given such opportunities.
< Students Actively Taking Part in the Camp Activities>
Si Jong Kwak, Director of the Global Institute for Talented Education, stated, "We hope this will be a practical way to help students foster their interest in science, learn the joy of discussion and communication, and design their future."
KAIST President Kwang Hyung Lee remarked, "This camp was a valuable opportunity for students from diverse cultural backgrounds to gain confidence through science and envision their future." He added, "KAIST will continue to dedicate efforts to nurturing multicultural talent and contribute to creating a sustainable society."
Since 2024, KAIST has introduced and selected multicultural students through its Equal Opportunity Admission track. Utilizing the development funds from GS Caltex, KAIST also established the 'GS Caltex Multicultural Excellence Scholarship Program.' Through this scholarship program, undergraduate students from multicultural families receive living expenses each semester, allowing them to focus more stably on their studies. As the number of applicants for the Equal Opportunity Admission track is increasing every year, more multicultural students are expected to benefit from scholarships in the future.
Additionally, in May, both organizations invited Ms. Si Si Wu Fong, a foreign employee at GS Caltex, to give a special lecture titled 'Working Life for Foreigners in Korea' to support foreign students' career exploration. Foreign students who attended the lecture reported positive feedback, stating that they gained practical career information and were motivated to pursue employment in STEM fields in Korea.
KAIST plans to continue strengthening its efforts to nurture multicultural talent, increase understanding of the upcoming multicultural society, and help spread social values.
<At the 2025 KAIST Science Frontier Camp>
2025.07.18 View 547 -
KAIST reveals for the first time the mechanism by which alcohol triggers liver inflammation
<(From left)Dr. Keungmo Yang, Professor Won-Il Jeong, Ph.D candidate Kyurae Kim>
Excessive alcohol consumption causes alcoholic liver disease, and about 20% of these cases progress to alcohol-associated steatohepatitis (ASH), which can lead to liver cirrhosis and liver failure. Early diagnosis and treatment are therefore extremely important. A KAIST research team has identified a new molecular mechanism in which alcohol-damaged liver cells increase reactive oxygen species (ROS), leading to cell death and inflammatory responses. In addition, they discovered that Kupffer cells, immune cells residing in the liver, act as a “dual-function regulator” that can either promote or suppress inflammation through interactions with liver cells.
KAIST (President Kwang-Hyung Lee) announced on the 17th that a research team led by Professor Won-Il Jeong from the Graduate School of Medical Science and Engineering, in collaboration with Professor Won Kim’s team at Seoul National University Boramae Medical Center, has uncovered the molecular pathway of liver damage and inflammation caused by alcohol consumption. This finding offers new clues for the diagnosis and treatment of alcohol-associated liver disease (ALD).
Professor Won-Il Jeong’s research team found that during chronic alcohol intake, expression of the vesicular glutamate transporter VGLUT3 increases, leading to glutamate accumulation in hepatocytes. Subsequent binge drinking causes rapid changes in intracellular calcium levels, which then triggers glutamate* secretion. The secreted glutamate stimulates the glutamate receptor mGluR5 on liver-resident macrophages (Kupffer cells), which induces ROS production and activates a pathological pathway resulting in hepatocyte death and inflammation.
*Glutamate: A type of amino acid involved in intercellular signaling, protein synthesis, and energy metabolism in various tissues including the brain and liver. In excess, it can cause overexcitation and death of nerve cells.
Glutamate accumulation in perivenous hepatocytes through vesicular glutamate transporter 3 after 2-week EtOH intake and its release by binge drinking>
A particularly groundbreaking aspect of this study is that damaged hepatocytes and Kupffer cells can form a "pseudosynapse"—a structure similar to a synapse which is previously thought to occur only in the brain—enabling them to exchange signals. This is the first time such a phenomenon has been identified in the liver.
This pseudosynapse forms when hepatocytes expand (ballooning) due to alcohol, becoming physically attached to Kupffer cells. Simply put, the damaged hepatocytes don’t just die—they send distress signals to nearby immune cells, prompting a response.
This discovery proposes a new paradigm: even in peripheral organs, direct structural contact between cells can allow signal transmission. It also shows that damaged hepatocytes can actively stimulate macrophages and induce regeneration through cell death, revealing the liver’s “autonomous recovery function.”
