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KAIST Skin-Conformable Micro-LED Mask Boosts Skin Rejuvenation, Brightening, and Synergistic Benefits with Polynucleotide (PN) Injections
Home beauty devices that let users care for their skin conveniently at home have grown popular recently, but conventional LED masks are limited not only by their rigid structures but also by their point-emitting LEDs, which must be positioned away from the skin to spread light over a broader area. This inherently prevents close skin contact and increases optical loss.
KAIST (President Chung-Sik Bae) announced on September 2 that a joint research team led by Professor Keon Jae Lee from the Department of Materials Science and Engineering confirmed skin-brightening and elasticity-improving effects using a face-conforming LED mask. The mask combines a flexible surface-emitting micro-LED layer, consisting of a dense micro-LED array and a light-diffusing layer for uniform illumination, with a three-dimensional elastic scaffold that conforms to facial contours.
In a 2024 study published in Advanced Materials, Professor Lee clinically demonstrated that a flexible surface-emitting micro-LED mask produced up to 340% greater improvement in deep skin elasticity than conventional LED masks.
In the present study, the 3D elastic scaffold adapted to different facial contours, increasing skin-contact area from 46.9% to 78.1% and reducing the light-source-to-skin distance to 1.8 mm, while achieving 93.83% light uniformity across eight facial measurement sites.
The researchers evaluated the mask in a split-face clinical study involving 33 participants. All of the participants received PN injections across the entire face, while the micro-LED mask was applied to only one side for eight weeks. The micro-LED-treated side showed greater improvement across all five skin-brightening indices, including skin brightness, tone uniformity, skin exfoliation, melasma count, and melasma area. Consistent with the clinical findings, human-derived skin tissue treated with micro-LEDs also showed reduced expression of the melanogenesis-related markers MITF and TYR, supporting a direct contribution of the LED treatment to the brightening effect. PN injections are primarily known for skin rejuvenation, with limited evidence of a direct skin-brightening effect when used alone.
The synergy between PN injection and the micro-LED mask was also evident in skin regeneration and post-procedure recovery. Deep skin elasticity improved by 12.8% on the LED+PN side, compared with 3.1% on the PN-only side, representing approximately 4.1 times the improvement observed with PN alone. Skin-barrier recovery was faster, while post-procedure redness was reduced to a greater extent, consistent with the known anti-inflammatory and tissue-repair effects of red-light photobiomodulation. These findings suggest that the LED mask may boost mitochondrial ATP production in the skin and activate regenerative responses that PN alone cannot fully induce.
Professor Lee said, “This study clinically demonstrates that home-use LED masks can extend beyond skin rejuvenation to skin brightening and highlights the importance of delivering light in close contact with the skin. When combined with in-clinic skin-rejuvenation injections, the mask can substantially improve skin elasticity and accelerate post-procedure recovery, creating a new clinic-to-home care platform.”
This study was conducted jointly by researchers from KAIST and AMOREPACIFIC. A related product based on the technology is scheduled to launch in Japan in Q4 2026 and enter the U.S. market in Q1 2027.
The resulting paper, titled “Clinical validation of skin brightening and rejuvenation enabled by a skin-conformable surface-emitting micro-LED mask with injection,” was published in Nano Energy
(Vol. 157, Article 112288; DOI: 10.1016/j.nanoen.2026.112288).
2026.09.02 View 463 -
KAIST Opens the Era of Industrial-Scale Microbial Foods, Proposing Growth Strategies for the Next-Generation Protein Market
The question is no longer whether microbial foods can be made. The question now is who can turn them into an industry first. KAIST researchers have comprehensively analyzed the conditions required for the microbial food industry to succeed across manufacturing, markets, and regulation, and have proposed growth strategies for the next-generation protein industry.
KAIST (President Choongsik Bae) announced on the 31st of July that a research team led by Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering, together with researchers from SilicoBio, a KAIST faculty startup, has comprehensively analyzed the conditions needed for the microbial food industry to succeed in terms of manufacturing, market entry, and regulatory readiness, and has presented an industrialization strategy and roadmap.
This study is significant in that it did not develop a new microorganism or production technology, but instead systematically analyzed the key challenges involved in connecting laboratory-based core technologies to real-world industry. In particular, by presenting an integrated perspective that encompasses manufacturing readiness, market entry strategies, and regulatory responses, the study proposes a direction for developing microbial foods beyond the next-generation protein industry into a future biomanufacturing platform. It is expected to serve as an important milestone for strengthening national biomanufacturing competitiveness and fostering the global sustainable food industry.
The researchers analyzed that competition in the microbial food industry is shifting from productivity at the laboratory level to manufacturing readiness. They identified stable raw material supply and quality control, control and safety assurance of non-model microorganisms, reduction of downstream processing costs, and regulatory compliance for byproduct recycling as key factors that will determine the pace of commercialization. Manufacturing Readiness refers to the level at which a laboratory technology can be reliably produced at industrial scale. Non-model microorganisms are microorganisms with high industrial potential but insufficient accumulated research infrastructure. Downstream processing refers to the processes of separating, purifying, concentrating, and drying target components after fermentation.
The researchers particularly emphasized that future competitiveness will depend less on the excellence of any single technology and more on the ability to build integrated manufacturing platforms. An Integrated Manufacturing Platform refers to a production system that operates the entire process as one connected framework, from strain development and large-scale fermentation to purification, quality control, and product formulation. Even for the same microbial food product, the choice of raw material can affect pretreatment costs and quality variability, while the choice of strain and fermentation process can greatly influence production cost, energy use, and product quality. The researchers therefore concluded that future industrial competitiveness will depend on how quickly companies can build manufacturing platforms that optimize these factors in an integrated way.
On the market side, the researchers also identified the conditions needed for the microbial food industry to succeed. Based on consumer surveys and industry cases, they found that microbial foods cannot spread simply by emphasizing environmental sustainability. Consumers place importance on taste, texture, familiarity, and safety, while food manufacturers value functionality that can be applied to actual products. Companies and investors, meanwhile, consider the predictability of regulatory approval procedures and speed of market entry to be especially important. In other words, the microbial food market has entered an industrial stage where not only technology, but also product development capability and regulatory readiness are evaluated together.
The researchers also argued that microbial foods should not be viewed merely as an alternative protein industry. They suggested that microbial foods have the potential to develop into a core platform for precision fermentation-based functional food ingredients, high-value biomaterials, and circular biomanufacturing. Precision Fermentation is a technology that uses microorganisms to selectively produce specific proteins or functional substances. Circular Biomanufacturing refers to a sustainable manufacturing system that uses byproducts and renewable resources to produce new bio-based products. This means that microbial foods could become not only a future food source, but also a new production system connecting the global food, materials, and biomanufacturing industries.
