Medical
-
KAIST to Develop a Korean-style ChatGPT Platform Specifically Geared Toward Medical Diagnosis and Drug Discovery
On May 23rd, KAIST (President Kwang-Hyung Lee) announced that its Digital Bio-Health AI Research Center (Director: Professor JongChul Ye of KAIST Kim Jaechul Graduate School of AI) has been selected for the Ministry of Science and ICT's 'AI Top-Tier Young Researcher Support Program (AI Star Fellowship Project).' With a total investment of ₩11.5 billion from May 2025 to December 2030, the center will embark on the full-scale development of AI technology and a platform capable of independently inferring and determining the kinds of diseases, and discovering new drugs.
< Photo. On May 20th, a kick-off meeting for the AI Star Fellowship Project was held at KAIST Kim Jaechul Graduate School of AI’s Yangjae Research Center with the KAIST research team and participating organizations of Samsung Medical Center, NAVER Cloud, and HITS. [From left to right in the front row] Professor Jaegul Joo (KAIST), Professor Yoonjae Choi (KAIST), Professor Woo Youn Kim (KAIST/HITS), Professor JongChul Ye (KAIST), Professor Sungsoo Ahn (KAIST), Dr. Haanju Yoo (NAVER Cloud), Yoonho Lee (KAIST), HyeYoon Moon (Samsung Medical Center), Dr. Su Min Kim (Samsung Medical Center) >
This project aims to foster an innovative AI research ecosystem centered on young researchers and develop an inferential AI agent that can utilize and automatically expand specialized knowledge systems in the bio and medical fields.
Professor JongChul Ye of the Kim Jaechul Graduate School of AI will serve as the lead researcher, with young researchers from KAIST including Professors Yoonjae Choi, Kimin Lee, Sungsoo Ahn, and Chanyoung Park, along with mid-career researchers like Professors Jaegul Joo and Woo Youn Kim, jointly undertaking the project. They will collaborate with various laboratories within KAIST to conduct comprehensive research covering the entire cycle from the theoretical foundations of AI inference to its practical application.
Specifically, the main goals include: - Building high-performance inference models that integrate diverse medical knowledge systems to enhance the precision and reliability of diagnosis and treatment. - Developing a convergence inference platform that efficiently combines symbol-based inference with neural network models. - Securing AI technology for new drug development and biomarker discovery based on 'cell ontology.'
Furthermore, through close collaboration with industry and medical institutions such as Samsung Medical Center, NAVER Cloud, and HITS Co., Ltd., the project aims to achieve: - Clinical diagnostic AI utilizing medical knowledge systems. - AI-based molecular target exploration for new drug development. - Commercialization of an extendible AI inference platform.
Professor JongChul Ye, Director of KAIST's Digital Bio-Health AI Research Center, stated, "At a time when competition in AI inference model development is intensifying, it is a great honor for KAIST to lead the development of AI technology specialized in the bio and medical fields with world-class young researchers." He added, "We will do our best to ensure that the participating young researchers reach a world-leading level in terms of research achievements after the completion of this seven-year project starting in 2025."
The AI Star Fellowship is a newly established program where post-doctoral researchers and faculty members within seven years of appointment participate as project leaders (PLs) to independently lead research. Multiple laboratories within a university and demand-side companies form a consortium to operate the program.
Through this initiative, KAIST plans to nurture bio-medical convergence AI talent and simultaneously promote the commercialization of core technologies in collaboration with Samsung Medical Center, NAVER Cloud, and HITS.
2025.05.26 View 931 -
Formosa Group of Taiwan to Establish Bio R&D Center at KAIST Investing 12.5 M USD
KAIST (President Kwang-Hyung Lee) announced on February 17th that it signed an agreement for cooperation in the bio-medical field with Formosa Group, one of the three largest companies in Taiwan.
< Formosa Group Chairman Sandy Wang and KAIST President Kwang-Hyung Lee at the signing ceremony >
Formosa Group Executive Committee member and Chairman Sandy Wang, who leads the group's bio and eco-friendly energy sectors, decided to establish a bio-medical research center within KAIST and invest approximately KRW 18 billion or more over 5 years. In addition, to commercialize the research results, KAIST and Formosa Group will establish a joint venture in Korea with KAIST Holdings, a KAIST-funded company.
The cooperation between the two organizations began in early 2023 when KAIST signed a comprehensive exchange and cooperation agreement (MOU) with Ming Chi University of Science and Technology (明志科技大學), Chang Gung University (長庚大學), and Chang Gung Memorial Hospital (長庚記念醫院), which are established and supported by Formosa Group. Afterwards, Chairman Sandy Wang visited KAIST in May 2024 and signed a more specific business agreement (MOA).
KAIST Holdings is a holding company established by KAIST, a government-funded organization, to attract investment and conduct business, and will pursue the establishment of a joint venture with a 50:50 equity structure in cooperation with Formosa Group. KAIST Holdings will invest KAIST’s intellectual property rights, and Formosa Group will invest a corresponding amount of funds.
The KAIST-Formosa joint venture will provide research funds to the KAIST-Formosa Bio-Medical Research Center to be established in the future, secure the right to implement the intellectual property rights generated, and promote full-scale business.
The KAIST-Formosa Bio-Medical Research Center will establish a ‘brain organoid bank’ created by obtaining tissues from hundreds of patients with degenerative brain diseases, thereby securing high-dimensional data that will reveal the fundamental causes of aging and disease. It is expected that KAIST’s world-class artificial intelligence technology will analyze large-scale patient data to find the causes of aging and disease.
Through this business, it is expected that by 2030, five years from now, it will discover more than 10 types of intractable brain disease treatments and expand to more than 20 businesses, including human cell-centered diagnostics and preclinical businesses, and secure infrastructure and intellectual property rights that can create value worth approximately KRW 250 billion.
The Chang Gung Memorial Hospital in Taiwan has 10,000 beds and handles 35,000 patients per day, and systematically accumulates patient tissue and clinical data. Chang Gung Memorial Hospital will differentiate the tissues of patients with degenerative brain diseases and send them to the KAIST-Formosa Bio-Medical Research Center, which will then produce brain organoids to be used for disease research and new drug development. This will allow the world’s largest patient tissue data bank to be established.