The team also confirmed in animal models that genetic or pharmacological inhibition of VGLUT3, mGluR5, or the ROS-producing enzyme NOX2 reduces alcohol-induced liver damage. They also confirmed that the same mechanism observed in animal models was present in human patients with ALD by analyzing blood and liver tissue samples.
Professor Won-Il Jeong of KAIST said, “These findings may serve as new molecular targets for early diagnosis and treatment of ASH in the future.”
This study was jointly led by Dr. Keungmo Yang (now at Yeouido St. Mary’s Hospital) and Kyurae Kim, a doctoral candidate at KAIST, who served as co–first authors. It was conducted in collaboration with Professor Won Kim’s team at Seoul National University Boramae Medical Center and was published in the journal Nature Communications on July 1.
※ Article Title: Binge drinking triggers VGLUT3-mediated glutamate secretion and subsequent hepatic inflammation by activating mGluR5/NOX2 in Kupffer cells ※ DOI: https://doi.org/10.1038/s41467-025-60820-3
This study was supported by the Ministry of Science and ICT through the National Research Foundation of Korea's Global Leader Program, Mid-Career Researcher Program, and the Bio & Medical Technology Development Program.
2025.07.17 View 671 -
KAIST Successfully Implements 3D Brain-Mimicking Platform with 6x Higher Precision
<(From left) Dr. Dongjo Yoon, Professor Je-Kyun Park from the Department of Bio and Brain Engineering, (upper right) Professor Yoonkey Nam, Dr. Soo Jee Kim>
Existing three-dimensional (3D) neuronal culture technology has limitations in brain research due to the difficulty of precisely replicating the brain's complex multilayered structure and the lack of a platform that can simultaneously analyze both structure and function. A KAIST research team has successfully developed an integrated platform that can implement brain-like layered neuronal structures using 3D printing technology and precisely measure neuronal activity within them.
KAIST (President Kwang Hyung Lee) announced on the 16th of July that a joint research team led by Professors Je-Kyun Park and Yoonkey Nam from the Department of Bio and Brain Engineering has developed an integrated platform capable of fabricating high-resolution 3D multilayer neuronal networks using low-viscosity natural hydrogels with mechanical properties similar to brain tissue, and simultaneously analyzing their structural and functional connectivity.
Conventional bioprinting technology uses high-viscosity bioinks for structural stability, but this limits neuronal proliferation and neurite growth. Conversely, neural cell-friendly low-viscosity hydrogels are difficult to precisely pattern, leading to a fundamental trade-off between structural stability and biological function. The research team completed a sophisticated and stable brain-mimicking platform by combining three key technologies that enable the precise creation of brain structure with dilute gels, accurate alignment between layers, and simultaneous observation of neuronal activity.
The three core technologies are: ▲ 'Capillary Pinning Effect' technology, which enables the dilute gel (hydrogel) to adhere firmly to a stainless steel mesh (micromesh) to prevent it from flowing, thereby reproducing brain structures with six times greater precision (resolution of 500 μm or less) than conventional methods; ▲ the '3D Printing Aligner,' a cylindrical design that ensures the printed layers are precisely stacked without misalignment, guaranteeing the accurate assembly of multilayer structures and stable integration with microelectrode chips; and ▲ 'Dual-mode Analysis System' technology, which simultaneously measures electrical signals from below and observes cell activity with light (calcium imaging) from above, allowing for the simultaneous verification of the functional operation of interlayer connections through multiple methods.
< Figure 1. Platform integrating brain-structure-mimicking neural network model construction and functional measurement technology>
The research team successfully implemented a three-layered mini-brain structure using 3D printing with a fibrin hydrogel, which has elastic properties similar to those of the brain, and experimentally verified the process of actual neural cells transmitting and receiving signals within it.
Cortical neurons were placed in the upper and lower layers, while the middle layer was left empty but designed to allow neurons to penetrate and connect through it. Electrical signals were measured from the lower layer using a microsensor (electrode chip), and cell activity was observed from the upper layer using light (calcium imaging). The results showed that when electrical stimulation was applied, neural cells in both upper and lower layers responded simultaneously. When a synapse-blocking agent (synaptic blocker) was introduced, the response decreased, proving that the neural cells were genuinely connected and transmitting signals.
