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KAIST Proposes a New Way to Circumvent a Long-time Frustration in Neural Computing
The human brain begins learning through spontaneous random activities even before it receives sensory information from the external world. The technology developed by the KAIST research team enables much faster and more accurate learning when exposed to actual data by pre-learning random information in a brain-mimicking artificial neural network, and is expected to be a breakthrough in the development of brain-based artificial intelligence and neuromorphic computing technology in the future.
KAIST (President Kwang-Hyung Lee) announced on the 23rd of October that Professor Se-Bum Paik 's research team in the Department of Brain Cognitive Sciences solved the weight transport problem*, a long-standing challenge in neural network learning, and through this, explained the principles that enable resource-efficient learning in biological brain neural networks.
*Weight transport problem: This is the biggest obstacle to the development of artificial intelligence that mimics the biological brain. It is the fundamental reason why large-scale memory and computational work are required in the learning of general artificial neural networks, unlike biological brains.
Over the past several decades, the development of artificial intelligence has been based on error backpropagation learning proposed by Geoffery Hinton, who won the Nobel Prize in Physics this year. However, error backpropagation learning was thought to be impossible in biological brains because it requires the unrealistic assumption that individual neurons must know all the connected information across multiple layers in order to calculate the error signal for learning.
< Figure 1. Illustration depicting the method of random noise training and its effects >
This difficult problem, called the weight transport problem, was raised by Francis Crick, who won the Nobel Prize in Physiology or Medicine for the discovery of the structure of DNA, after the error backpropagation learning was proposed by Hinton in 1986. Since then, it has been considered the reason why the operating principles of natural neural networks and artificial neural networks will forever be fundamentally different.
At the borderline of artificial intelligence and neuroscience, researchers including Hinton have continued to attempt to create biologically plausible models that can implement the learning principles of the brain by solving the weight transport problem.
In 2016, a joint research team from Oxford University and DeepMind in the UK first proposed the concept of error backpropagation learning being possible without weight transport, drawing attention from the academic world. However, biologically plausible error backpropagation learning without weight transport was inefficient, with slow learning speeds and low accuracy, making it difficult to apply in reality.
KAIST research team noted that the biological brain begins learning through internal spontaneous random neural activity even before experiencing external sensory experiences. To mimic this, the research team pre-trained a biologically plausible neural network without weight transport with meaningless random information (random noise).
As a result, they showed that the symmetry of the forward and backward neural cell connections of the neural network, which is an essential condition for error backpropagation learning, can be created. In other words, learning without weight transport is possible through random pre-training.
< Figure 2. Illustration depicting the meta-learning effect of random noise training >
The research team revealed that learning random information before learning actual data has the property of meta-learning, which is ‘learning how to learn.’ It was shown that neural networks that pre-learned random noise perform much faster and more accurate learning when exposed to actual data, and can achieve high learning efficiency without weight transport.
< Figure 3. Illustration depicting research on understanding the brain's operating principles through artificial neural networks >
Professor Se-Bum Paik said, “It breaks the conventional understanding of existing machine learning that only data learning is important, and provides a new perspective that focuses on the neuroscience principles of creating appropriate conditions before learning,” and added, “It is significant in that it solves important problems in artificial neural network learning through clues from developmental neuroscience, and at the same time provides insight into the brain’s learning principles through artificial neural network models.”
This study, in which Jeonghwan Cheon, a Master’s candidate of KAIST Department of Brain and Cognitive Sciences participated as the first author and Professor Sang Wan Lee of the same department as a co-author, will be presented at the 38th Neural Information Processing Systems (NeurIPS), the world's top artificial intelligence conference, to be held in Vancouver, Canada from December 10 to 15, 2024. (Paper title: Pretraining with random noise for fast and robust learning without weight transport)
This study was conducted with the support of the National Research Foundation of Korea's Basic Research Program in Science and Engineering, the Information and Communications Technology Planning and Evaluation Institute's Talent Development Program, and the KAIST Singularity Professor Program.
2024.10.23 View 989 -
KAIST Develops Janus-like Metasurface Technology that Acts According to the Direction of Light
Metasurface technology is an advanced optical technology that is thinner, lighter, and capable of precisely controlling light through nanometer-sized artificial structures compared to conventional technologies. KAIST researchers have overcome the limitations of existing metasurface technologies and successfully designed a Janus metasurface capable of perfectly controlling asymmetric light transmission. By applying this technology, they also proposed an innovative method to significantly enhance security by only decoding information under specific conditions.
KAIST (represented by President Kwang Hyung Lee) announced on the 15th of October that a research team led by Professor Jonghwa Shin from the Department of Materials Science and Engineering had developed a Janus metasurface capable of perfectly controlling asymmetric light transmission.
Asymmetric properties, which react differently depending on the direction, play a crucial role in various fields of science and engineering. The Janus metasurface developed by the research team implements an optical system capable of performing different functions in both directions.
Like the Roman god Janus with two faces, this metasurface shows entirely different optical responses depending on the direction of incoming light, effectively operating two independent optical systems with a single device (for example, a metasurface that acts as a magnifying lens in one direction and as a polarized camera in the other). In other words, by using this technology, it's possible to operate two different optical systems (e.g., a lens and a hologram) depending on the direction of the light.
This achievement addresses a challenge that existing metasurface technologies had not resolved. Conventional metasurface technology had limitations in selectively controlling the three properties of light—intensity, phase, and polarization—based on the direction of incidence.
The research team proposed a solution based on mathematical and physical principles, and succeeded in experimentally implementing different vector holograms in both directions. Through this achievement, they showcased a complete asymmetric light transmission control technology.