Dean Daesoo Kim of the College of Life Science and Bioengineering at KAIST said, “This collaboration between KAIST and Formosa Group is a new research collaboration model that goes beyond joint research to establish a joint venture and global commercialization of developed technologies, and it is significant in that it can serve as an opportunity to promote biomedical research and development.”
With this agreement, KAIST, which has been promoting the KAIST Advanced Regenerative Medicine Engineering Center in Osong K-Bio Square, has secured a practical global partner.
< Representatives of the Formosa Group and KAIST >
KAIST’s Senior Vice President for Planning and Budget, Professor Kyung-Soo Kim emphasized, “KAIST has made great efforts to secure an edge in state-of-the-art biomedical fields such as stem cells and gene editing technology, by attracting the world’s best experts and discovering global cooperation partners, and these results can ultimately be linked to the Osong K-Bio Square project.”
SVP Kim then predicted, “In particular, the practical cooperation with Taiwan’s best Formosa Chang Gung Memorial Hospital, which has abundant clinical experience in stem cell treatment, will be an important axis of KAIST’s bio innovation strategy.”
Formosa Chairman Sandy Wang emphasized that this investment and cooperation is built on trust in KAIST’s R&D capabilities and the passion of its researchers. And added that through this, the Formosa Group will practice corporate social responsibility and take an important first step together with KAIST to protect the welfare and health of humanity. She also went on the say that she expects to see the cooperation expanded to various fields such as mobility and semiconductors based on the successes begotten from the cooperation in the bio field.
KAIST President Kwang-Hyung Lee said, “I evaluate this agreement as one of the most important events that will spearhead KAIST into overseas biotechnology stages,” and added, “I expect that this cooperation will be an opportunity for Taiwan and Korea, both of which have IT industry-centered structures, to create new growth engines in the bio industry.” Meanwhile, Formosa Group is a company founded by Chairman Sandy Wang’s father, Chairman Yung-Ching Wang. It is the world’s No. 1 plastic PVC producer and is leading core industries of the Taiwanese economy, including semiconductors, steel, heavy industry, bio, and batteries. Chairman Yung-Ching Wang was respected by the Taiwanese people for his exemplary return of wealth to society under the belief that the companies and assets he founded “belong to the people.”
2025.02.17 View 2707 -
Genome Sequencing Unveils Mutational Impacts of Radiation on Mammalian Cells
Recent release of the waste water from Japan's Fukushima nuclear disaster stirred apprehension regarding the health implications of radiation exposure. Classified as a Group 1 carcinogen, ionizing radiation has long been associated with various cancers and genetic disorders, as evidenced by survivors and descendants of atomic bombings and the Chernobyl disaster. Despite much smaller amount, we remain consistently exposed to low levels of radiation in everyday life and medical procedures.
Radiation, whether in the form of high-energy particles or electromagnetic waves, is conventionally known to break our cellular DNA, leading to cancer and genetic disorders. Yet, our understanding of the quantitative and qualitative mutational impacts of ionizing radiation has been incomplete.
On the 14th, Professor Young Seok Ju and his research team from KAIST, in collaboration with Dr. Tae Gen Son from the Dongnam Institute of Radiological and Medical Science, and Professors Kyung Su Kim and Ji Hyun Chang from Seoul National University, unveiled a breakthrough. Their study, led by joint first authors Drs. Jeonghwan Youk, Hyun Woo Kwon, Joonoh Lim, Eunji Kim and Tae-Woo Kim, titled "Quantitative and qualitative mutational impact of ionizing radiation on normal cells," was published in Cell Genomics.
Employing meticulous techniques, the research team comprehensively analyzed the whole-genome sequences of cells pre- and post-radiation exposure, pinpointing radiation-induced DNA mutations. Experiments involving cells from different organs of humans and mice exposed to varying radiation doses revealed mutation patterns correlating with exposure levels. (Figure 1)
Notably, exposure to 1 Gray (Gy) of radiation resulted in on average 14 mutations in every post-exposure cell. (Figure 2) Unlike other carcinogens, radiation-induced mutations primarily comprised short base deletions and a set of structural variations including inversions, translocations, and various complex genomic rearrangements. (Figure 3) Interestingly, experiments subjecting cells to low radiation dose rate over 100 days demonstrated that mutation quantities, under equivalent total radiation doses, mirrored those of high-dose exposure.
"Through this study, we have clearly elucidated the effects of radiation on cells at the molecular level," said Prof. Ju at KAIST. "Now we understand better how radiation changes the DNA of our cells," he added.
Dr. Son from the Dongnam Institute of Radiological and Medical Science stated, "Based on this study, we will continue to research the effects of very low and very high doses of radiation on the human body," and further remarked, "We will advance the development of safe and effective radiation therapy techniques."
Professors Kim and Chang from Seoul National University College of Medicine expressed their views, saying, "Through this study, we believe we now have a tool to accurately understand the impact of radiation on human DNA," and added, "We hope that many subsequent studies will emerge using the research methodologies employed in this study."
This research represents a significant leap forward in radiation studies, made possible through collaborative efforts and interdisciplinary approaches. This pioneering research engaged scholars from diverse backgrounds, spanning from the Genetic Engineering Research Institute at Seoul National University, the Cambridge Stem Cell Institute in the UK, the Institute for Molecular Biotechnology in Austria (IMBA), and the Genome Insight Inc. (a KAIST spin-off start-up). This study was supported by various institutions including the National Research Foundation of Korea, Dongnam Institute of Radiological and Medical Science (supported by Ministry of Science and ICT, the government of South Korea), the Suh Kyungbae Foundation, the Human Frontier Science Program (HFSP), and the Korea University Anam Hospital Korea Foundation for the Advancement of Science and Creativity, the Ministry of Science and ICT, and the National R&D Program.
2024.02.15 View 7569 -
KAIST Holds 2023 Commencement Ceremony
< Photo 1. On the 17th, KAIST held the 2023 Commencement Ceremony for a total of 2,870 students, including 691 doctors. >
KAIST held its 2023 commencement ceremony at the Sports Complex of its main campus in Daejeon at 2 p.m. on February 27. It was the first commencement ceremony to invite all its graduates since the start of COVID-19 quarantine measures.