Professor Je-Kyun Park of KAIST explained, "This research is a joint development achievement of an integrated platform that can simultaneously reproduce the complex multilayered structure and function of brain tissue. Compared to existing technologies where signal measurement was impossible for more than 14 days, this platform maintains a stable microelectrode chip interface for over 27 days, allowing the real-time analysis of structure-function relationships. It can be utilized in various brain research fields such as neurological disease modeling, brain function research, neurotoxicity assessment, and neuroprotective drug screening in the future."
The research, in which Dr. Soo Jee Kim and Dr. Dongjo Yoon from KAIST's Department of Bio and Brain Engineering participated as co-first authors, was published online in the international journal 'Biosensors and Bioelectronics' on June 11, 2025.
※Paper: Hybrid biofabrication of multilayered 3D neuronal networks with structural and functional interlayer connectivity
※DOI: https://doi.org/10.1016/j.bios.2025.117688
2025.07.16 View 595 -
KAIST Develops Robots That React to Danger Like Humans
<(From left) Ph.D candidate See-On Park, Professor Jongwon Lee, and Professor Shinhyun Choi>
In the midst of the co-development of artificial intelligence and robotic advancements, developing technologies that enable robots to efficiently perceive and respond to their surroundings like humans has become a crucial task. In this context, Korean researchers are gaining attention for newly implementing an artificial sensory nervous system that mimics the sensory nervous system of living organisms without the need for separate complex software or circuitry. This breakthrough technology is expected to be applied in fields such as in ultra-small robots and robotic prosthetics, where intelligent and energy-efficient responses to external stimuli are essential.
KAIST (President Kwang Hyung Lee) announced on July15th that a joint research team led by Endowed Chair Professor Shinhyun Choi of the School of Electrical Engineering at KAIST and Professor Jongwon Lee of the Department of Semiconductor Convergence at Chungnam National University (President Jung Kyum Kim) developed a next-generation neuromorphic semiconductor-based artificial sensory nervous system. This system mimics the functions of a living organism's sensory nervous system, and enables a new type of robotic system that can efficiently responds to external stimuli.
In nature, animals — including humans — ignore safe or familiar stimuli and selectively react sensitively to important or dangerous ones. This selective response helps prevent unnecessary energy consumption while maintaining rapid awareness of critical signals. For instance, the sound of an air conditioner or the feel of clothing against the skin soon become familiar and are disregarded. However, if someone calls your name or a sharp object touches your skin, a rapid focus and response occur. These behaviors are regulated by the 'habituation' and 'sensitization' functions in the sensory nervous system. Attempts have been consistently made to apply these sensory nervous system functions of living organisms in order to create robots that efficiently respond to external environments like humans.
However, implementing complex neural characteristics such as habituation and sensitization in robots has faced difficulties in miniaturization and energy efficiency due to the need for separate software or complex circuitry. In particular, there have been attempts to utilize memristors, a neuromorphic semiconductor. A memristor is a next-generation electrical device, which has been widely utilized as an artificial synapse due to its ability to store analog value in the form of device resistance. However, existing memristors had limitations in mimicking the complex characteristics of the nervous system because they only allowed simple monotonic changes in conductivity.
To overcome these limitations, the research team developed a new memristor capable of reproducing complex neural response patterns such as habituation and sensitization within a single device. By introducing additional layer inside the memristor that alter conductivity in opposite directions, the device can more realistically emulate the dynamic synaptic behaviors of a real nervous system — for example, decreasing its response to repeated safe stimuli but quickly regaining sensitivity when a danger signal is detected.
<New memristor mimicking functions of sensory nervous system such as habituation/sensitization>
Using this new memristor, the research team built an artificial sensory nervous system capable of recognizing touch and pain, an applied it to a robotic hand to test its performance. When safe tactile stimuli were repeatedly applied, the robot hand, which initially reacted sensitively to unfamiliar tactile stimuli, gradually showed habituation characteristics by ignoring the stimuli. Later, when stimuli were applied along with an electric shock, it recognized this as a danger signal and showed sensitization characteristics by reacting sensitively again. Through this, it was experimentally proven that robots can efficiently respond to stimuli like humans without separate complex software or processors, verifying the possibility of developing energy-efficient neuro-inspired robots.