The industrialization strategy proposed in this study is also closely aligned with the business direction of SilicoBio, which participated in the joint research. Based on the manufacturing readiness strategy presented in the study, SilicoBio is working to build a platform that connects microbial proteins and functional food ingredients to industrial-scale fermentation, scale-up, and product development. Scale-up refers to the process of expanding production from laboratory scale to industrial scale.
Distinguished Professor Sang Yup Lee of KAIST said, “As global competition surrounding synthetic biology and biomanufacturing intensifies, microbial foods are growing into a key industry that will shape national biomanufacturing competitiveness beyond future food.” He added, “Going forward, competitiveness will be determined by how quickly we can build an industrialization ecosystem that connects core technologies to real production and markets.”
A SilicoBio representative said, “Our goal is to connect the industrialization strategy proposed in this study to actual production and commercialization,” adding, “We will build a platform capable of stably producing microbial-based next-generation foods and functional biomaterials.”
This study, with Seok Yeong Jung, a doctoral student in the Department of Chemical and Biomolecular Engineering, as first author and researchers from SilicoBio participating as co-authors, was published on July 17 in the international journal One Earth (Impact Factor 15.3, JCR top 2.07%).
Paper title: Microbial foods as scalable platforms toward a circular protein economy for sustainable nutrition
DOI: https://doi.org/10.1016/j.oneear.2026.101772
Authors: Sang Yup Lee (KAIST, corresponding author), Seok Yeong Jung (KAIST, first author), Sol Choi (SilicoBio, second author), Jun-Woo Kim (SilicoBio and Inha University, third author), and two others
SilicoBio is a KAIST faculty startup founded in June 2025 by Distinguished Professor Sang Yup Lee, a world-renowned scholar in synthetic biology. The company focuses on connecting laboratory-level achievements in systems metabolic engineering to real industrialization. By combining KAIST’s core technologies with the industrialization experience of personnel from CJ BIO, SilicoBio has built a team capable of reviewing not only strain design, but also industrial-scale fermentation and scale-up, material purification and product development, pilot production, and process validation. Based on this foundation, SilicoBio is pursuing a phased commercialization strategy, starting with next-generation protein products and expanding into functional ingredients and eventually new drug and novel material candidates.
This research was supported by the “Development of Next-Generation Biorefinery Core Technologies to Lead the Biochemical Industry” project under the Petroleum-Alternative Eco-Friendly Chemical Technology Development Program funded by the Ministry of Science and ICT, and by the “Advancement of a Synthetic Biology-Based Industrial Cell Factory Platform and Commercialization of High-Value Functional Biomaterials” project under the Deep Science Startup Activation Support Program funded by the Commercialization Promotion Agency for R&D Outcome.
2026.08.31 View 473 -
Next-Generation Biological Foundation Model, K-Fold
“Design a drug candidate that binds effectively to this protein.”
In response to such a request, AI predicts the protein’s three-dimensional structure, analyzes which compounds are most likely to bind to it, and designs promising drug candidates. KAIST researchers have developed K-Fold, the world's fastest Bio-AI model for protein structure prediction, which also supports drug candidate design.
KAIST (President Choongsik Bae) announced on August 28 that it had formed “Team KAIST” after being selected as the lead institution for the Ministry of Science and ICT’s “AI Specialized Foundation Model Project” and unveiled K-Fold, a next-generation Bio-AI model developed by Team KAIST.
Team KAIST is led by Professor Woo Youn Kim from the Department of Chemistry. His research group, together with Professors Sung Ju Hwang and Sungsoo Ahn's groups at the Kim Jaechul Graduate School of AI, developed the AI model. Professors Byung-Ha Oh, Ho Min Kim, and Gyuri Lee from the Department of Biological Sciences oversaw protein data construction and validation. HITS, a KAIST faculty startup, integrated K-Fold into HyperLab, its web-based AI research platform, enabling researchers to use the model in real-world research workflows. In addition, the Korea Pharmaceutical and Bio-Pharma Manufacturers Association (KPBMA) and the Korea Biotechnology Industry Organization (KoreaBIO) will lead efforts to raise awareness of K-Fold’s achievements and promote its use across the industry.
K-Fold’s defining capability is its ability to predict the binding between proteins and drug candidates—a critical step in drug discovery.
Drug development begins with determining the structure of a disease-related protein and identifying, among numerous compounds, those most likely to bind to the protein and produce the desired effect. K-Fold not only predicts a protein’s three-dimensional structure, but also calculates where and how a drug candidate is likely to bind, helping researchers identify promising candidates more quickly.
K-Fold goes beyond predicting the structure of a single protein. It can also predict the structures formed when different biomolecules interact, including protein–protein and protein–drug candidate complexes, as well as complexes involving DNA and RNA.
In the project’s stage evaluation conducted in March, its accuracy in predicting molecular complex structures was assessed as approaching that of AlphaFold3, developed by Google DeepMind. In an in-house performance evaluation conducted by the research team in August, K-Fold also outperformed existing global models in selected evaluation categories.
K-Fold demonstrated particularly strong performance in predicting how drug candidates bind to and act on key therapeutic targets, including G protein-coupled receptors (GPCRs) and kinases, which are major drug targets for cancer and other diseases. It also performed strongly in targeted protein degradation (TPD), an emerging drug discovery approach designed to directly eliminate disease-causing proteins.
K-Fold also significantly increased the speed of structure prediction. Conventional protein structure prediction models often require a complex preprocessing step that searches for and compares large amounts of data on similar proteins before calculating a structure. K-Fold applies a new approach that does not depend on this process, eliminating the need for preprocessing calculations and increasing structure prediction speeds by up to 25 times compared with existing models.
This means that researchers can evaluate more drug candidates within the same amount of time. By reducing the time and computing resources required for structure prediction, K-Fold can help rapidly identify the most promising compounds from a vast pool of candidates and narrow the selection for experimental validation.
“National competitiveness in the AI era depends on sovereign AI capabilities, which is the crucial ability to develop and deploy core technologies independently,” said KAIST President Choongsik Bae. “K-Fold is significant because it combines homegrown AI technology with biotechnology to challenge the world’s leading technologies and translates that capability into a service applicable to real-world drug discovery. KAIST will continue to strengthen Korea’s technological sovereignty in AI and its future competitiveness by advancing the convergence of foundational AI technologies with science and technology.”
The research team went beyond developing K-Fold as a standalone model, turning it into an AI research service that researchers can use through a conversational web-based interface.