KAIST awarded a total of 2,870 degrees including 691 PhD degrees, 1,464 master’s degrees, and 715 bachelor’s degrees, which adds to the total of 74,999 degrees KAIST has conferred since its foundation in 1971, which includes 15,772 PhD, 38,360 master’s and 20,867 bachelor’s degrees.
This year’s Cum Laude, Gabin Ryu, from the Department of Mechanical Engineering received the Minister of Science and ICT Award. Seung-ju Lee from the School of Computing received the Chairman of the KAIST Board of Trustees Award, while Jantakan Nedsaengtip, an international student from Thailand received the KAIST Presidential Award, and Jaeyong Hwang from the Department of Physics and Junmo Lee from the Department of Industrial and Systems Engineering each received the President of the Alumni Association Award and the Chairman of the KAIST Development Foundation Award, respectively.
Minister Jong-ho Lee of the Ministry of Science and ICT awarded the recipients of the academic awards and delivered a congratulatory speech.
Yujin Cha from the Department of Bio and Brain Engineering, who received a PhD degree after 19 years since his entrance to KAIST as an undergraduate student in 2004 gave a speech on behalf of the graduates to move and inspire the graduates and the guests.
After Cha received a bachelor’s degree from the Department of Nuclear and Quantum Engineering, he entered a medical graduate school and became a radiation oncology specialist. But after experiencing the death of a young patient who suffered from osteosarcoma, he returned to his alma mater to become a scientist. As he believes that science and technology is the ultimate solution to the limitations of modern medicine, he started as a PhD student at the Department of Bio and Brain Engineering in 2018, hoping to find such solutions.
During his course, he identified the characteristics of the decision-making process of doctors during diagnosis, and developed a brain-inspired AI algorithm. It is an original and challenging study that attempted to develop a fundamental machine learning theory from the data he collected from 200 doctors of different specialties.
Cha said, “Humans and AI can cooperate by humans utilizing the unique learning abilities of AI to develop our expertise, while AIs can mimic us humans’ learning abilities to improve.” He added, “My ultimate goal is to develop technology to a level at which humans and machines influence each other and ‘coevolve’, and applying it not only to medicine, but in all areas.”
Cha, who is currently an assistant professor at the KAIST Biomedical Research Center, has also written Artificial Intelligence for Doctors in 2017 to help medical personnel use AI in clinical fields, and the book was selected as one of the 2018 Sejong Books in the academic category.
During his speech at this year’s commencement ceremony, he shared that “there are so many things in the world that are difficult to solve and many things to solve them with, but I believe the things that can really broaden the horizons of the world and find fundamental solutions to the problems at hand are science and technology.”
Meanwhile, singer-songwriter Sae Byul Park who studied at the KAIST Graduate School of Culture Technology will also receive her PhD degree.
Natural language processing (NLP) is a field in AI that teaches a computer to understand and analyze human language that is actively being studied. An example of NLP is ChatGTP, which recently received a lot of attention. For her research, Park analyzed music rather than language using NLP technology.
To analyze music, which is in the form of sound, using the methods for NLP, it is necessary to rebuild notes and beats into a form of words or sentences as in a language. For this, Park designed an algorithm called Mel2Word and applied it to her research.
She also suggested that by converting melodies into texts for analysis, one would be able to quantitatively express music as sentences or words with meaning and context rather than as simple sounds representing a certain note.
Park said, “music has always been considered as a product of subjective emotion, but this research provides a framework that can calculate and analyze music.”
Park’s study can later be developed into a tool to measure the similarities between musical work, as well as a piece’s originality, artistry and popularity, and it can be used as a clue to explore the fundamental principles of how humans respond to music from a cognitive science perspective.
Park began her Ph.D. program in 2014, while carrying on with her musical activities as well as public and university lectures alongside, and dealing with personally major events including marriage and childbirth during the course of years. She already met the requirements to receive her degree in 2019, but delayed her graduation in order to improve the level of completion of her research, and finally graduated with her current achievements after nine years.
Professor Juhan Nam, who supervised Park’s research, said, “Park, who has a bachelor’s degree in psychology, later learned to code for graduate school, and has complete high-quality research in the field of artificial intelligence.” He added, “Though it took a long time, her attitude of not giving up until the end as a researcher is also excellent.”
Sae Byul Park is currently lecturing courses entitled Culture Technology and Music Information Retrieval at the Underwood International College of Yonsei University.
Park said, “the 10 or so years I’ve spent at KAIST as a graduate student was a time I could learn and prosper not only academically but from all angles of life.” She added, “having received a doctorate degree is not the end, but a ‘commencement’. Therefore, I will start to root deeper from the seeds I sowed and work harder as a both a scholar and an artist.”
< Photo 2. From left) Yujin Cha (Valedictorian, Medical-Scientist Program Ph.D. graduate), Saebyeol Park (a singer-songwriter, Ph.D. graduate from the Graduate School of Culture and Technology), Junseok Moon and Inah Seo (the two highlighted CEO graduates from the Department of Management Engineering's master’s program) >
Young entrepreneurs who dream of solving social problems will also be wearing their graduation caps. Two such graduates are Jun-seok Moon and Inah Seo, receiving their master’s degrees in social entrepreneurship MBA from the KAIST College of Business.
Before entrance, Moon ran a café helping African refugees stand on their own feet. Then, he entered KAIST to later expand his business and learn social entrepreneurship in order to sustainably help refugees in the blind spots of human rights and welfare.
During his master’s course, Moon realized that he could achieve active carbon reduction by changing the coffee alone, and switched his business field and founded Equal Table. The amount of carbon an individual can reduce by refraining from using a single paper cup is 10g, while changing the coffee itself can reduce it by 300g.
1kg of coffee emits 15kg of carbon over the course of its production, distribution, processing, and consumption, but Moon produces nearly carbon-neutral coffee beans by having innovated the entire process. In particular, the company-to-company ESG business solution is Moon’s new start-up area. It provides companies with carbon-reduced coffee made by roasting raw beans from carbon-neutral certified farms with 100% renewable energy, and shows how much carbon has been reduced in its making. Equal Table will launch the service this month in collaboration with SK Telecom, its first partner.