< Figure 1. Schematics of a device featuring asymmetric transmission. a) Device operating as a magnifying lens for back-side illumination. b) Device operating as a polarization camera for front-side illumination. >
Additionally, the research team developed a new optical encryption technology based on this metasurface technology. By using the Janus metasurface, they implemented a vector hologram that generates different images depending on the direction and polarization state of incoming light, showcasing an optical encryption system that significantly enhances security by allowing information to be decoded only under specific conditions.
This technology is expected to serve as a next-generation security solution, applicable in various fields such as quantum communication and secure data transmission.
Furthermore, the ultra-thin structure of the metasurface is expected to significantly reduce the volume and weight of traditional optical devices, contributing greatly to the miniaturization and lightweight design of next-generation devices.
< Figure 2. Experimental demonstration of Janus vectorial holograms. With front illuminations, vector images of the butterfly and the grasshopper are created, and with the back-side illuminations, vector images of the ladybug and the beetle are created. >
Professor Jonghwa Shin from the Department of Materials Science and Engineering at KAIST stated, "This research has enabled the complete asymmetric transmission control of light’s intensity, phase, and polarization, which has been a long-standing challenge in optics. It has opened up the possibility of developing various applied optical devices." He added, "We plan to continue developing optical devices that can be applied to various fields such as augmented reality (AR), holographic displays, and LiDAR systems for autonomous vehicles, utilizing the full potential of metasurface technology."
This research, in which Hyeonhee Kim (a doctoral student in the Department of Materials Science and Engineering at KAIST) and Joonkyo Jung participated as co-first authors, was published online in the international journal Advanced Materials and is scheduled to be published in the October 31 issue. (Title of the paper: "Bidirectional Vectorial Holography Using Bi-Layer Metasurfaces and Its Application to Optical Encryption")
The research was supported by the Nano Materials Technology Development Program and the Mid-Career Researcher Program of the National Research Foundation of Korea.
2024.10.15 View 1094 -
Revolutionary 'scLENS' Unveiled to Decode Complex Single-Cell Genomic Data
Unlocking biological information from complex single-cell genomic data has just become easier and more precise, thanks to the innovative 'scLENS' tool developed by the Biomedical Mathematics Group within the IBS Center for Mathematical and Computational Sciences led by Chief Investigator Jae Kyoung Kim, who is also a professor at KAIST. This new finding represents a significant leap forward in the field of single-cell transcriptomics.
Single-cell genomic analysis is an advanced technique that measures gene expression at the individual cell level, revealing cellular changes and interactions that are not observable with traditional genomic analysis methods. When applied to cancer tissues, this analysis can delineate the composition of diverse cell types within a tumor, providing insights into how cancer progresses and identifying key genes involved during each stage of progression.
Despite the immense potential of single-cell genomic analysis, handling the vast amount of data that it generates has always been challenging. The amount of data covers the expression of tens of thousands of genes across hundreds to thousands of individual cells. This not only results in large datasets but also introduces noise-related distortions, which arise in part due to current measurement limitations.
< Figure 1. Overview of scLENS (single-cell Low-dimensional embedding using the effective Noise Subtract) >
(Left) Current dimensionality reduction methods for scRNA-seq data involve conventional data preprocessing steps, such as log normalization, followed by manual selection of signals from the scaled data. However, this study reveals that the high levels of sparsity and variability in scRNA-seq data can lead to signal distortion during the data preprocessing, compromising the accuracy of downstream analyses.
(Right) To address this issue, the researchers integrated L2 normalization into the conventional preprocessing pipeline, effectively mitigating signal distortion. Moreover, they developed a novel signal detection algorithm that eliminates the need for user intervention by leveraging random matrix theory-based noise filtering and signal robustness testing. By incorporating these techniques, scLENS enables accurate and automated analysis of scRNA-seq data, overcoming the limitations of existing dimensionality reduction methods.
Corresponding author Jae Kyoung Kim highlighted, “There has been a remarkable advancement in experimental technologies for analyzing single-cell transcriptomes over the past decade. However, due to limitations in data analysis methods, there has been a struggle to fully utilize valuable data obtained through extensive cost and time."
Researchers have developed numerous analysis methods over the years to discern biological signals from this noise. However, the accuracy of these methods has been less than satisfactory. A critical issue is that determining signal and noise thresholds often depends on subjective decisions from the users.
The newly developed scLENS tool harnesses Random Matrix Theory and Signal robustness test to automatically differentiate signals from noise without relying on subjective user input.
First author Hyun Kim stated, "Previously, users had to arbitrarily decide the threshold for signal and noise, which compromised the reproducibility of analysis results and introduced subjectivity. scLENS eliminates this problem by automatically detecting signals using only the inherent structure of the data."
During the development of scLENS, researchers identified the fundamental reasons for inaccuracies in existing analysis methods. They found that commonly used data preprocessing methods distort both biological signals and noise. The new preprocessing approach that scLENS offers is free from such distortions.
By resolving issues related to noise threshold determined by subjective user choice and signal distortion in conventional data preprocessing, scLENS significantly outperforms existing methods in accuracy. Additionally, scLENS automates the laborious process of signal dimension selection, allowing researchers to extract biological signals conveniently and automatically.
CI Kim added, "scLENS solves major issues in single-cell transcriptome data analysis, substantially improving the accuracy and efficiency throughout the analysis process. This is a prime example of how fundamental mathematical theories can drive innovation in life sciences research, allowing researchers to more quickly and accurately answer biological questions and uncover secrets of life that were previously hidden."
This research was published in the international journal 'Nature Communications' on April 27.