K-Fold has been integrated into HyperLab, a multi-agent platform developed by HITS, a KAIST faculty startup. This allows researchers to use the model without having to build their own high-performance computing infrastructure or operate complex AI software.
For example, if a researcher asks the AI to “design an antibody that binds strongly to this protein,” it provides step-by-step support for predicting the protein’s structure, designing candidates with a high likelihood of binding, and computationally evaluating the results. Researchers can also ask it to “find a suitable peptide candidate for this cancer target protein.” In practical terms, instead of moving between multiple software tools to calculate structures and analyze results, researchers can simply state their research objective and have the AI carry out the necessary analyses and design tasks in sequence.
To support these capabilities, HyperLab incorporates approximately 120 computational tools and 160 specialized functions for structure prediction, drug design, and the analysis of life science data, including genomic and proteomic data. It also connects more than 100 specialized databases with a large-scale knowledge graph, enabling the platform to retrieve relevant scientific information and apply it to its analyses.
HyperLab aims to serve as an AI Co-Scientist that assists researchers throughout the research process by supporting the full workflow, from understanding a research question and selecting the appropriate tools to predicting structures, analyzing results, and iteratively improving designs.
Bio AI is emerging as a critical technology capable of reducing the time and cost required for drug discovery, driving intense competition among global technology companies and major research institutions in the United States, the United Kingdom, and China. The development of K-Fold is significant because it lays the foundation for sovereign bio AI by securing core bio AI technology domestically, rather than relying solely on overseas models, and making it available for real-world research.
The achievement was first presented at the 2026 Annual Meeting of the Korean Federation of Biomolecular Science, held on June 23, where Professor Woo Youn Kim from the KAIST Department of Chemistry delivered a keynote lecture titled “Generative Drug Design Powered by Agentic AI.”
“K-Fold was developed not to follow existing models, but to overcome the limitations of conventional approaches through a new AI architecture,” explained Professor Kim. “We will develop it into an AI-for-Science platform that makes world-class bio AI technology accessible to researchers everywhere.”
The Team KAIST consortium plans to release K-Fold free of charge. HyperLab will provide beta access to researchers in Korea and abroad, and gradually expand its commercial services by the end of this year.
Meanwhile, industry training on K-Fold is gaining momentum. An online session hosted by KoreaBIO on August 27 attracted 85 participants, while 118 have registered for KPBMA’s hybrid session on September 1. Designed primarily for researchers and practitioners at pharmaceutical and biotech companies, the training covers how to use K-Fold and presents case studies of its application to drug design. The initiative is intended to accelerate the adoption of sovereign bio-AI across the industry.
Related website: [HyperLab Co-Scientist] https://hyperlab.ai/features-co-scientist
K-Fold–HyperLab 3.0 Introduction Video: https://drive.google.com/file/d/1MxhZr-C3pQdIVn1EP44hQICj8G70is3_/view
This research was supported by the Ministry of Science and ICT (MSIT, PJT-25-100009)
2026.08.28 View 1394 -
KAIST Develops Core Technology to Reverse Biological Changes Once Thought Irreversible, Opening New Possibilities for Aging and Cancer Research
Once a cell has locked into an abnormal state — the way cancer cells do — can it ever be restored back to normal? A KAIST research team has identified the ‘molecular lock’ that keeps cells trapped in an altered state, opening a new path toward releasing that lock and reversing a cell’s fate.
KAIST (President Choongsik Bae) announced on the 21st of August that a research team led by Professor Kwang-Hyun Cho of the Department of Bio and Brain Engineering has, for the first time, identified the causal circuits responsible for irreversibility in intracellular molecular networks and developed a fundamental control technology called ROOT that can regulate these circuits and restore biological states to their original condition.
Cells in the human body change their state in response to external stimuli. In many cases, however, these state changes are irreversible, in the sense that cells do not return to their original state even after the stimulus disappears.
Irreversibility is essential for maintaining normal biological processes, such as a cell differentiating into one with a specific function. At the same time, it can also drive disease progression — for example, in epithelial–mesenchymal transition, which gives cancer cells the ability to migrate into and invade surrounding tissue.
Complicating matters, the circuits that maintain these state changes inside a cell are highly intricate: more than a thousand positive feedback loops are woven throughout the network, in which one molecule activates a series of other molecules that in turn reactivate the original molecule. This is similar to the feedback screech produced when a microphone is placed next to a speaker, where a sound repeatedly amplifies itself. Even a change that starts with an external stimulus can persist after the stimulus is gone, simply because the cell’s own molecules keep reinforcing one another. Until now, it has been extremely difficult to determine which of these countless circuits is actually responsible for locking a cell into an irreversible state.
To solve this problem, the team developed ROOT technology, short for Revelation Of the Original circuit of irreversible Transition, which works by representing intracellular regulatory processes as computational logic models and analyzing them through systems biology techniques. Using ROOT, the research team successfully simulated the process in which cells maintain a signal even after an external stimuli is removed, allowing them to identify a set of core circuits that cause irreversibility, which they defined as the “irreversibility kernel.”
Going beyond identifying the cause, the team also proposed two groundbreaking control strategies.
The first, “resetting control,” restores a cell to its state before the change while leaving the cell’s underlying irreversible property intact — comparable to leaving the lock itself in place, but opening the locked door and returning to the starting point.
The second, “reversing control,” removes the source of irreversibility itself, allowing a cell to move freely between different states — comparable to disabling the mechanism that automatically locks a door each time it closes, so that afterward the door can be opened and closed again.
The team applied the new technique to various biological models, including B-cell differentiation, epithelial–mesenchymal transition in lung cancer, and enterocyte and beta-cell differentiation models based on single-cell transcriptome data, in which the ROOT method accurately identified causal circuits that matched known cell-fate determinants. The team also proposed more effective resetting control strategies, demonstrating that the method can be broadly applied even to models built from real experimental data.
Rather than simply removing cells that have become fixed in an abnormal state, as in cancer or aging, the technology is expected to help identify and control the core circuits that keep cells trapped in that state, enabling new treatment strategies that restore cells to a normal condition.
Professor Kwang-Hyun Cho said, “The core achievement of this study is identifying the causal circuits behind cells that, once changed, do not return to their original state, and developing a technology to control these circuits and restore cells to their previous condition.” He added, “We expect this technology to be used in developing new treatment strategies that restore abnormally fixed cell states — such as those seen in cancer and aging — back to normal.”
This study was co-led by Dr. Jongwan Kim and Dr. Seong-Hoon Jang of KAIST’s Department of Bio and Brain Engineering as co-first authors, with participation from Dr. Jonghoon Lee and Ph.D. student Corbin Hopper. The research was published on August 13 in Proceedings of the National Academy of Sciences of the United States of America (PNAS), one of the world’s leading scientific journals.