Inah Seo, who also graduated with Moon, founded Conscious Wear to start a fashion business reducing environmental pollution. In order to realize her mission, she felt the need to gain the appropriate expertise in management, and enrolled for the social entrepreneurship MBA.
Out of the various fashion industries, Seo focused on the leather market, which is worth 80 trillion won. Due to thickness or contamination issues, only about 60% of animal skin fabric is used, and the rest is discarded. Heavy metals are used during such processes, which also directly affects the environment.
During the social entrepreneurship MBA course, Seo collaborated with SK Chemicals, which had links through the program, and launched eco-friendly leather bags. The bags used discarded leather that was recycled by grinding and reprocessing into a biomaterial called PO3G. It was the first case in which PO3G that is over 90% biodegradable was applied to regenerated leather. In other words, it can reduce environmental pollution in the processing and disposal stages, while also reducing carbon emissions and water usage by one-tenth compared to existing cowhide products.
The social entrepreneurship MBA course, from which Moon and Seo graduated, will run in integration with the Graduate School of Green Growth as an Impact MBA program starting this year. KAIST plans to steadily foster entrepreneurs who will lead meaningful changes in the environment and society as well as economic values through innovative technologies and ideas.
< Photo 3. NYU President Emeritus John Sexton (left), who received this year's honorary doctorate of science, poses with President Kwang Hyung Lee >
Meanwhile, during this day’s commencement ceremony, KAIST also presented President Emeritus John Sexton of New York University with an honorary doctorate in science. He was recognized for laying the foundation for the cooperation between KAIST and New York University, such as promoting joint campuses.
< Photo 4. At the commencement ceremony of KAIST held on the 17th, President Kwang Hyung Lee is encouraging the graduates with his commencement address. >
President Kwang Hyung Lee emphasized in his commencement speech that, “if you can draw up the future and work hard toward your goal, the future can become a work of art that you create with your own hands,” and added, “Never stop on the journey toward your dreams, and do not give up even when you are met with failure. Failure happens to everyone, all the time. The important thing is to know 'why you failed', and to use those elements of failure as the driving force for the next try.”
2023.02.20 View 19571 -
Mathematicians Identify a Key Source of Cell-to-Cell Variability in Cell Signaling
Systematic inferences identify a major source of heterogeneity in cell signaling dynamics
Why do genetically identical cells respond differently to the same external stimuli, such as antibiotics? This long-standing mystery has been solved by KAIST and IBS mathematicians who have developed a new framework for analyzing cell responses to some stimuli. The team found that the cell-to-cell variability in antibiotic stress response increases as the effective length of the cell signaling pathway (i.e., the number of rate-limiting steps) increases. This finding could identify more effective chemotherapies to overcome the fractional killing of cancer cells caused by cell-to-cell variability.
Cells in the human body contain signal transduction systems that respond to various external stimuli such as antibiotics and changes in osmotic pressure. When an external stimulus is detected, various biochemical reactions occur sequentially. This leads to the expression of relevant genes, allowing the cells to respond to the perturbed external environment. Furthermore, signal transduction leads to a drug response (e.g., antibiotic resistance genes are expressed when antibiotic drugs are given).
However, even when the same external stimuli are detected, the responses of individual cells are greatly heterogeneous. This leads to the emergence of persister cells that are highly resistant to drugs. To identify potential sources of this cell-to cell variability, many studies have been conducted. However, most of the intermediate signal transduction reactions are unobservable with current experimental techniques.
A group of researchers including Dae Wook Kim and Hyukpyo Hong and led by Professor Jae Kyoung Kim from the KAIST Department of Mathematical Sciences and IBS Biomedical Mathematics Group solved the mystery by exploiting queueing theory and Bayesian inference methodology. They proposed a queueing process that describes the signal transduction system in cells. Based on this, they developed Bayesian inference computational software using MBI (the Moment-based Bayesian Inference method). This enables the analysis of the signal transduction system without a direct observation of the intermediate steps. This study was published in Science Advances.
By analyzing experimental data from Escherichia coli using MBI, the research team found that cell-to-cell variability increases as the number of rate-limiting steps in the signaling pathway increases.
The rate-limiting steps denote the slowest steps (i.e., bottlenecks) in sequential biochemical reaction steps composing cell signaling pathways and thus dominates most of the signaling time. As the number of the rate-limiting steps increases, the intensity of the transduced signal becomes greatly heterogeneous even in a population of genetically identical cells. This finding is expected to provide a new paradigm for studying the heterogeneous antibiotic resistance of cells, which is a big challenge in cancer medicine.
Professor Kim said, “As a mathematician, I am excited to help advance the understanding of cell-to-cell variability in response to external stimuli. I hope this finding facilitates the development of more effective chemotherapies.”
This work was supported by the Samsung Science and Technology Foundation, the National Research Foundation of Korea, and the Institute for Basic Science.
-Publication:Dae Wook Kim, Hyukpyo Hong, and Jae Kyoung Kim (2022) “Systematic inference identifies a major source of heterogeneity in cell signaling dynamics: the rate-limiting step number,”Science Advances March 18, 2022 (DOI: 10.1126/sciadv.abl4598)
-Profile:Professor Jae Kyoung Kimhttp://mathsci.kaist.ac.kr/~jaekkim
jaekkim@kaist.ac.kr@umichkim on TwitterDepartment of Mathematical SciencesKAIST
2022.03.29 View 10447 -
Tomographic Measurement of Dielectric Tensors
Dielectric tensor tomography allows the direct measurement of the 3D dielectric tensors of optically anisotropic structures
A research team reported the direct measurement of dielectric tensors of anisotropic structures including the spatial variations of principal refractive indices and directors. The group also demonstrated quantitative tomographic measurements of various nematic liquid-crystal structures and their fast 3D nonequilibrium dynamics using a 3D label-free tomographic method. The method was described in Nature Materials.
Light-matter interactions are described by the dielectric tensor. Despite their importance in basic science and applications, it has not been possible to measure 3D dielectric tensors directly. The main challenge was due to the vectorial nature of light scattering from a 3D anisotropic structure. Previous approaches only addressed 3D anisotropic information indirectly and were limited to two-dimensional, qualitative, strict sample conditions or assumptions.