Terminology
* Single-cell RNA sequencing (scRNA-seq): A technique used to measure gene expression levels in individual cells, providing insights into cell heterogeneity and rare cell types.
* Dimensionality reduction: A method to reduce the number of features or variables in a dataset while preserving the most important information, making data analysis more manageable and interpretable.
* Random matrix theory: A mathematical framework used to model and analyze the properties of large, random matrices, which can be applied to filter out noise in high-dimensional data.
* Signal robustness test: Among the signals, this test selects signals that are robust to the slight perturbation in data because real biological signals should be invariant for such slight modification in the data.
2024.05.09 View 2715 -
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 3939 -
KAIST-UCSD researchers build an enzyme discovering AI
- A joint research team led by Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering and Bernhard Palsson of UCSD developed ‘DeepECtransformer’, an artificial intelligence that can predict Enzyme Commission (EC) number of proteins.
- The AI is tasked to discover new enzymes that have not been discovered yet, which would allow prediction for a total of 5,360 types of Enzyme Commission (EC) numbers
- It is expected to be used in the development of microbial cell factories that produce environmentally friendly chemicals as a core technology for analyzing the metabolic network of a genome.
While E. coli is one of the most studied organisms, the function of 30% of proteins that make up E. coli has not yet been clearly revealed. For this, an artificial intelligence was used to discover 464 types of enzymes from the proteins that were unknown, and the researchers went on to verify the predictions of 3 types of proteins were successfully identified through in vitro enzyme assay.
KAIST (President Kwang-Hyung Lee) announced on the 24th that a joint research team comprised of Gi Bae Kim, Ji Yeon Kim, Dr. Jong An Lee and Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering at KAIST, and Dr. Charles J. Norsigian and Professor Bernhard O. Palsson of the Department of Bioengineering at UCSD has developed DeepECtransformer, an artificial intelligence that can predict the enzyme functions from the protein sequence, and has established a prediction system by utilizing the AI to quickly and accurately identify the enzyme function.
Enzymes are proteins that catalyze biological reactions, and identifying the function of each enzyme is essential to understanding the various chemical reactions that exist in living organisms and the metabolic characteristics of those organisms. Enzyme Commission (EC) number is an enzyme function classification system designed by the International Union of Biochemistry and Molecular Biology, and in order to understand the metabolic characteristics of various organisms, it is necessary to develop a technology that can quickly analyze enzymes and EC numbers of the enzymes present in the genome.
Various methodologies based on deep learning have been developed to analyze the features of biological sequences, including protein function prediction, but most of them have a problem of a black box, where the inference process of AI cannot be interpreted. Various prediction systems that utilize AI for enzyme function prediction have also been reported, but they do not solve this black box problem, or cannot interpret the reasoning process in fine-grained level (e.g., the level of amino acid residues in the enzyme sequence).
The joint team developed DeepECtransformer, an AI that utilizes deep learning and a protein homology analysis module to predict the enzyme function of a given protein sequence. To better understand the features of protein sequences, the transformer architecture, which is commonly used in natural language processing, was additionally used to extract important features about enzyme functions in the context of the entire protein sequence, which enabled the team to accurately predict the EC number of the enzyme. The developed DeepECtransformer can predict a total of 5360 EC numbers.
The joint team further analyzed the transformer architecture to understand the inference process of DeepECtransformer, and found that in the inference process, the AI utilizes information on catalytic active sites and/or the cofactor binding sites which are important for enzyme function. By analyzing the black box of DeepECtransformer, it was confirmed that the AI was able to identify the features that are important for enzyme function on its own during the learning process.
"By utilizing the prediction system we developed, we were able to predict the functions of enzymes that had not yet been identified and verify them experimentally," said Gi Bae Kim, the first author of the paper. "By using DeepECtransformer to identify previously unknown enzymes in living organisms, we will be able to more accurately analyze various facets involved in the metabolic processes of organisms, such as the enzymes needed to biosynthesize various useful compounds or the enzymes needed to biodegrade plastics." he added.
"DeepECtransformer, which quickly and accurately predicts enzyme functions, is a key technology in functional genomics, enabling us to analyze the function of entire enzymes at the systems level," said Professor Sang Yup Lee. He added, “We will be able to use it to develop eco-friendly microbial factories based on comprehensive genome-scale metabolic models, potentially minimizing missing information of metabolism.”
The joint team’s work on DeepECtransformer is described in the paper titled "Functional annotation of enzyme-encoding genes using deep learning with transformer layers" written by Gi Bae Kim, Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering of KAIST and their colleagues. The paper was published via peer-review on the 14th of November on “Nature Communications”.
This research was conducted with the support by “the Development of next-generation biorefinery platform technologies for leading bio-based chemicals industry project (2022M3J5A1056072)” and by “Development of platform technologies of microbial cell factories for the next-generation biorefineries project (2022M3J5A1056117)” from National Research Foundation supported by the Korean Ministry of Science and ICT (Project Leader: Distinguished Professor Sang Yup Lee, KAIST).
< Figure 1. The structure of DeepECtransformer's artificial neural network >
2023.11.24 View 3169 -
A KAIST research team develops a washable, transparent, and flexible OLED with MXene nanotechnology
Transparent and flexible displays, which have received a lot of attention in various fields including automobile displays, bio-healthcare, military, and fashion, are in fact known to break easily when experiencing small deformations. To solve this problem, active research is being conducted on many transparent and flexible conductive materials such as carbon nanotubes, graphene, silver nanowires, and conductive polymers.