<Robot arm with memristor-based artificial sensory nervous system>
See-On Park, researcher at KAIST, stated, "By mimicking the human sensory nervous system with next-generation semiconductors, we have opened up the possibility of implementing a new concept of robots that are smarter and more energy-efficient in responding to external environments." He added, "This technology is expected to be utilized in various fusion fields of next-generation semiconductors and robotics, such as ultra-small robots, military robots, and medical robots like robotic prosthetics".
This research was published online on July 1st in the international journal 'Nature Communications,' with Ph.D candidate See-On Park as the first author.
Paper Title: Experimental demonstration of third-order memristor-based artificial sensory nervous system for neuro-inspired robotics
DOI: https://doi.org/10.1038/s41467-025-60818-x
This research was supported by the Korea National Research Foundation's Next-Generation Intelligent Semiconductor Technology Development Project, the Mid-Career Researcher Program, the PIM Artificial Intelligence Semiconductor Core Technology Development Project, the Excellent New Researcher Program, and the Nano Convergence Technology Division, National Nanofab Center's (NNFC) Nano-Medical Device Project.
2025.07.16 View 757 -
A KAIST Team Engineers a Microbial Platform for Efficient Lutein Production
<(From Left) Ph.D. Candidate Hyunmin Eun, Distinguished Professor Sang Yup Lee, , Dr. Cindy Pricilia Surya Prabowo>
The application of systems metabolic engineering strategies, along with the construction of an electron channeling system, has enabled the first gram-per-liter scale production of lutein from Corynebacterium glutamicum, providing a viable alternative to plant-derived lutein production.
A research group at KAIST has successfully engineered a microbial strain capable of producing lutein at industrially relevant levels. The team, led by Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering, developed a novel C. glutamicum strain using systems metabolic engineering strategies to overcome the limitations of previous microbial lutein production efforts. This research is expected to be beneficial for the efficient production of other industrially important natural products used in food, pharmaceuticals, and cosmetics.
Lutein is a xanthophyll carotenoid found in egg yolk, fruits, and vegetables, known for its role in protecting our eyes from oxidative stress and reducing the risk of macular degeneration and cataracts. Currently, commercial lutein is predominantly extracted from marigold flowers; however, this approach has several drawbacks, including long cultivation times, high labor costs, and inefficient extraction yields, making it economically unfeasible for large-scale production. These challenges have driven the demand for alternative production methods.
To address these issues, KAIST researchers, including Ph.D. Candidate Hyunmin Eun, Dr. Cindy Pricilia Surya Prabowo, and Distinguished Professor Sang Yup Lee, applied systems metabolic engineering strategies to engineer C. glutamicum, a GRAS (Generally Recognized As Safe) microorganism widely used in industrial fermentation. Unlike Escherichia coli, which was previously explored for microbial lutein production, C. glutamicum lacks endotoxins, making it a safer and more viable option for food and pharmaceutical applications.
The team’s work, entitled “Gram-per-litre scale production of lutein by engineered Corynebacterium,” was published in Nature Synthesis on 04 July , 2025.
This research details the high-level production of lutein using glucose as a renewable carbon source via systems metabolic engineering. The team focused on eliminating metabolic bottlenecks that previously limited microbial lutein synthesis. By employing enzyme scaffold-based electron channeling strategies, the researchers improved metabolic flux towards lutein biosynthesis while minimizing unwanted byproducts.
<Lutein production metabolic pathway engineering>
To enhance productivity, bottleneck enzymes within the metabolic pathway were identified and optimized. It was determined that electron-requiring cytochrome P450 enzymes played a major role in limiting lutein biosynthesis. To overcome this limitation, an electron channeling strategy was implemented, where engineered cytochrome P450 enzymes and their reductase partners were spatially organized on synthetic scaffolds, allowing more efficient electron transfer and significantly increasing lutein production.
The engineered C. glutamicum strain was further optimized in fed-batch fermentation, achieving a record-breaking 1.78 g/L of lutein production within 54 hours, with a content of 19.51 mg/gDCW and a productivity of 32.88 mg/L/h—the highest lutein production performance in any host reported to date. This milestone demonstrates the feasibility of replacing plant-based lutein extraction with microbial fermentation technology.
“We can anticipate that this microbial cell factory-based mass production of lutein will be able to replace the current plant extraction-based process,” said Ph.D. Candidate Hyunmin Eun. He emphasized that the integrated metabolic engineering strategies developed in this study could be broadly applied for the efficient production of other valuable natural products used in pharmaceuticals and nutraceuticals.