The research team developed a method enabling the tomographic reconstruction of 3D dielectric tensors without any preparation or assumptions. A sample is illuminated with a laser beam with various angles and circularly polarization states. Then, the light fields scattered from a sample are holographically measured and converted into vectorial diffraction components. Finally, by inversely solving a vectorial wave equation, the 3D dielectric tensor is reconstructed.
Professor YongKeun Park said, “There were a greater number of unknowns in direct measuring than with the conventional approach. We applied our approach to measure additional holographic images by slightly tilting the incident angle.”
He said that the slightly tilted illumination provides an additional orthogonal polarization, which makes the underdetermined problem become the determined problem. “Although scattered fields are dependent on the illumination angle, the Fourier differentiation theorem enables the extraction of the same dielectric tensor for the slightly tilted illumination,” Professor Park added.
His team’s method was validated by reconstructing well-known liquid crystal (LC) structures, including the twisted nematic, hybrid aligned nematic, radial, and bipolar configurations. Furthermore, the research team demonstrated the experimental measurements of the non-equilibrium dynamics of annihilating, nucleating, and merging LC droplets, and the LC polymer network with repeating 3D topological defects.
“This is the first experimental measurement of non-equilibrium dynamics and 3D topological defects in LC structures in a label-free manner. Our method enables the exploration of inaccessible nematic structures and interactions in non-equilibrium dynamics,” first author Dr. Seungwoo Shin explained.
-PublicationSeungwoo Shin, Jonghee Eun, Sang Seok Lee, Changjae Lee, Herve Hugonnet, Dong Ki Yoon, Shin-Hyun Kim, Jongwoo Jeong, YongKeun Park, “Tomographic Measurement ofDielectric Tensors at Optical Frequency,” Nature Materials March 02, 2022 (https://doi.org/10/1038/s41563-022-01202-8)
-ProfileProfessor YongKeun ParkBiomedical Optics Laboratory (http://bmol.kaist.ac.kr)Department of PhysicsCollege of Natural SciencesKAIST
2022.03.22 View 8567 -
Scientist Discover How Circadian Rhythm Can Be Both Strong and Flexible
Study reveals that master and slave oscillators function via different molecular mechanisms
From tiny fruit flies to human beings, all animals on Earth maintain their daily rhythms based on their internal circadian clock. The circadian clock enables organisms to undergo rhythmic changes in behavior and physiology based on a 24-hour circadian cycle. For example, our own biological clock tells our brain to release melatonin, a sleep-inducing hormone, at night time.
The discovery of the molecular mechanism of the circadian clock was bestowed the Nobel Prize in Physiology or Medicine 2017. From what we know, no one centralized clock is responsible for our circadian cycles. Instead, it operates in a hierarchical network where there are “master pacemaker” and “slave oscillator”.
The master pacemaker receives various input signals from the environment such as light. The master then drives the slave oscillator that regulates various outputs such as sleep, feeding, and metabolism. Despite the different roles of the pacemaker neurons, they are known to share common molecular mechanisms that are well conserved in all lifeforms. For example, interlocked systems of multiple transcriptional-translational feedback loops (TTFLs) composed of core clock proteins have been deeply studied in fruit flies.
However, there is still much that we need to learn about our own biological clock. The hierarchically-organized nature of master and slave clock neurons leads to a prevailing belief that they share an identical molecular clockwork. At the same time, the different roles they serve in regulating bodily rhythms also raise the question of whether they might function under different molecular clockworks.
Research team led by Professor Kim Jae Kyoung from the Department of Mathematical Sciences, a chief investigator at the Biomedical Mathematics Group at the Institute for Basic Science, used a combination of mathematical and experimental approaches using fruit flies to answer this question. The team found that the master clock and the slave clock operate via different molecular mechanisms.
In both master and slave neurons of fruit flies, a circadian rhythm-related protein called PER is produced and degraded at different rates depending on the time of the day. Previously, the team found that the master clock neuron (sLNvs) and the slave clock neuron (DN1ps) have different profiles of PER in wild-type and Clk-Δ mutant Drosophila. This hinted that there might be a potential difference in molecular clockworks between the master and slave clock neurons.
However, due to the complexity of the molecular clockwork, it was challenging to identify the source of such differences. Thus, the team developed a mathematical model describing the molecular clockworks of the master and slave clocks. Then, all possible molecular differences between the master and slave clock neurons were systematically investigated by using computer simulations. The model predicted that PER is more efficiently produced and then rapidly degraded in the master clock compared to the slave clock neurons. This prediction was then confirmed by the follow-up experiments using animal.
Then, why do the master clock neurons have such different molecular properties from the slave clock neurons? To answer this question, the research team again used the combination of mathematical model simulation and experiments. It was found that the faster rate of synthesis of PER in the master clock neurons allows them to generate synchronized rhythms with a high level of amplitude. Generation of such a strong rhythm with high amplitude is critical to delivering clear signals to slave clock neurons.
However, such strong rhythms would typically be unfavorable when it comes to adapting to environmental changes. These include natural causes such as different daylight hours across summer and winter seasons, up to more extreme artificial cases such as jet lag that occurs after international travel. Thanks to the distinct property of the master clock neurons, it is able to undergo phase dispersion when the standard light-dark cycle is disrupted, drastically reducing the level of PER. The master clock neurons can then easily adapt to the new diurnal cycle. Our master pacemaker’s plasticity explains how we can quickly adjust to the new time zones after international flights after just a brief period of jet lag.
It is hoped that the findings of this study can have future clinical implications when it comes to treating various disorders that affect our circadian rhythm. Professor Kim notes, “When the circadian clock loses its robustness and flexibility, the circadian rhythms sleep disorders can occur. As this study identifies the molecular mechanism that generates robustness and flexibility of the circadian clock, it can facilitate the identification of the cause of and treatment strategy for the circadian rhythm sleep disorders.” This work was supported by the Human Frontier Science Program.