On June 13, a joint research team led by Professor Kyung Cheol Choi from the KAIST School of Electrical Engineering and Dr. Yonghee Lee from the National Nano Fab Center (NNFC) announced the successful development of a water-resistant, transparent, and flexible OLED using MXene nanotechnology. The material can emit and transmit light even when exposed to water.
MXene is a 2D material with high electrical conductivity and optical transmittance, and it can be produced on a large scale through solution processes. However, despite these attractive properties, MXene’s applications were limited as a long-term electrical device due to its electrical properties being degraded easily by atmospheric moisture and water. The material was therefore unable to be systemized into the form of a matrix that can display information.
Professor Choi’s research team used an encapsulation tactic that can protect materials from oxidation caused by moisture and oxygen to develop a MXene-based OLED with a long lifespan and high stability against external environmental factors. The research team first focused on analyzing the degradation mechanism of MXene’s electrical conductivity, and then concentrated on designing an encapsulation membrane. The team blocked moisture and provided flexibility through residual stress offset, ultimately producing a double-layered encapsulation membrane. In addition, a thin plastic film with a thickness of a few micrometers was attached to the top layer to allow washing in water without degradation.
< Figure 1. (a) Transparent passive-matrix display made of MXene-based OLED, (b) Cross-sectional image of MXene-based OLED observed by transmission electron microscope (TEM), (c) Electro-optical characteristic graph of red, green, and blue MXene-based OLED >
Through this study, the research team developed a MXene-based red(R)/green(G)/blue(B) OLED that emits a brightness of over 1,000 cd/m2 that is detectable by the naked eye even under sunlight, thereby meeting the conditions for outdoor displays. As for the red MXene-based OLED, the researchers confirmed a standby storage life of 2,000 hours (under 70% luminescence), a standby operation life of 1,500 hours (under 60% luminescence), and a flexibility withstanding 1,000 cycles under a low curvature of under 1.5mm. In addition, they showed that its performance was maintained even after six hours of immersion under water (under 80% luminescence). Furthermore, a patterning technique was used to produce the MXene-based OLED in the form of a passive matrix, and the team demonstrated its use as a transparent display by displaying letters and shapes.
Ph.D. candidate So Yeong Jeong, who led this study, said, “To improve the reliability of MXene OLED, we focused on producing an adequate encapsulation structure and a suitable process design.” She added, “By producing a matrix-type MXene OLED and displaying simple letters and shapes, we have laid the foundations for MXene’s application in the field of transparent displays.”
< Image 1. Cover of ACS Nano Front Cover (Conceptual diagram of MXene-based OLED display) >
Professor Choi said, “This research will become the guideline for applying MXene in electrical devices, but we expect for it to also be applied in other fields that require flexible and transparent displays like automobiles, fashion, and functional clothing. And to widen the gap with China’s OLED technology, these new OLED convergence technologies must continue to be developed.”
This research was supported by the National Research Foundation of Korea and funded by the Ministry of Science and ICT, Korea. It was published as a front cover story of ACS Nano under the title, “Highly Air-Stable, Flexible, and Water-Resistive 2D Titanium Carbide MXene-Based RGB Organic Light-Emitting Diode Displays for Transparent Free-Form Electronics” on June 13.
2023.07.10 View 4016 -
A KAIST research team unveils new path for dense photonic integration
Integrated optical semiconductor (hereinafter referred to as optical semiconductor) technology is a next-generation semiconductor technology for which many researches and investments are being made worldwide because it can make complex optical systems such as LiDAR and quantum sensors and computers into a single small chip. In the existing semiconductor technology, the key was how small it was to make it in units of 5 nanometers or 2 nanometers, but increasing the degree of integration in optical semiconductor devices can be said to be a key technology that determines performance, price, and energy efficiency.
KAIST (President Kwang-Hyung Lee) announced on the 19th that a research team led by Professor Sangsik Kim of the Department of Electrical and Electronic Engineering discovered a new optical coupling mechanism that can increase the degree of integration of optical semiconductor devices by more than 100 times.
The degree of the number of elements that can be configured per chip is called the degree of integration. However, it is very difficult to increase the degree of integration of optical semiconductor devices, because crosstalk occurs between photons between adjacent devices due to the wave nature of light.
In previous studies, it was possible to reduce crosstalk of light only in specific polarizations, but in this study, the research team developed a method to increase the degree of integration even under polarization conditions, which were previously considered impossible, by discovering a new light coupling mechanism.
This study, led by Professor Sangsik Kim as a corresponding author and conducted with students he taught at Texas Tech University, was published in the international journal 'Light: Science & Applications' [IF=20.257] on June 2nd. done. (Paper title: Anisotropic leaky-like perturbation with subwavelength gratings enables zero crosstalk).
Professor Sangsik Kim said, "The interesting thing about this study is that it paradoxically eliminated the confusion through leaky waves (light tends to spread sideways), which was previously thought to increase the crosstalk." He went on to add, “If the optical coupling method using the leaky wave revealed in this study is applied, it will be possible to develop various optical semiconductor devices that are smaller and that has less noise.”
Professor Sangsik Kim is a researcher recognized for his expertise and research in optical semiconductor integration. Through his previous research, he developed an all-dielectric metamaterial that can control the degree of light spreading laterally by patterning a semiconductor structure at a size smaller than the wavelength, and proved this through experiments to improve the degree of integration of optical semiconductors. These studies were reported in ‘Nature Communications’ (Vol. 9, Article 1893, 2018) and ‘Optica’ (Vol. 7, pp. 881-887, 2020). In recognition of these achievements, Professor Kim has received the NSF Career Award from the National Science Foundation (NSF) and the Young Scientist Award from the Association of Korean-American Scientists and Engineers.