Paper title: The structural origin of irreversible transitions in biological networks,
DOI: https://doi.org/10.1073/pnas.2600800123
This research was supported by the Mid-Career Researcher Program and the Basic Research Laboratory Program of the National Research Foundation of Korea, funded by the Ministry of Science and ICT.
2026.08.21 View 1376 -
KAIST Gives Antibodies “Eyes” to Detect Cancer, Targeting Cancer Mutations Inside Cells
Antibodies are like “guided missiles” that find and attack cancer cells, but cancer-causing mutations inside cells have remained a “blind spot” for treatment because antibodies cannot reach them. KAIST researchers have now succeeded in precisely targeting even intracellular cancer mutations using a newly designed antibody created through computational methods. This achievement is expected to open a new path toward next-generation precision therapies for difficult-to-treat cancers, going beyond the limitations of conventional antibody treatments.
KAIST (President Choongsik Bae) announced on the 24th of July that a research team led by Professor Byung-Ha Oh from the Department of Biological Sciences, together with researchers from Therazyne, a KAIST faculty startup specializing in protein design and headed by Professor Oh, has developed an antibody that selectively recognizes only cancer cells carrying KRAS(G12D), a representative cancer-driving mutation. By combining computational antibody design with experimental validation, the team designed a new antibody that would have been difficult to develop through conventional approaches and is now verifying its efficacy in animal disease models.
KRAS(G12D) is a mutated form of the KRAS protein, which regulates cell growth and proliferation. It is one of the most common cancer-driving mutations found in pancreatic, colorectal, and lung cancers. However, because the KRAS protein exists inside cells, it has long been considered an “undruggable target” that is difficult to directly target with conventional antibody therapeutics.
The research team focused on the natural process by which cells break down aged or damaged proteins into small fragments. The KRAS(G12D) protein inside cells is also processed in this way into small protein fragments, known as neoantigens, which serve as clues that allow immune cells to distinguish cancer cells. Some of these fragments are then transported to the cell surface and presented to immune cells. By combining computational protein design with experimental screening, the team developed a TCR-like antibody that precisely recognizes only this cancer-mutation-derived fragment.
TCR, or T cell receptor, acts as a “sensor” that allows T cells, the body’s immune cells, to read protein fragments displayed on the surface of cells and identify cancer cells or virus-infected cells. The TCR-like antibody developed in this study works on a similar principle, effectively giving an antibody the “eyes” of a T cell. It was designed to selectively recognize traces of intracellular cancer mutations that conventional antibodies cannot easily access.
Experimental results confirmed that the antibody developed by the team selectively recognizes only cancer cells carrying the KRAS(G12D) mutation, while showing little to no reaction with normal cells or other proteins. When applied to immunotherapy, it was also shown to effectively eliminate only cancer cells carrying the mutation. This finding suggests the possibility of expanding antibody therapy to intracellular cancer-driving proteins that conventional antibody treatments have been unable to target. It is also expected to serve as a platform technology for developing next-generation precision antibody therapies targeting not only KRAS but also a wide range of cancer mutations.
Professor Byung-Ha Oh said, “The antibody developed in this study can selectively identify only cancer cells carrying the KRAS(G12D) mutation, demonstrating the potential for precision antibody therapeutics that minimize damage to normal cells.” He added, “The computational antibody design technology developed in this research is expected to be widely applicable to the development of next-generation antibody therapeutics targeting KRAS as well as various other cancer mutations.”
Both the first author and corresponding authors of this study are KAIST-affiliated researchers. SangPhil Ahn, a researcher at Therazyne, participated as the first author, while Professor Byung-Ha Oh and Bo-Seong Jeong, Head of Research at Therazyne, jointly led the study as co-corresponding authors. The research was published online on June 3 in Molecular Therapy, a leading international journal in the field of gene and cell therapy.
Paper title: Discovery of TCR-like antibodies to the KRAS G12D neoantigen via in silico-in vitro workflow DOI: https://doi.org/10.1016/j.ymthe.2026.05.032
This research was conducted in collaboration with Therazyne and the New Drug Development Center of the Osong Biomedical Innovation Foundation, and was supported by the Ministry of Science and ICT’s Industry-Academia-Research Linked New Drug Development Program and the National Research Foundation of Korea’s Bio & Medical Technology Development Program.
2026.07.24 View 1750 -
A Single Blood Sample May Improve the Prediction of Colorectal Cancer Recurrence and Progression, KAIST Study Finds
A single preoperative blood sample may help improve the prediction of recurrence or metastasis in patients with colorectal cancer. A joint research team from KAIST, Gangnam Severance Hospital, and Asan Medical Center has shown that, as colorectal cancer advances, the network of relationships among circulating amino acids (a kind of metabolic map) undergoes systematic remodeling. Building on this finding, the researchers developed an analytical method that showed higher predictive performance than a CEA-only model and models based solely on individual amino acid levels.
KAIST (President Choongsik Bae) announced on July 22 that a joint research team led by Professor Ji Min Lee from the Graduate School of Medical Science and Engineering and Professor Hyunwoo Kim from the Department of Chemistry, in collaboration with researchers at Gangnam Severance Hospital and Asan Medical Center, has developed a new framework for analyzing networks of circulating amino acids, which reflect the body’s metabolic state. Using this framework, the team showed that the circulating amino acid network undergoes stage-dependent remodeling that reflects systemic metabolic reprogramming. The team then used these network-derived features to develop a new analytical strategy for predicting recurrence or metastasis.
Cancer cells require large amounts of nutrients to grow and proliferate. Amino acids are not only the building blocks of proteins but also essential for energy production and DNA synthesis, making them critical to cancer cell survival and growth. Colorectal cancer, in particular, is characterized by pronounced changes in amino acid metabolism.
These changes are not confined to tumor tissue; they also appear in the bloodstream. As a result, blood amino acids have drawn attention as an important metabolic biomarker reflecting the body's overall metabolic state. Until now, however, research has focused mainly on the concentrations of individual amino acids, leaving the question of how amino acids are interconnected and change together largely unexplored.
The team used fluorine-19 nuclear magnetic resonance (¹⁹F NMR) spectroscopy to simultaneously quantify 18 circulating amino acids in a small serum sample. By analyzing not only the relative abundance of each amino acid but also the relationships among them as a network, the researchers showed that the circulating amino acid network is progressively remodeled as colorectal cancer advances.
The researchers interpreted this remodeling as evidence of systemic metabolic reprogramming (broad changes in metabolism associated with tumor progression).