-PublicationEui Min Jeong, Miri Kwon, Eunjoo Cho, Sang Hyuk Lee, Hyun Kim, Eun Young Kim, and Jae Kyoung Kim, “Systematic modeling-driven experiments identify distinct molecularclockworks underlying hierarchically organized pacemaker neurons,” February 22, 2022, Proceedings of the National Academy of Sciences of the United States of America
-ProfileProfessor Jae Kyoung KimDepartment of Mathematical SciencesKAIST
2022.02.23 View 10251 -
President Lee Presents Plans to Nurture Next-Generation Talents
President Lee stressed that nurturing medical scientists, semiconductor R&D personnel, startup entrepreneurs, and global innovators are key missions he will continue to pursue during a news conference
KAIST President Kwang Hyung Lee said that nurturing medical scientists, semiconductor R&D personnel, startup entrepreneurs, and global innovators are key missions he will continue to pursue during an online news conference marking the 1st anniversary of him becoming the president on February 15.
He said that nurturing physician-scientists is the most critical mission for KAIST to help the nation create a new growth engine. He said KAIST will help the nation drive the bio-industry and provide medical science resources for the nation’s health sector. To this end, he said that KAIST will open its Medical Science and Technology School by 2026.
“We plan to expand the current Graduate School of Medical Science and Engineering into a new Medical Science and Technology School that will focus entirely on a condensed MD-PhD course converging the fields of AI, bio, and physics,” he said.
The school aims to foster medical scientists whose research results will eventually be commercialized. He said that the university is now discussing revisions to related laws and regulations with the government and other universities.
To supply human resources to the semiconductor industry, President Lee said the university will add a campus in Pyongtaek City that will serve as an advanced convergence research hub in the field of next generation semiconductors in collaboration with Samsung Electronics and the city of Pyongtaek. The three-stage opening plan projected the final opening of the campus by 2036. During the first stage, which will be completed by 2026, it will construct the campus infrastructure in Pyongtaek city where Samsung Semiconductors runs two massive semiconductor complexes. By 2031, it plans to launch the open research platform including a future cities research center and future vehicles research center. The campus will open the global industrial collaboration cluster hub by 2036.
In the global arena, President Lee said he is working to open the New York campus with stakeholders in the United States. He announced the plan last December that was endorsed by New York-based entrepreneur Hee-Nam Bae, the chairman of Big Continent Inc. President Lee and Chairman Lee signed an MOU for the funding to open the campus in New York.
“We are discussing how to facilitate the plan and best accommodate the interests and potential of our students. Many ideas and plans are on the table and we think it will take longer than expected to finalize the plan,” explained President Lee.
However, he added that the basic idea is to offer art tech and health technology programs as well as an AI-based finance MBA at the New York campus, in addition to it serving as the startup accelerator of KAIST.
President Lee stressed the importance of technology commercialization when successfully launching KAIST Holdings last month to help spinoffs of KAIST labs accelerate their end results. He said that KAIST Holdings will build a virtuous supporting system to commercialize the technology startups coming from KAIST.
“We plan to list at least 10 KAIST startups on the KOSDAQ and two on the NASDAQ by 2031. KAIST Holdings also aims to nurture companies valued at a total of one billion KRW and earn 100 billion KRW in technology fees by 2031.
2022.02.17 View 11310 -
3D Visualization and Quantification of Bioplastic PHA in a Living Bacterial Cell
3D holographic microscopy leads to in-depth analysis of bacterial cells accumulating the bacterial bioplastic, polyhydroxyalkanoate (PHA)
A research team at KAIST has observed how bioplastic granule is being accumulated in living bacteria cells through 3D holographic microscopy. Their 3D imaging and quantitative analysis of the bioplastic ‘polyhydroxyalkanoate’ (PHA) via optical diffraction tomography provides insights into biosynthesizing sustainable substitutes for petroleum-based plastics.
The bio-degradable polyester polyhydroxyalkanoate (PHA) is being touted as an eco-friendly bioplastic to replace existing synthetic plastics. While carrying similar properties to general-purpose plastics such as polyethylene and polypropylene, PHA can be used in various industrial applications such as container packaging and disposable products.
PHA is synthesized by numerous bacteria as an energy and carbon storage material under unbalanced growth conditions in the presence of excess carbon sources. PHA exists in the form of insoluble granules in the cytoplasm. Previous studies on investigating in vivo PHA granules have been performed by using fluorescence microscopy, transmission electron microscopy (TEM), and electron cryotomography.
These techniques have generally relied on the statistical analysis of multiple 2D snapshots of fixed cells or the short-time monitoring of the cells. For the TEM analysis, cells need to be fixed and sectioned, and thus the investigation of living cells was not possible. Fluorescence-based techniques require fluorescence labeling or dye staining. Thus, indirect imaging with the use of reporter proteins cannot show the native state of PHAs or cells, and invasive exogenous dyes can affect the physiology and viability of the cells. Therefore, it was difficult to fully understand the formation of PHA granules in cells due to the technical limitations, and thus several mechanism models based on the observations have been only proposed.
The team of metabolic engineering researchers led by Distinguished Professor Sang Yup Lee and Physics Professor YongKeun Park, who established the startup Tomocube with his 3D holographic microscopy, reported the results of 3D quantitative label-free analysis of PHA granules in individual live bacterial cells by measuring the refractive index distributions using optical diffraction tomography. The formation and growth of PHA granules in the cells of Cupriavidus necator, the most-studied native PHA (specifically, poly(3-hydroxybutyrate), also known as PHB) producer, and recombinant Escherichia coli harboring C. necator PHB biosynthesis pathway were comparatively examined.
From the reconstructed 3D refractive index distribution of the cells, the team succeeded in the 3D visualization and quantitative analysis of cells and intracellular PHA granules at a single-cell level. In particular, the team newly presented the concept of “in vivo PHA granule density.” Through the statistical analysis of hundreds of single cells accumulating PHA granules, the distinctive differences of density and localization of PHA granules in the two micro-organisms were found. Furthermore, the team identified the key protein that plays a major role in making the difference that enabled the characteristics of PHA granules in the recombinant E. coli to become similar to those of C. necator.
The research team also presented 3D time-lapse movies showing the actual processes of PHA granule formation combined with cell growth and division. Movies showing the living cells synthesizing and accumulating PHA granules in their native state had never been reported before.