Meanwhile, this research was carried out with the support from the New Research Project of Excellence of the National Research Foundation of Korea and and the National Science Foundation of the US.
< Figure 1. Illustration depicting light propagation without crosstalk in the waveguide array of the developed metamaterial-based optical semiconductor >
2023.06.21 View 4562 -
'Jumping Genes' Found to Alter Human Colon Genomes, Offering Insights into Aging and Tumorigenesis
The Korea Advanced Institute of Science and Technology (KAIST) and their collaborators have conducted a groundbreaking study targeting 'jumping genes' in the entire genomes of the human large intestine. Published in Nature on May 18 2023, the research unveils the surprising activity of 'Long interspersed nuclear element-1 (L1),' a type of jumping gene previously thought to be mostly dormant in human genomes. The study shows that L1 genes can become activated and disrupt genomic functions throughout an individual's lifetime, particularly in the colorectal epithelium.
(Paper Title: Widespread somatic L1 retrotransposition in normal colorectal epithelium, https://www.nature.com/articles/s41586-023-06046-z)
With approximately 500,000 L1 jumping genes, accounting for 17% of the human genome, they have long been recognized for their contribution to the evolution of the human species by introducing 'disruptive innovation' to genome sequences. Until now, it was believed that most L1 elements had lost their ability to jump in normal tissues of modern humans. However, this study reveals that some L1 jumping genes can be widely activated in normal cells, leading to the accumulation of genomic mutations over an individual's lifetime. The rate of L1 jumping and resulting genomic changes vary among different cell types, with a notable concentration observed in aged colon epithelial cells. The study illustrates that every colonic epithelial cell experiences an L1 jumping event by the age of 40 on average.
The research, led by co-first authors Chang Hyun Nam (a graduate student at KAIST) and Dr. Jeonghwan Youk (former graduate student at KAIST and assistant clinical professor at Seoul National University Hospital), involved the analysis of whole-genome sequences from 899 single cells obtained from skin (fibroblasts), blood, and colon epithelial tissues collected from 28 individuals. The study uncovers the activation of L1 jumping genes in normal cells, resulting in the gradual accumulation of genomic mutations over time. Additionally, the team explored epigenomic (DNA methylation) sequences to understand the mechanism behind L1 jumping gene activation. They found that cells with activated L1 jumping genes exhibit epigenetic instability, suggesting the critical role of epigenetic changes in regulating L1 jumping gene activity. Most of these epigenomic instabilities were found to arise during the early stages of embryogenesis. The study provides valuable insights into the aging process and the development of diseases in human colorectal tissues.
"This study illustrates that genomic damage in normal cells is acquired not only through exposure to carcinogens but also through the activity of endogenous components whose impact was previously unclear. Genomes of apparently healthy aged cells, particularly in the colorectal epithelium, become mosaic due to the activity of L1 jumping genes," said Prof. Young Seok Ju at KAIST.
"We emphasize the essential and ongoing collaboration among researchers in clinical medicine and basic medical sciences," said Prof. Min Jung Kim of the Department of Surgery at Seoul National University Hospital. "This case highlights the critical role of systematically collected human tissues from clinical settings in unraveling the complex process of disease development in humans."
"I am delighted that the research team's advancements in single-cell genome technology have come to fruition. We will persistently strive to lead in single-cell genome technology," said Prof. Hyun Woo Kwon of the Department of Nuclear Medicine at Korea University School of Medicine.
The research team received support from the Research Leader Program and the Young Researcher Program of the National Research Foundation of Korea, a grant from the MD-PhD/Medical Scientist Training Program through the Korea Health Industry Development Institute, and the Suh Kyungbae Foundation.
< Figure 1. Experimental design of the study >
< Figure 2. Schematic diagram illustrating factors influencing the soL1R landscape. >
Genetic composition of rc-L1s is inherited from the parents. The methylation landscape of rc-L1 promoters is predominantly determined by global DNA demethylation, followed by remethylation processes in the developmental stages. Then, when an rc-L1 is promoter demethylated in a specific cell lineage, the source expresses L1 transcripts thus making possible the induction of soL1Rs.
2023.05.22 View 4615 -
Using light to throw and catch atoms to open up a new chapter for quantum computing
The technology to move and arrange atoms, the most basic component of a quantum computer, is very important to Rydberg quantum computing research. However, to place the atoms at the desired location, the atoms must be captured and transported one by one using a highly focused laser beam, commonly referred to as an optical tweezer. and, the quantum information of the atoms is likely to change midway.
KAIST (President Kwang Hyung Lee) announced on the 27th that a research team led by Professor Jaewook Ahn of the Department of Physics developed a technology to throw and receive rubidium atoms one by one using a laser beam.
The research team developed a method to throw and receive atoms which would minimize the time the optical tweezers are in contact with the atoms in which the quantum information the atoms carry may change. The research team used the characteristic that the rubidium atoms, which are kept at a very low temperature of 40μK below absolute zero, move very sensitively to the electromagnetic force applied by light along the focal point of the light tweezers.
The research team accelerated the laser of an optical tweezer to give an optical kick to an atom to send it to a target, then caught the flying atom with another optical tweezer to stop it. The atom flew at a speed of 65 cm/s, and traveled up to 4.2 μm. Compared to the existing technique of guiding the atoms with the optical tweezers, the technique of throwing and receiving atoms eliminates the need to calculate the transporting path for the tweezers, and makes it easier to fix the defects in the atomic arrangement. As a result, it is effective in generating and maintaining a large number of atomic arrangements, and when the technology is used to throw and receive flying atom qubits, it will be used in studying new and more powerful quantum computing methods that presupposes the structural changes in quantum arrangements.