As colorectal cancer progressed, the proportion of branched-chain amino acids (BCAAs) such as valine and leucine, which play key roles in muscle and energy metabolism, decreased, while the proportion of glycine and serine, which cancer cells need to synthesize DNA and proliferate rapidly, increased. This shift suggests that systemic amino acid utilization changes with advancing disease.
Glycine proved particularly notable. Although glycine is actively used by rapidly proliferating cancer cells, its relative abundance in the blood increased rather than decreased. The team also observed the emergence of a glycine-centered interaction pattern, providing further evidence of systemic metabolic remodeling during colorectal cancer progression.
The team then applied the pairwise amino acid interaction features into machine-learning models designed to identify patients with recurrence or metastasis.
In nested cross-validation, the correlation-based model showed higher predictive performance than a CEA-only model, while the combined model incorporating CEA, individual amino acid levels, and interaction-derived features achieved the highest overall performance. It also outperformed a model based solely on individual amino acid levels.
The findings suggest that examining how amino acids interact and change together, rather than considering their levels alone, provides a more informative picture of cancer progression. The study is the first to show that the interaction network among circulating amino acids could serve as a blood-based metabolic biomarker.
"We hope this will lead to new precision medicine technologies that can predict recurrence risk more accurately using a blood sample alone and help establish personalized treatment strategies," said Professor Ji Min Lee.
The research began with an interdisciplinary idea proposed through KAIST’s Master’s and PhD Venture Research Program. Graduate students in medical science and chemistry jointly conceived an interdisciplinary approach to studying cancer progression through networks of circulating amino acids. The proposal was selected for support and ultimately led to publication in the internationally renowned journal Advanced Science.
"This research embodies the spirit of KAIST by showing how students’ creative ideas and interdisciplinary collaboration can open new possibilities,” said KAIST President Choongsik Bae. He added that KAIST will continue to foster an environment in which students and researchers can freely pursue challenges across disciplinary boundaries and support creative interdisciplinary research that produces innovative technologies contributing to public health and quality of life.
The study's co-first authors are Ji-Yeon Lee, a student in the integrated master’s and doctoral program at the Graduate School of Medical Science and Engineering, and Dr. Jumi Kim, a postdoctoral researcher in the Department of Chemistry. Professors Ji Min Lee and Hyunwoo Kim of KAIST and Professor Eun Jung Park, affiliated with Gangnam Severance Hospital and Asan Medical Center, served as co-corresponding authors. The findings were published online in Advanced Science, which has a Journal Impact Factor of 14.1, on June 9, 2026.
Paper title: Circulating Amino Acid Network Remodeling Reveals Systemic Metabolic Reprogramming Predictive of Colorectal Cancer Recurrence and Metastasis
DOI: 10.1002/advs.76044
This research was supported by the Samsung Research Funding & Incubation Center of Samsung Electronics, the National Research Foundation of Korea, a Faculty Research Grant from the Department of Surgery at Asan Medical Center, and grants from the Asan Institute for Life Sciences, among others.
2026.07.22 View 1429 -
KAIST Uncovers the “Hidden Key” to Immunotherapy for Intractable Brain Tumors
Researchers have uncovered a clue to why immune checkpoint inhibitors—cancer therapies that release the immune “brakes” exploited by tumors to evade attack—show limited efficacy in some brain tumors. A KAIST research team found that B cell and antibody responses initiated in tumor-draining lymph nodes, rather than T cells alone, are critical to the antitumor effects of anti-CTLA-4 therapy, opening a new avenue for treating intractable brain tumors.
KAIST (President Choongsik Bae) announced on the 19th of July that a research team led by Professor Heung Kyu Lee from the Department of Biological Sciences has identified a previously unrecognized immune mechanism through which anti-CTLA-4, a type of immune checkpoint inhibitor, promotes B-cell responses in tumor-draining lymph nodes, thereby helping the immune system attack brain tumors.
Glioblastoma is one of the most aggressive malignant brain tumors, with frequent recurrence and a poor prognosis even after surgery and radiation therapy. Immune checkpoint inhibitors, which restore the ability of immune cells to attack cancer cells, have produced substantial therapeutic benefits in various cancers. However, their effectiveness in glioblastoma has remained limited because of the highly immunosuppressive environment surrounding the tumor.
Researchers have traditionally regarded T cells—immune cells that can directly attack cancer cells—as the primary target of immune checkpoint inhibitors. B cells, meanwhile, are well known for producing antibodies following infection or vaccination, but their role in brain tumor immunotherapy has remained largely unexplored. The research team therefore investigated whether anti-CTLA-4 could influence B-cell responses as well as T-cell responses.
The findings challenged the prevailing T-cell-centered view. In mouse glioma models, anti-CTLA-4 treatment reduced tumor burden and significantly prolonged survival. However, these therapeutic effects were largely lost in mice lacking B cells, demonstrating that B cells are required for the efficacy of anti-CTLA-4 treatment in these models.
The team also identified where B cells played their key role. Rather than being prominent in the brain, where the tumor cells were located, the response increased markedly in the Deep Cervical Lymph Nodes, which are located deep in the neck and receive lymphatic drainage from the brain. In particular, germinal center B cells and T follicular helper cells, both of which are important for antibody formation, increased together in these lymph nodes. This was accompanied by an increase in immunoglobulin G, or IgG, responses. IgG is a major class of antibody that can recognize cancer cells as targets and help immune cells eliminate them.
The resulting IgG antibodies bound to the surface of glioma cells, helping macrophages—immune cells that engulf foreign substances and cancer cells—remove the tumor cells more effectively.
The research team also examined this process directly in vivo. Using a specialized dual-reporter glioma model expressing the red fluorescent protein mCherry and the green fluorescent protein EGFP, the researchers successfully visualized tumor-infiltrating phagocytes actively engulfing glioma cells following anti-CTLA-4 treatment.
The study provides the first functional evidence that B-cell immune responses—previously known mainly for their roles in infection and vaccination—can be a key factor determining the effectiveness of immunotherapy for hard-to-treat brain tumors. It also expands the conventional T-cell-centered framework of cancer immunotherapy by showing that treatment efficacy can be strongly shaped by immune responses originating not only within the tumor, but also in tumor-draining lymph nodes outside it.
Yumin Kim, a postdoctoral researcher in the KAIST Department of Biological Sciences, served as the first author of the study, with Professor Heung Kyu Lee serving as the corresponding author. Professor Ji Eun Oh from the KAIST Graduate School of Medical Science and Engineering also contributed to the research. The findings were published on July 10 in Science Immunology.