Professor Lee said, “This study provides insights into the morphological and physical characteristics of in vivo PHA as well as the unique mechanisms of PHA granule formation that undergo the phase transition from soluble monomers into the insoluble polymer, followed by granule formation. Through this study, a deeper understanding of PHA granule formation within the bacterial cells is now possible, which has great significance in that a convergence study of biology and physics was achieved. This study will help develop various bioplastics production processes in the future.”
This work was supported by the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries (Grants NRF-2012M1A2A2026556 and NRF-2012M1A2A2026557) and the Bio & Medical Technology Development Program (Grant No. 2021M3A9I4022740) from the Ministry of Science and ICT (MSIT) through the National Research Foundation (NRF) of Korea to S.Y.L. This work was also supported by the KAIST Cross-Generation Collaborative Laboratory project.
-PublicationSo Young Choi, Jeonghun Oh, JaeHwang Jung, YongKeun Park, and Sang Yup Lee. Three-dimensional label-free visualization and quantification of polyhydroxyalkanoates in individualbacterial cell in its native state. PNAS(https://doi.org./10.1073/pnas.2103956118)
-ProfileDistinguished Professor Sang Yup LeeMetabolic Engineering and Synthetic Biologyhttp://mbel.kaist.ac.kr/
Department of Chemical and Biomolecular Engineering KAIST
Endowed Chair Professor YongKeun ParkBiomedical Optics Laboratoryhttps://bmokaist.wordpress.com/
Department of PhysicsKAIST
2021.07.28 View 13917 -
Professor Jae Kyoung Kim to Lead a New Mathematical Biology Research Group at IBS
Professor Jae Kyoung Kim from the KAIST Department of Mathematical Sciences was appointed as the third Chief Investigator (CI) of the Pioneer Research Center (PRC) for Mathematical and Computational Sciences at the Institute for Basic Science (IBS). Professor Kim will launch and lead a new research group that will be devoted to resolving various biological conundrums from a mathematical perspective. His appointment began on March 1, 2021.
Professor Kim, a rising researcher in the field of mathematical biology, has received attention from both the mathematical and biological communities at the international level. Professor Kim puts novel and unremitting efforts into understanding biological systems such as cell-to-cell interactions mathematically and designing mathematical models for identifying causes of diseases and developing therapeutic medicines.
Through active joint research with biologists, mathematician Kim has addressed many challenges that have remained unsolved in biology and published papers in a number of leading international journals in related fields. His notable works based on mathematical modelling include having designed a biological circuit that can maintain a stable circadian rhythm (Science, 2015) and unveiling the principles of how the biological clock in the body maintains a steady speed for the first time in over 60 years (Molecular Cell, 2015). Recently, through a joint research project with Pfizer, Professor Kim identified what causes the differences between animal and clinical test results during drug development explaining why drugs have different efficacies in different people (Molecular Systems Biology, 2019).
The new IBS biomedical mathematics research group led by Professor Kim will further investigate the causes of unstable circadian rhythms and sleeping patterns. The team will aim to present a new paradigm in treatments for sleep disorders.
Professor Kim said, “We are all so familiar with sleep behaviors, but the exact mechanisms behind how such behaviors occur are still unknown. Through cooperation with biomedical scientists, our group will do its best to discover the complicated, fundamental mechanisms of sleep, and investigate the causes and cures of sleep disorders.”
Every year, the IBS selects young and promising researchers and appoints them as CIs. A maximum of five selected CIs can form each independent research group within the IBS PRC, and receive research funds of 1 billion to 1.5 billion KRW over five years.
(END)
2021.03.18 View 10380 -
Deep-Learning and 3D Holographic Microscopy Beats Scientists at Analyzing Cancer Immunotherapy
Live tracking and analyzing of the dynamics of chimeric antigen receptor (CAR) T-cells targeting cancer cells can open new avenues for the development of cancer immunotherapy. However, imaging via conventional microscopy approaches can result in cellular damage, and assessments of cell-to-cell interactions are extremely difficult and labor-intensive. When researchers applied deep learning and 3D holographic microscopy to the task, however, they not only avoided these difficultues but found that AI was better at it than humans were.
Artificial intelligence (AI) is helping researchers decipher images from a new holographic microscopy technique needed to investigate a key process in cancer immunotherapy “live” as it takes place. The AI transformed work that, if performed manually by scientists, would otherwise be incredibly labor-intensive and time-consuming into one that is not only effortless but done better than they could have done it themselves. The research, conducted by the team of Professor YongKeun Park from the Department of Physics, appeared in the journal eLife last December.
A critical stage in the development of the human immune system’s ability to respond not just generally to any invader (such as pathogens or cancer cells) but specifically to that particular type of invader and remember it should it attempt to invade again is the formation of a junction between an immune cell called a T-cell and a cell that presents the antigen, or part of the invader that is causing the problem, to it. This process is like when a picture of a suspect is sent to a police car so that the officers can recognize the criminal they are trying to track down. The junction between the two cells, called the immunological synapse, or IS, is the key process in teaching the immune system how to recognize a specific type of invader.
Since the formation of the IS junction is such a critical step for the initiation of an antigen-specific immune response, various techniques allowing researchers to observe the process as it happens have been used to study its dynamics. Most of these live imaging techniques rely on fluorescence microscopy, where genetic tweaking causes part of a protein from a cell to fluoresce, in turn allowing the subject to be tracked via fluorescence rather than via the reflected light used in many conventional microscopy techniques.
However, fluorescence-based imaging can suffer from effects such as photo-bleaching and photo-toxicity, preventing the assessment of dynamic changes in the IS junction process over the long term. Fluorescence-based imaging still involves illumination, whereupon the fluorophores (chemical compounds that cause the fluorescence) emit light of a different color. Photo-bleaching or photo-toxicity occur when the subject is exposed to too much illumination, resulting in chemical alteration or cellular damage.
One recent option that does away with fluorescent labelling and thereby avoids such problems is 3D holographic microscopy or holotomography (HT). In this technique, the refractive index (the way that light changes direction when encountering a substance with a different density—why a straw looks like it bends in a glass of water) is recorded in 3D as a hologram.
Until now, HT has been used to study single cells, but never cell-cell interactions involved in immune responses. One of the main reasons is the difficulty of “segmentation,” or distinguishing the different parts of a cell and thus distinguishing between the interacting cells; in other words, deciphering which part belongs to which cell.