<Schematic diagram of microbial-based lutein production platform>
“As maintaining good health in an aging society becomes increasingly important, we expect that the technology and strategies developed here will play pivotal roles in producing other medically and nutritionally significant natural products,” added Distinguished Professor Sang Yup Lee.
This work is supported by the Development of Next-generation Biorefinery Platform Technologies for Leading Bio-based Chemicals Industry project 2022M3J5A1056072 and the Development of Platform Technologies of Microbial Cell Factories for the Next-Generation Biorefineries project 2022M3J5A1056117 from the National Research Foundation supported by the Korean Ministry of Science and ICT.
Source:
Hyunmin Eun (1st), Cindy Pricilia Surya Prabowo (co-1st), and Sang Yup Lee (Corresponding). “Gram-per-litre scale production of lutein by engineered Corynebacterium”. Nature Synthesis (Online published)
For further information:
Sang Yup Lee, Distinguished Professor of Chemical and Biomolecular Engineering, KAIST (leesy@kaist.ac.kr, Tel: +82-42-350-3930)
2025.07.14 View 1195 -
KAIST Ushers in Era of Predicting ‘Optimal Alloys’ Using AI, Without High-Temperature Experiments
<Picture1.(From Left) Prof. Seungbum Hong, Ph.D candidate Youngwoo Choi>
Steel alloys used in automobiles and machinery parts are typically manufactured through a melting process at high temperatures. The phenomenon where the components remain unchanged during melting is called “congruent melting.” KAIST researchers have now addressed this process—traditionally only possible through high-temperature experiments—using artificial intelligence (AI). This study draws attention as it proposes a new direction for future alloy development by predicting in advance how well alloy components will mix during melting, a long-standing challenge in the field.
KAIST (President Kwang Hyung Lee) announced on the 14th of July that Professor Seungbum Hong’s research team from the Department of Materials Science and Engineering, in international collaboration with Professor Chris Wolverton’s group at Northwestern University, has developed a high-accuracy machine learning model that predicts whether alloy components will remain stable during melting. This was achieved using formation energy data derived from Density Functional Theory (DFT)* calculations.
*Density Functional Theory (DFT): A computational quantum mechanical method used to investigate the electronic structure of many-body systems, especially atoms, molecules, and solids, based on electron density.
The research team combined formation energy values obtained via DFT with experimental melting reaction data to train a machine learning model on 4,536 binary compounds.
Among the various machine learning algorithms tested, the XGBoost-based classification model demonstrated the highest accuracy in predicting whether alloys would mix well, achieving a prediction accuracy of approximately 82.5%. The team also applied the Shapley value method* to analyze the key features of the model. One major finding was that sharp changes in the slope of the formation energy curve (referred to as “convex hull sharpness”) were the most significant factor. A steep slope indicates a composition with energetically favorable (i.e., stable) formation.
*Shapley value: An explainability method in AI used to determine how much each feature contributed to a prediction.
The most notable significance of this study is that it predicts alloy melting behavior without performing high-temperature experiments. This is especially useful for materials such as high-entropy alloys or ultra-heat-resistant alloys, which are difficult to handle experimentally. The approach could also be extended to the design of complex multi-component alloy systems in the future.
Furthermore, the physical indicators identified by the AI model showed high consistency with actual experimental results on how well alloys mix and remain stable. This suggests that the model could be broadly applied to the development of various metal materials and the prediction of structural stability.
Professor Seungbum Hong of KAIST stated, “This research demonstrates how data-driven predictive materials development is possible by integrating computational methods, experimental data, and machine learning—departing from the traditional experience-based alloy design.” He added, “In the future, by incorporating state-of-the-art AI techniques such as generative models and reinforcement learning, we could enter an era where completely new alloys are designed automatically.”
<Model performance and feature importance analysis for predicting melting congruency. (a) SHAP summary plot showing the impact of individual features on model predictions. (b) Confusion matrix illustrating the model’s classification performance. (c) Receiver operating characteristic (ROC) curve with an AUC (area under the curve) score of 0.87, indicating a strong classification performance.>
Ph.D. candidate Youngwoo Choi, from the Department of Materials Science and Engineering at KAIST, participated as the first author. The study was published in the May issue of APL Machine Learning, a prestigious journal in the field of machine learning published by the American Institute of Physics, and was selected as a “Featured Article.”