Paper title: The efficacy of immunotherapy in glioma requires distal B-cell responses in tumor-draining lymph nodes
DOI: 10.1126/sciimmunol.adz2494
Authors: Yumin Kim, In Kang, Byeong Hoon Kang, Won Hyung Park, Chae Won Kim, Hyun-Jin Kim, Jeongwoo La, Myoung Seung Kwon, Sang Hee Park, Seo Hyeon Im, Hyeon Cheol Kim, Keun Bon Ku, Minji Kim, and Ji Eun Oh from KAIST, with Heung Kyu Lee from KAIST as the corresponding author.
This research was supported by National Research Foundation of Korea grants RS-2023-NR077244 (to H.K.L.), RS-2024-00439735 (to H.K.L.), RS-2024-00411928 (to Y.K.), RS-2025-00517107 (to J.E.O.), and RS-2026-25509011 (to H.K.L.). This study was also supported by Samsung Science and Technology Foundation grants SSTF-BA1902-05 (to H.K.L.) and SSTF-BA2201-11 (to J.E.O.).
2026.07.20 View 2032 -
KAIST Brings the Era of Microbial Cell Factories One Step Closer
The era of "biomanufacturing", in which microbes, not petroleum, produce chemical products, is one step closer. A KAIST research team has analyzed the key challenges limiting the commercialization of biomanufacturing and proposed an AI-driven strategy for industrialization.
KAIST (President Choongsik Bae) announced on the 14th of July that a research team led by Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering has comprehensively analyzed the key bottlenecks to commercializing biomanufacturing and proposed an industrialization strategy and a roadmap for future growth to address them.
Most chemical products today — including plastics, textiles, and pharmaceutical raw materials — are produced from petroleum. But as concerns over carbon emissions and environmental pollution grow, biomanufacturing, which uses microbes to produce chemicals, is drawing attention as a next-generation manufacturing technology. Still, scaling up lab-developed technologies into economically viable mass production at actual factories remains a major challenge.
Systems metabolic engineering, a core technology in biomanufacturing, designs and optimizes microbial metabolic pathways to build "microbial cell factories" that produce desired chemicals. But technologies that show high productivity in the lab often perform worse once moved to industrial settings — productivity drops, production costs rise, and many fail to achieve price competitiveness, ultimately failing to commercialize.
The research team analyzed succinic acid, a bio-based chemical feedstock, and polyhydroxyalkanoate (PHA), a biodegradable plastic, as representative cases illustrating this "gap between the lab and industry," often called the "valley of death."
Succinic acid is a key raw material for producing eco-friendly plastics and various chemical materials. The team explained that for succinic acid to compete with existing petrochemical products, competitiveness depends not just on production volume, but also on raw material and separation/purification costs, the fermentation process, and market size — all of which must be weighed together. The team also suggested that a phased strategy — entering high-value markets such as pharmaceuticals, cosmetics, and food ingredients first — could be a realistic solution.
PHA is a biodegradable plastic that microbes accumulate inside their cells, an eco-friendly material that breaks down naturally in the environment after use. But PHA is currently less price-competitive than conventional plastics due to high production and recovery costs, and its intrinsic material properties pose a separate barrier: the archetypal polymer P(3HB) is highly crystalline, becomes brittle with age, and has a narrow window between its melting and decomposition temperatures, meaning PHAs are generally not suitable as direct "drop-in" replacements.The team found that a phased approach is needed — simplifying the production process and first applying it to high-value fields such as medical applications and food packaging before expanding into general-purpose markets.
The team predicted that artificial intelligence will become a key to industrializing biomanufacturing going forward. AI can optimize the entire biomanufacturing process — from enzyme and microbial design to digital twins that virtually simulate production processes, and technologies that simultaneously analyze economic feasibility and environmental impact. The team explained that this can shorten development timelines, reduce production costs, and increase the likelihood of successful commercialization.
The team also proposed that techno-economic analysis (TEA) and life cycle assessment (LCA) should be applied as design criteria from the earliest stages of research, rather than as evaluations conducted only after research is complete. The team further emphasized that supply chain resilience — accounting for raw material availability and shifts in the international landscape — should be considered a new design standard for biomanufacturing.
This study is significant not for developing a new production technology, but for comprehensively analyzing the conditions for successful biomanufacturing industrialization and presenting an industrialization roadmap spanning the entire cycle — from securing raw materials to microbial design, fermentation, separation and purification, and market entry. The team expects the study to accelerate the commercialization of the bio-based chemical industry and, over the long term, contribute to shifting the petroleum-centered chemical industry toward an eco-friendly bioeconomy.
The paper, with Ji Yeon Kim and Hye Eun Yu as co-first authors, both Ph.D. candidates in KAIST's Department of Chemical and Biomolecular Engineering, was published online on May 30 in the international journal Nature Communications.
※ Paper title: Beyond petrochemicals: challenges and opportunities in industrial-scale biomanufacturing
※ DOI: 10.1038/s41467-026-73835-1
※ Authors: Ji Yeon Kim (KAIST, co-first author), Hye Eun Yu (KAIST, co-first author), Min Ho Kim (KAIST), Sang Yup Lee (KAIST, corresponding author)
This research was supported by the National Research Foundation of Korea, funded by the Ministry of Science and ICT, through the “Development of Platform Technologies of Microbial Cell Factories for Next-Generation Biorefineries” project (Project No. 2022M3J5A1056117) and the “Development of Advanced Synthetic Biology Source Technologies for Leading the Biomanufacturing Industry” project (Project No. RS-2024-00399424).
2026.07.14 View 1387 -
KAIST Enables DNA Synthesis Using Only Temperature Instead of Chemical Reagents
"Complex chemical processes are essential for making DNA." This long-held assumption in the field of biotechnology has been overturned by a Korean research team. A KAIST research team has developed the world's first foundational technology that enables the synthesis of desired DNA using only temperature. Using this technology, the team also demonstrated a "DNA temperature black box" that records temperature changes during shipping without electricity.
KAIST announced on the 7th of July that a research team led by Professor Yeongjae Choi of the Graduate School of Engineering Biology, in collaboration with ATG Lifetech Inc. (CEO Taehoon Ryu) and a research team led by Professor Hansol Choi from the Department of Life Science at Ewha Womans University, has developed this platform technology that synthesizes desired DNA sequences by controlling only temperature.
DNA is the "blueprint" that contains the genetic information of humans and all other living organisms. Scientists use custom-made DNA in various biotechnology applications, such as diagnosing diseases, developing new drugs, and creating microorganisms with new functions. Until now, however, each time one of the four bases that make up DNA—A, T, G, and C—was connected, chemical reagents had to be added and washed out repeatedly. As a result, costly automated DNA synthesis equipment and specialized research facilities were essential.