Manual segmentation, or marking out the different parts manually, is one option, but it is difficult and time-consuming, especially in three dimensions. To overcome this problem, automatic segmentation has been developed in which simple computer algorithms perform the identification.
“But these basic algorithms often make mistakes,” explained Professor YongKeun Park, “particularly with respect to adjoining segmentation, which of course is exactly what is occurring here in the immune response we’re most interested in.”
So, the researchers applied a deep learning framework to the HT segmentation problem. Deep learning is a type of machine learning in which artificial neural networks based on the human brain recognize patterns in a way that is similar to how humans do this. Regular machine learning requires data as an input that has already been labelled. The AI “learns” by understanding the labeled data and then recognizes the concept that has been labelled when it is fed novel data. For example, AI trained on a thousand images of cats labelled “cat” should be able to recognize a cat the next time it encounters an image with a cat in it. Deep learning involves multiple layers of artificial neural networks attacking much larger, but unlabeled datasets, in which the AI develops its own ‘labels’ for concepts it encounters.
In essence, the deep learning framework that KAIST researchers developed, called DeepIS, came up with its own concepts by which it distinguishes the different parts of the IS junction process. To validate this method, the research team applied it to the dynamics of a particular IS junction formed between chimeric antigen receptor (CAR) T-cells and target cancer cells. They then compared the results to what they would normally have done: the laborious process of performing the segmentation manually. They found not only that DeepIS was able to define areas within the IS with high accuracy, but that the technique was even able to capture information about the total distribution of proteins within the IS that may not have been easily measured using conventional techniques.
“In addition to allowing us to avoid the drudgery of manual segmentation and the problems of photo-bleaching and photo-toxicity, we found that the AI actually did a better job,” Professor Park added.
The next step will be to combine the technique with methods of measuring how much physical force is applied by different parts of the IS junction, such as holographic optical tweezers or traction force microscopy.
-Profile
Professor YongKeun Park
Department of Physics
Biomedical Optics Laboratory
http://bmol.kaist.ac.kr
KAIST
2021.02.24 View 13021 -
'Mini-Lungs' Reveal Early Stages of SARS-CoV-2 Infection
Researchers in Korea and the UK have successfully grown miniature models of critical lung structures called alveoli, and used them to study how the coronavirus that causes COVID-19 infects the lungs.
To date, there have been more than 40 million cases of COVID-19 and almost 1.13 million deaths worldwide. The main target tissues of SARS-CoV-2, the virus that causes COVID-19, especially in patients that develop pneumonia, appear to be alveoli – tiny air sacs in the lungs that take up the oxygen we breathe and exchange it with carbon dioxide to exhale.
To better understand how SARS-CoV-2 infects the lungs and causes disease, a team of Professor Young Seok Ju from the Graduate School of Medical Science and Engineering at KAIST in collaboration with the Wellcome-MRC Cambridge Stem Cell Institute at the University of Cambridge turned to organoids – ‘mini-organs’ grown in three dimensions to mimic the behaviour of tissue and organs.
The team used tissue donated to tissue banks at the Royal Papworth Hospital NHS Foundation Trust and Addenbrooke’s Hospital, Cambridge University NHS Foundations Trust, UK, and Seoul National University Hospital to extract a type of lung cell known as human lung alveolar type 2 cells. By reprogramming these cells back to their earlier ‘stem cell’ stage, they were able to grow self-organizing alveolar-like 3D structures that mimic the behaviour of key lung tissue.
“The research community now has a powerful new platform to study precisely how the virus infects the lungs, as well as explore possible treatments,” said Professor Ju, co-senior author of the research.
Dr. Joo-Hyeon Lee, another co-senior author at the Wellcome-MRC Cambridge Stem Cell Institute, said: “We still know surprisingly little about how SARS-CoV-2 infects the lungs and causes disease. Our approach has allowed us to grow 3D models of key lung tissue – in a sense, ‘mini-lungs’ – in the lab and study what happens when they become infected.”
The team infected the organoids with a strain of SARS-CoV-2 taken from a patient in Korea who was diagnosed with COVID-19 on January 26 after traveling to Wuhan, China. Using a combination of fluorescence imaging and single cell genetic analysis, they were able to study how the cells responded to the virus.
When the 3D models were exposed to SARS-CoV-2, the virus began to replicate rapidly, reaching full cellular infection just six hours after infection. Replication enables the virus to spread throughout the body, infecting other cells and tissue.
Around the same time, the cells began to produce interferons – proteins that act as warning signals to neighbouring cells, telling them to activate their antiviral defences. After 48 hours, the interferons triggered the innate immune response – its first line of defence – and the cells started fighting back against infection.
Sixty hours after infection, a subset of alveolar cells began to disintegrate, leading to cell death and damage to the lung tissue.
Although the researchers observed changes to the lung cells within three days of infection, clinical symptoms of COVID-19 rarely occur so quickly and can sometimes take more than ten days after exposure to appear. The team say there are several possible reasons for this. It may take several days from the virus first infiltrating the upper respiratory tract to it reaching the alveoli. It may also require a substantial proportion of alveolar cells to be infected or for further interactions with immune cells resulting in inflammation before a patient displays symptoms.
“Based on our model we can tackle many unanswered key questions, such as understanding genetic susceptibility to SARS-CoV-2, assessing relative infectivity of viral mutants, and revealing the damage processes of the virus in human alveolar cells,” said Professor Ju. “Most importantly, it provides the opportunity to develop and screen potential therapeutic agents against SARS-CoV-2 infection.”
“We hope to use our technique to grow these 3D models from cells of patients who are particularly vulnerable to infection, such as the elderly or people with diseased lungs, and find out what happens to their tissue,” added Dr. Lee.
The research was a collaboration involving scientists from KAIST, the University of Cambridge, Korea National Institute of Health, Institute for Basic Science (IBS), Seoul National University Hospital and Genome Insight in Korea.
- ProfileProfessor Young Seok JuLaboratory of Cancer Genomics https://julab.kaist.ac.kr the Graduate School of Medical Science and EngineeringKAIST
2020.10.26 View 12188