"This technology will be used to develop larger and more powerful Rydberg quantum computers," says Professor Jaewook Ahn. “In a Rydberg quantum computer,” he continues, “atoms are arranged to store quantum information and interact with neighboring atoms through electromagnetic forces to perform quantum computing. The method of throwing an atom away for quick reconstruction the quantum array can be an effective way to fix an error in a quantum computer that requires a removal or replacement of an atom.”
The research, which was conducted by doctoral students Hansub Hwang and Andrew Byun of the Department of Physics at KAIST and Sylvain de Léséleuc, a researcher at the National Institute of Natural Sciences in Japan, was published in the international journal, Optica, 0n March 9th. (Paper title: Optical tweezers throw and catch single atoms).
This research was carried out with the support of the Samsung Science & Technology Foundation.
<Figure 1> A schematic diagram of the atom catching and throwing technique. The optical tweezer on the left kicks the atom to throw it into a trajectory to have the tweezer on the right catch it to stop it.
2023.03.28 View 4213 -
KAIST team develops smart immune system that can pin down on malignant tumors
A joint research team led by Professor Jung Kyoon Choi of the KAIST Department of Bio and Brain Engineering and Professor Jong-Eun Park of the KAIST Graduate School of Medical Science and Engineering (GSMSE) announced the development of the key technologies to treat cancers using smart immune cells designed based on AI and big data analysis. This technology is expected to be a next-generation immunotherapy that allows precision targeting of tumor cells by having the chimeric antigen receptors (CARs) operate through a logical circuit. Professor Hee Jung An of CHA Bundang Medical Center and Professor Hae-Ock Lee of the Catholic University of Korea also participated in this research to contribute joint effort.
Professor Jung Kyoon Choi’s team built a gene expression database from millions of cells, and used this to successfully develop and verify a deep-learning algorithm that could detect the differences in gene expression patterns between tumor cells and normal cells through a logical circuit. CAR immune cells that were fitted with the logic circuits discovered through this methodology could distinguish between tumorous and normal cells as a computer would, and therefore showed potentials to strike only on tumor cells accurately without causing unwanted side effects.
This research, conducted by co-first authors Dr. Joonha Kwon of the KAIST Department of Bio and Brain Engineering and Ph.D. candidate Junho Kang of KAIST GSMSE, was published by Nature Biotechnology on February 16, under the title Single-cell mapping of combinatorial target antigens for CAR switches using logic gates.
An area in cancer research where the most attempts and advances have been made in recent years is immunotherapy. This field of treatment, which utilizes the patient’s own immune system in order to overcome cancer, has several methods including immune checkpoint inhibitors, cancer vaccines and cellular treatments. Immune cells like CAR-T or CAR-NK equipped with chimera antigen receptors, in particular, can recognize cancer antigens and directly destroy cancer cells.
Starting with its success in blood cancer treatment, scientists have been trying to expand the application of CAR cell therapy to treat solid cancer. But there have been difficulties to develop CAR cells with effective killing abilities against solid cancer cells with minimized side effects. Accordingly, in recent years, the development of smarter CAR engineering technologies, i.e., computational logic gates such as AND, OR, and NOT, to effectively target cancer cells has been underway.
At this point in time, the research team built a large-scale database for cancer and normal cells to discover the exact genes that are expressed only from cancer cells at a single-cell level. The team followed this up by developing an AI algorithm that could search for a combination of genes that best distinguishes cancer cells from normal cells. This algorithm, in particular, has been used to find a logic circuit that can specifically target cancer cells through cell-level simulations of all gene combinations. CAR-T cells equipped with logic circuits discovered through this methodology are expected to distinguish cancerous cells from normal cells like computers, thereby minimizing side effects and maximizing the effects of chemotherapy.
Dr. Joonha Kwon, who is the first author of this paper, said, “this research suggests a new method that hasn’t been tried before. What’s particularly noteworthy is the process in which we found the optimal CAR cell circuit through simulations of millions of individual tumors and normal cells.” He added, “This is an innovative technology that can apply AI and computer logic circuits to immune cell engineering. It would contribute greatly to expanding CAR therapy, which is being successfully used for blood cancer, to solid cancers as well.”
This research was funded by the Original Technology Development Project and Research Program for Next Generation Applied Omic of the Korea Research Foundation.
Figure 1. A schematic diagram of manufacturing and administration process of CAR therapy and of cancer cell-specific dual targeting using CAR.
Figure 2. Deep learning (convolutional neural networks, CNNs) algorithm for selection of dual targets based on gene combination (left) and algorithm for calculating expressing cell fractions by gene combination according to logical circuit (right).
2023.03.09 View 6119 -
KAIST presents a fundamental technology to remove metastatic traits from lung cancer cells
KAIST (President Kwang Hyung Lee) announced on January 30th that a research team led by Professor Kwang-Hyun Cho from the Department of Bio and Brain Engineering succeeded in using systems biology research to change the properties of carcinogenic cells in the lungs and eliminate both drug resistance and their ability to proliferate out to other areas of the body.
As the incidences of cancer increase within aging populations, cancer has become the most lethal disease threatening healthy life. Fatality rates are especially high when early detection does not happen in time and metastasis has occurred in various organs. In order to resolve this problem, a series of attempts were made to remove or lower the ability of cancer cells to spread, but they resulted in cancer cells in the intermediate state becoming more unstable and even more malignant, which created serious treatment challenges.