※ Paper title: Machine learning-based melting congruency prediction of binary compounds using density functional theory-calculated formation energy ※ DOI: 10.1063/5.0247514
This research was supported by the Ministry of Science and ICT and the National Research Foundation of Korea.
2025.07.14 View 500 -
KAIST Presents a Breakthrough in Overcoming Drug Resistance in Cancer – Hope for Treating Intractable Diseases like Diabetes
<(From the left) Prof. Hyun Uk Kim, Ph.D candiate Hae Deok Jung, Ph.D candidate Jina Lim, Prof.Yoosik Kim from the Department of Chemical and Biomolecular Engineering>
One of the biggest obstacles in cancer treatment is drug resistance in cancer cells. Conventional efforts have focused on identifying new drug targets to eliminate these resistant cells, but such approaches can often lead to even stronger resistance. Now, researchers at KAIST have developed a computational framework to predict key metabolic genes that can re-sensitize resistant cancer cells to treatment. This technique holds promise not only for a variety of cancer therapies but also for treating metabolic diseases such as diabetes.
On the 7th of July, KAIST (President Kwang Hyung Lee) announced that a research team led by Professors Hyun Uk Kim and Yoosik Kim from the Department of Chemical and Biomolecular Engineering had developed a computational framework that predicts metabolic gene targets to re-sensitize drug-resistant breast cancer cells. This was achieved using a metabolic network model capable of simulating human metabolism.
Focusing on metabolic alterations—key characteristics in the formation of drug resistance—the researchers developed a metabolism-based approach to identify gene targets that could enhance drug responsiveness by regulating the metabolism of drug-resistant breast cancer cells.
< Computational framework that can identify metabolic gene targets to revert the metabolic state of the drug-resistant cells to that of the drug-sensitive parental cells>
The team first constructed cell-specific metabolic network models by integrating proteomic data obtained from two different types of drug-resistant MCF7 breast cancer cell lines: one resistant to doxorubicin and the other to paclitaxel. They then performed gene knockout simulations* on all of the metabolic genes and analyzed the results.
*Gene knockout simulation: A computational method to predict changes in a biological network by virtually removing specific genes.
As a result, they discovered that suppressing certain genes could make previously resistant cancer cells responsive to anticancer drugs again. Specifically, they identified GOT1 as a target in doxorubicin-resistant cells, GPI in paclitaxel-resistant cells, and SLC1A5 as a common target for both drugs.
The predictions were experimentally validated by suppressing proteins encoded by these genes, which led to the re-sensitization of the drug-resistant cancer cells.
Furthermore, consistent re-sensitization effects were also observed when the same proteins were inhibited in other types of breast cancer cells that had developed resistance to the same drugs.
Professor Yoosik Kim remarked, “Cellular metabolism plays a crucial role in various intractable diseases including infectious and degenerative conditions. This new technology, which predicts metabolic regulation switches, can serve as a foundational tool not only for treating drug-resistant breast cancer but also for a wide range of diseases that currently lack effective therapies.”
Professor Hyun Uk Kim, who led the study, emphasized, “The significance of this research lies in our ability to accurately predict key metabolic genes that can make resistant cancer cells responsive to treatment again—using only computer simulations and minimal experimental data. This framework can be widely applied to discover new therapeutic targets in various cancers and metabolic diseases.”
The study, in which Ph.D. candidates JinA Lim and Hae Deok Jung from KAIST participated as co-first authors, was published online on June 25 in Proceedings of the National Academy of Sciences (PNAS), a leading multidisciplinary journal that covers top-tier research in life sciences, physics, engineering, and social sciences.
※ Title: Genome-scale knockout simulation and clustering analysis of drug-resistant breast cancer cells reveal drug sensitization targets ※ DOI: https://doi.org/10.1073/pnas.2425384122 ※ Authors: JinA Lim (KAIST, co-first author), Hae Deok Jung (KAIST, co-first author), Han Suk Ryu (Seoul National University Hospital, corresponding author), Yoosik Kim (KAIST, corresponding author), Hyun Uk Kim (KAIST, corresponding author), and five others.
This research was supported by the Ministry of Science and ICT through the National Research Foundation of Korea, and the Electronics and Telecommunications Research Institute (ETRI).
2025.07.08 View 1208