To overcome these limitations, the research team developed "hairpin DNA that reacts only at specific temperatures." This hairpin DNA is a special DNA structure that remains folded like a hairpin and unfolds only at a certain temperature. The team placed multiple types of hairpin DNA that operate at different temperatures into a single test tube and succeeded in synthesizing desired DNA step by step by changing only the temperature in the sequence.
This opens the way for synthesizing DNA with only a general temperature control device, without the need for complex reagent replacement or large-scale equipment.
As the technology advances, it is expected to greatly reduce the cost and time required to make DNA, lowering the entry barriers not only for synthetic biology and genetic research, but also for various bioindustries such as drug development and precision medicine.
To demonstrate the practical applicability of the technology, the research team also implemented a power-free "DNA temperature black box." This device is normally stored in a freeze-dried state and begins operating when a single drop of water is added just before use. It then automatically records—directly into a DNA sequence—when, how long, and in what order the temperature changes during shipping. In addition, when exposed to temperatures above a certain level, the device changes color, allowing abnormalities to be checked visually on the spot. It is expected to be used for the quality control of products for which cold-chain distribution is important, such as vaccines, biopharmaceuticals, cell therapies, and fresh foods.
KAIST researcher Jangho Choi and GIST doctoral student Jinho Kim participated in this research as co-first authors, and the research results were published in the international journal Nature Communications on July 2.
※ Paper title: Programmable one-pot polymerase-mediated DNA synthesis via temperature control
※ DOI: https://doi.org/10.1038/s41467-026-74890-4
※ Related Video: https://drive.google.com/file/d/1bUtzC83qIm1k-hNFKTb09yFPhfsD4iU-/view?usp=drive_lin
※ Authors: Jangho Choi (KAIST, co-first author), Jinho Kim (GIST, co-first author), Hansol Choi (Ewha Womans University, corresponding author), Yeongjae Choi (KAIST, corresponding author)
This research was supported by the Ministry of Science and ICT through the Future Promising Convergence Technology Pioneer Program, the Biofoundry-Based Technology Development Program, the Young Researcher Program, and the Global Basic Research Laboratory Program.
2026.07.07 View 2468 -
Official Opening of the KAIST-FORMOSA Bio R&D Center
A new initiative is officially underway to build a next-generation medical innovation platform. By combining Artificial Intelligence (AI) and advanced biotechnology, this project aims to overcome the "Death Valley" of the pre-clinical stage—a long-standing hurdle in drug discovery—and accurately predict human responses to replace animal testing.
KAIST announced on June 17 that it held the opening ceremony for the "KAIST-FORMOSA BIO R&D CENTER" at the KAIST Meta-Convergence Building on June 16, jointly with the world-renowned Taiwanese company Formosa Group, and has officially commenced research operations.
This research center is a follow-up project to the KAIST-Formosa Biomedical Cooperation Agreement signed last year and will operate centered around the "The FORM-K" project. Formosa Group will provide approximately 17 billion KRW in research funding over the next five years. Based on this financial backing, both institutions will develop and commercialize an organoid-based, next-generation animal alternative testing platform known as NAMs (New Approach Methodologies) on a global scale. NAMs are drawing significant attention as next-generation drug discovery evaluation technologies that utilize human cells, tissues, and artificial intelligence to replace animal experiments.
The opening ceremony was attended by Formosa Group Chairman Ruey-Yu Wang, key executives, and faculty members from Chang Gung University and Chang Gung Memorial Hospital, who celebrated the launch of the research center and discussed plans to expand future cooperation. Under the shared goal of improving human health, both institutions further solidified their cooperative framework in the advanced biomedical field.
The core objective of the research center is to bridge the "Death Valley" gap that occurs during the drug development process. Currently, even if drug candidates show excellent results in animal testing, about 90% of them fail during actual human clinical trials. This limitation stems from the biological differences between humans and animals.
The research center plans to address this issue using "organoids," which are 3D human organ models derived from patient cells. In particular, the center will actively develop NAMs, which regulatory bodies like the US FDA and European agencies are pushing to adopt, to build a drug discovery platform that predicts human biological responses more accurately. This approach is expected to drastically reduce the time and cost required to develop new drugs for rare and incurable diseases.
The greatest strength of this collaboration lies in the integration of Chang Gung Memorial Hospital’s vast patient tissue and clinical data—Taiwan's largest medical institution—with KAIST's world-class organoid, AI, and optical technologies.
Boasting a medical infrastructure of 12,000 beds, Chang Gung Memorial Hospital possesses a massive repository of accumulated patient data. The research center will leverage this data to build disease-specific organoid models and utilize AI-based analysis to identify disease mechanisms and discover new drug candidates.
Furthermore, by establishing a next-generation bio-R&D platform that utilizes organoids and AI, the KAIST-FORMOSA BIO R&D CENTER aims to enhance the efficiency of drug discovery, commercialize relevant technologies, and enter the global market. This initiative is expected to strengthen the bio-cooperation ecosystem and contribute to creating new industries and jobs.
Professor Dae-Soo Kim from the Department of Brain and Cognitive Sciences at KAIST stated:
"The KAIST-FORMOSA BIO R&D CENTER is a new model of international collaborative research where world-class clinical data meets cutting-edge biotechnology. We will develop it into a global research hub that reduces reliance on animal testing and accelerates patient-tailored precision medicine and drug discovery."
Formosa Group Chairman Ruey-Yu Wang, remarked:
"I find it highly meaningful to visit KAIST in person to join the opening of the KAIST-FORMOSA BIO R&D CENTER. I would like to express my deepest gratitude to President Kwang Hyung Lee, the KAIST research team, and all members for showing exceptional interest and leadership in laying the foundation for this cooperation and establishing the center. I hope this research center will establish itself as a symbolic collaborative platform leading future bio-innovation together."
She further added, "Formosa Group will actively support the research outcomes so that they can translate into actual patient treatment and industrial innovation, and we will lead global biomedical innovation hand-in-hand with KAIST."
Kwang Hyung Lee, President of KAIST, said:
"The opening of this research center is a prime example showing the global expansion of KAIST's biotechnology. I extend my deepest gratitude to Chair Sandy Wang and the officials from Formosa Group for taking the time out of their busy schedules to visit KAIST and celebrate this opening. Through this collaboration, we expect that the integration of KAIST's advanced biotechnology with the rich clinical capabilities of Formosa Group and Chang Gung Memorial Hospital will serve as a catalyst for creating new growth engines in the future bio-industry."
Meanwhile, Formosa Group is a global enterprise operating businesses across diverse industrial sectors, including petrochemicals, biotechnology, semiconductor materials, and energy. Chang Gung Memorial Hospital is one of the largest medical institutions in Taiwan, leading global bio-research backed by rich clinical data and research infrastructure.