Professor Kwang-Hyun Cho's research team simulated various cancer cell states in the Epithelial-to-Mesenchymal Transition (EMT) of lung cancer cells, between epithelial cells without metastatic ability and mesenchymal cells with metastatic ability. A mathematical model of molecular network was established, and key regulators that could reverse the state of invasive and drug resistant mesenchymal cells back to the epithelial state were discovered through computer simulation analysis and molecular cell experiments. In particular, this process succeeded in properly reverting the mesenchymal lung cancer cells to a state where they were sensitive to chemotherapy treatment while avoiding the unstable EMT hybrid cell state in the middle process, which had remained a difficult problem.
The results of this research, in which KAIST Ph.D. student Namhee Kim, Dr. Chae Young Hwang, Researcher Taeyoung Kim, and Ph.D. student Hyunjin Kim participated, were published as an online paper in the international journal “Cancer Research” published by the American Association for Cancer Research (AACR) on January 30th. (Paper title: A cell fate reprogramming strategy reverses epithelial-to-mesenchymal transition of lung cancer cells while avoiding hybrid states)
Cells in an EMT hybrid state, which are caused by incomplete transitions during the EMT process in cancer cells, have the characteristics of both epithelial cells and mesenchymal cells, and are known to have high drug resistance and metastatic potential by acquiring high stem cell capacity. In particular, EMT is further enhanced through factors such as transforming growth factor-beta (TGF-β) secreted from the tumor microenvironment (TME) and, as a result, various cell states with high plasticity appear. Due to the complexity of EMT, it has been very difficult to completely reverse the transitional process of the mesenchymal cancer cells to an epithelial cell state in which metastatic ability and drug resistance are eliminated while avoiding the EMT hybrid cell state with high metastatic ability and drug resistance.
Professor Kwang-Hyun Cho's research team established a mathematical model of the gene regulation network that governs the complex process of EMT, and then applied large-scale computer simulation analysis and complex system network control technology to identify and verify 'p53', 'SMAD4', and 'ERK1' and 'ERK 2' (collectively ERKs) through molecular cell experiments as the three key molecular targets that can transform lung cancer cells in the mesenchymal cell state, reversed back to an epithelial cell state that no longer demonstrates the ability to metastasize, while avoiding the EMT hybrid cell state.
In particular, by analyzing the molecular regulatory mechanism of the complex EMT process at the system level, the key pathways were identified that were linked to the positive feedback that plays an important role in completely returning cancer cells to an epithelial cell state in which metastatic ability and drug resistance are removed.
This discovery is significant in that it proved that mesenchymal cells can be reverted to the state of epithelial cells under conditions where TGF-β stimulation are present, like they are in the actual environment where cancer tissue forms in the human body.
Abnormal EMT in cancer cells leads to various malignant traits such as the migration and invasion of cancer cells, changes in responsiveness to chemotherapy treatment, enhanced stem cell function, and the dissemination of cancer. In particular, the acquisition of the metastatic ability of cancer cells is a key determinant factor for the prognosis of cancer patients. The EMT reversal technology in lung cancer cells developed in this research is a new anti-cancer treatment strategy that reprograms cancer cells to eliminate their high plasticity and metastatic potential and increase their responsiveness to chemotherapy.
Professor Kwang-Hyun Cho said, "By succeeding in reversing the state of lung cancer cells that acquired high metastatic traits and resistance to drugs and reverting them to a treatable epithelial cell state with renewed sensitivity to chemotherapy, the research findings propose a new strategy for treatments that can improve the prognosis of cancer patients.”
Professor Kwang-Hyun Cho's research team was the first to present the principle of reversal treatment to revert cancer cells to normal cells, following through with the announcement of the results of their study that reverted colon cancer cells to normal colon cells in January of 2020, and also presenting successful re-programming research where the most malignant basal type breast cancer cells turned into less-malignant luminal type breast cancer cells that were treatable with hormonal therapies in January of 2022. This latest research result is the third in the development of reversal technology where lung cancer cells that had acquired metastatic traits returned to a state in which their metastatic ability was removed and drug sensitivity was enhanced.
This research was carried out with support from the Ministry of Science and ICT and the National Research Foundation of Korea's Basic Research in Science & Engineering Program for Mid-Career Researchers.
< Figure 1. Construction of the mathematical model of the regulatory network to represent the EMT phenotype based on the interaction between various molecules related to EMT.
(A) Professor Kwang-Hyun Cho's research team investigated numerous literatures and databases related to complex EMT, and based on comparative analysis of cell line data showing epithelial and mesenchymal cell conditions, they extracted key signaling pathways related to EMT and built a mathematical model of regulatory network (B) By comparing the results of computer simulation analysis and the molecular cell experiments, it was verified how well the constructed mathematical model simulated the actual cellular phenomena. >
< Figure 2. Understanding of various EMT phenotypes through large-scale computer simulation analysis and complex system network control technology.
(A) Through computer simulation analysis and experiments, Professor Kwang-Hyun Cho's research team found that complete control of EMT is impossible with single-molecule control alone. In particular, through comparison of the relative stability of attractors, it was revealed that the cell state exhibiting EMT hybrid characteristics has unstable properties. (B), (C) Based on these results, Prof. Cho’s team identified two feedbacks (positive feedback consisting of Snail-miR-34 and ZEB1-miR-200) that play an important role in avoiding the EMT hybrid state that appeared in the TGF-β-ON state. It was found through computer simulation analysis that the two feedbacks restore relatively high stability when the excavated p53 and SMAD4 are regulated. In addition, molecular cell experiments demonstrated that the expression levels of E-cad and ZEB1, which are representative phenotypic markers of EMT, changed similarly to the expression profile in the epithelial cell state, despite the TGF-β-ON state. >
< Figure 3. Complex molecular network analysis and discovery of reprogramming molecular targets for intact elimination of EMT hybrid features.
(A) Controlling the expression of p53 and SMAD4 in lung cancer cell lines was expected to overcome drug resistance, but contrary to expectations, chemotherapy responsiveness was not restored. (B) Professor Kwang-Hyun Cho's research team additionally analyzed computer simulations, genome data, and experimental results and found that high expression levels of TWIST1 and EPCAM were related to drug resistance. (C) Prof. Cho’s team identified three key molecular targets: p53, SMAD4 and ERK1 & ERK2. (D), (E) Furthermore, they identified a key pathway that plays an important role in completely reversing into epithelial cells while avoiding EMT hybrid characteristics, and confirmed through network analysis and attractor analysis that high stability of the key pathway was restored when the proposed molecular target was controlled. >
< Figure 4. Verification through experiments with lung cancer cell lines.
When p53 was activated and SMAD4 and ERK1/2 were inhibited in lung cancer cell lines, (A), (B) E-cad protein expression increased and ZEB1 protein expression decreased, and (C) mesenchymal cell status including TWIST1 and EPCAM and gene expression of markers related to stem cell potential characteristics were completely inhibited. In addition, (D) it was confirmed that resistance to chemotherapy treatment was also overcome as the cell state was reversed by the regulated target. >
< Figure 5. A schematic representation of the research results.
Prof. Cho’s research team identified key molecular regulatory pathways to avoid high plasticity formed by abnormal EMT of cancer cells and reverse it to an epithelial cell state through systems biology research. From this analysis, a reprogramming molecular target that can reverse the state of mesenchymal cells with acquired invasiveness and drug resistance to the state of epithelial cells with restored drug responsiveness was discovered.
For lung cancer cells, when a drug that enhances the expression of p53, one of the molecular targets discovered, and inhibits the expression of SMAD4 and ERK1 & ERK2 is administered, the molecular network of genes in the state of mesenchymal cells is modified, eventually eliminating metastatic ability and it is reprogrammed to turn into epithelial cells without the resistance to chemotherapy treatments. >
2023.01.30 View 11840 -
KAIST Offers Hope to Musicians with Dystonia
< Photo 1. Conductor and Pianist João Carlos Martins before the Recital at the Carnegie Hall preparing with his bionic gloves >
KAIST’s neuroscientist and professor, Dr. Daesoo Kim attended the “Conference for Musicians with Dystonia” supported by the World Health Organization (WHO) and the Carnegie Hall concert of legendary pianist João Carlos Martins, who is also a dystonia patient, to announce his team’s recent advancements toward finding a cure for dystonia.
On November 19, 2022, a “miracle concert” was held in Carnegie Hall. João Carlos Martins was a renowned world-class pianist in the 70s and 80s, but he had to put an end to his musical career due to focal dystonia in his fingers. But in 2020, he began using a bionic glove developed by industrial designer Ubiratã Bizarro Costa and after years of hard work he was back in Carnegie Hall as an 82-year-old man.
During the concert, he conducted the NOVUS NY orchestra in a performance of Bach, and later even played the piano himself. In particular, between his performances, he gave shout-outs to scientists studying dystonia including KAIST Professor Daesoo Kim, asking them to continue working towards curing rare diseases for musicians.
< Photo 2. Professor Daesoo Kim with Conductor and Pianist João Carlos Martins >
Musician’s dystonia affects 1-3% of musicians around the world and musicians make up approximately 5% of the total number of dystonia patients. Musicians who are no longer able to practice music due to the disease often experience stress and depression, which may even lead to suicide in extreme cases. Musicians are known to be particularly prone to such diseases due to excessive practice regimens, perfectionism, and even genetics. Currently, botulinum toxin (Botox) is used to suppress abnormal muscles, but muscle function suppression ultimately means that the musician is no longer able to play the instrument. João Carlos Martins himself underwent several Botox procedures and three brain surgeries, but saw no therapeutic results. This is why a new treatment was necessary.
Professor Daesoo Kim’s research team at KAIST took note of the fact that abnormal muscle tension is caused by excessive stress, and developed NT-1, a treatment that blocks the development of the symptoms of dystonia from the brain, allowing patients to use their muscles as they normally would. The research team published their findings in Science Advances in 2021, and João Carlos Martins invited Professor Daesoo Kim to the UN conference and his concert after reading this paper.
< Photo 3. Professor Daesoo Kim (3rd from the left) photographed with other guests at the recital including Dr. Dévora Kestel, the Director of the Mental Health and Substance Use at WHO, sharing the center with Conductor and Pianist João Carlos Martins >
During the UN conference held the day prior to the Carnegie Hall concert, Dr. Dévora Kestel, Director of the Mental Health and Substance Use at WHO, said, “Although dystonia is not as well-known, it is a common disease around the world, and needs our society’s attention and the devotion of many researchers.” Professor Daesoo Kim said, “NT-1 is a drug that blocks the cause of dystonia in the brain, and will allow musicians to continue practicing music. We aim to attain clinical approval in Korea by 2024.”
NT-1 is currently under development by NeuroTobe, a faculty-led start-up company at KAIST, headed by Professor Daesoo Kim as the CEO. The synthesis of the drug for clinical testing has been successfully completed, and it has shown excellent efficacy and safety through various rounds of animal testing. Unlike Botox, which takes a few days to show its therapeutic effects after receiving the procedure from a hospital, NT-1 shows its therapeutic effects within an hour after taking it. As a so-called “edible Botox”, it is expected to help treat various muscular diseases and ailments.
2022.12.27 View 8368