2026.06.17 View 2118 -
KAIST Produces Eco-Friendly Core Nylon Precursors Used from Clothing to Automobiles with Microbes
<(From Left) Dr. Da-Hee Ahn, Distinguished Professor Sang Yup Lee>
Nylon is a representative plastic material used throughout our daily lives, from clothing to automobiles. However, most of its raw materials have been produced through petrochemical processes, resulting in large carbon emissions. KAIST researchers have developed a technology that can produce key nylon precursors in an eco-friendly way using microbes.
KAIST (President Kwang Hyung Lee) announced on the 31st of May that a research team led by Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering has developed an Escherichia coli-based modular platform capable of producing three key monomers (basic molecular units that make up polymers) of “nylon 6,6” and “nylon 6” — adipic acid, hexamethylenediamine, and epsilon-caprolactam — from “glycerol (an eco-friendly bio-based byproduct generated during biodiesel production),” a renewable carbon source, using systems metabolic engineering (a technology that designs and optimizes microbial metabolic pathways to maximize the production of desired substances).
“Nylon 6” is highly flexible and is used in clothing and films, while “nylon 6,6” has excellent strength and heat resistance and is used in automobiles and machinery parts. The numbers after the nylon name indicate the number of carbon atoms contained in the raw material molecules.
The core of this study is that the biosynthetic pathway was divided into upstream and downstream modules, with E. coli strains assigned different roles. The upstream strain was designed to produce adipic acid from glycerol, while the downstream strain was designed to convert it into hexamethylenediamine or epsilon-caprolactam, respectively. Through this, the research team succeeded in producing adipic acid and hexamethylenediamine, the key raw materials of nylon 6,6, and epsilon-caprolactam, the key raw material of nylon 6, within a single integrated platform.
To improve production efficiency, the researchers compared and validated various enzymes (proteins that promote chemical reactions in living organisms), including carboxylic acid reductases and transaminases, and applied the optimal combination, thereby improving hexamethylenediamine titer. In addition, in the epsilon-caprolactam production process, they designed a flexible-linker fusion enzyme that enhances reaction efficiency through efficient cofactor regeneration.In the upstream module, the team reconstructed the biosynthetic pathway (a series of reaction processes through which compounds are produced in living organisms) and improved the performance of key enzymes using artificial intelligence (AI), increasing production titer. As a result, they succeeded in producing adipic acid at a level of 6 grams per liter (g/L) in a fed-batch fermentation process.
The research team also applied a “delayed inoculation” strategy (time-staggered co-culture), in which the second strain is introduced later after sufficient adipic acid has first been produced, rather than adding the two types of E. coli simultaneously. This is a method of sequentially introducing microbes with different roles at different times.
When this strategy was applied to a fed-batch fermentation process (a fermentation method that increases productivity by supplying nutrients step by step), the team produced 230 milligrams per liter (mg/L) of hexamethylenediamine and 808 micrograms per liter (μg/L) of epsilon-caprolactam using only glycerol. Although the production amounts are not yet high, the research team explained that these results represent world-class performance among cases of direct production from glycerol.
<Schematic Diagram>
This technology is significant in that it presents the possibility of producing nylon raw materials, which have relied on petrochemical processes, through bio-based methods.
The research team plans to further improve titer by combining AI-based enzyme design with additional systems metabolic engineering, and to expand the platform to produce various polymer raw materials (substances formed by the repeated bonding of multiple monomers).
Distinguished Professor Sang Yup Lee stated, “This study is meaningful in that it presents a modular microbial platform capable of producing key monomers required for nylon 6 and nylon 6,6 production from renewable carbon sources,” adding, “We will continue to advance enzyme and metabolic flux engineering to improve titer and develop this into a core platform for sustainably producing various bio-based polymer raw materials.”
The results of this study were published on May 4 in the Proceedings of the National Academy of Sciences (PNAS), with Dr. Da-Hee Ahn of the Department of Chemical and Biomolecular Engineering as the first author.
※ Paper title: “Metabolic engineering of Escherichia coli for the biosynthesis of nylon 6 and nylon 6,6 monomers”
Authors: Sang Yup Lee (KAIST, corresponding author), Da-Hee Ahn (KAIST, first author), Tong Un Chae (KAIST, second author), total of 3 authors
DOI: https://doi.org/10.1073/pnas.2535786123
This research was supported by the “Development of Platform Technologies of Microbial Cell Factories for the Next-Generation Biorefineries” project under the Petroleum Replacement Eco-Friendly Chemical Technology Development Program supported by the Ministry of Science and ICT, and by the “Development of Advanced Synthetic Biology Source Technologies for Leading the Biomanufacturing Industry” project under the Core Synthetic Biology Technology Development Program.
2026.06.01 View 2011 -
European Academy of Microbiology welcomes 95 new Fellows
<KAIST Distinguished Professor Sang Yup Lee>
The European Academy of Microbiology (EAM) is pleased to announce the election of 95 new Fellows, recognising scientific excellence and long-standing contributions to microbiology.
The newly elected Fellows represent a diverse range of expertise across microbiology and related disciplines, spanning institutions across Europe and beyond. Their work reflects the breadth and dynamism of the field, from fundamental microbial research to applied innovations addressing global challenges in health, environment, and biotechnology.
Election to the EAM Fellowship recognises outstanding scientific achievement and leadership in microbiology. Fellows are selected through a rigorous nomination and evaluation process by existing members of the Academy.
With the addition of these new Fellows in different areas of microbiology from Europe and beyond, the EAM continues to strengthen its network of leading microbiologists. As Fellows of the Academy, members are committed to advancing knowledge, fostering collaboration, and supporting the next generation of scientists. Together they promote the visibility, impact and rapid progress of microbiology across the world.
Reflecting strength and diversity of microbiology
Commenting on the election, the EAM President Prof. Cecília M. Arraiano said:
“We are delighted to welcome this new group of Fellows to the European Academy of Microbiology. Their achievements and expertise reflect the strength and diversity of microbiology. The Academy thrives through the engagement of its Fellows, and we look forward to the perspectives and contributions they will bring to shape the future of microbial science.”
See the full list of the newly elected Fellows.
About the European Academy of Microbiology (EAM)
The European Academy of Microbiology, is part of the Federation of European Microbiological Societies (FEMS) network, and brings together eminent microbiologists whose work has significantly advanced the field. Through the collective expertise of its Fellows, the Academy contributes to scientific dialogue, supports emerging priorities in microbiology, and helps amplify the impact of microbiological research for society.
2026.03.20 View 2046