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KAIST succeeds in controlling complex altered gene networks to restore them to normal
Previously, research on controlling gene networks has been carried out based on a single stimulus-response of cells. More recently, studies have been proposed to precisely analyze complex gene networks to identify control targets. A KAIST research team has succeeded in developing a universal technology that identifies gene control targets in altered cellular gene networks and restores them. This achievement is expected to be widely applied to new anticancer therapies such as cancer reversibility, drug development, precision medicine, and reprogramming for cell therapy.
KAIST (President Kwang Hyung Lee) announced on the 28th of August that Professor Kwang-Hyun Cho’s research team from the Department of Bio and Brain Engineering has developed a technology to systematically identify gene control targets that can restore the altered stimulus-response patterns of cells to normal by using an algebraic approach. The algebraic approach expresses gene networks as mathematical equations and identifies control targets through algebraic computations.
The research team represented the complex interactions among genes within a cell as a "logic circuit diagram" (Boolean network). Based on this, they visualized how a cell responds to external stimuli as a "landscape map" (phenotype landscape).
By applying a mathematical method called the "semi-tensor product,*" they developed a way to quickly and accurately calculate how the overall cellular response would change if a specific gene were controlled.
*Semi-tensor product: a method that calculates all possible gene combinations and control effects in a single algebraic formula
However, because the key genes that determine actual cellular responses number in the thousands, the calculations are extremely complex. To address this, the research team applied a numerical approximation method (Taylor approximation) to simplify the calculations. In simple terms, they transformed a complex problem into a simpler formula while still yielding nearly identical results.
Through this, the team was able to calculate which stable state (attractor) a cell would reach and predict how the cell’s state would change when a particular gene was controlled. As a result, they were able to identify core gene control targets that could restore abnormal cellular responses to states most similar to normal.
Professor Cho’s team applied the developed control technology to various gene networks and verified that it can accurately predict gene control targets that restore altered stimulus-response patterns of cells back to normal.
In particular, by applying it to bladder cancer cell networks, they identified gene control targets capable of restoring altered responses to normal. They also discovered gene control targets in large-scale distorted gene networks during immune cell differentiation that are capable of restoring normal stimulus-response patterns. This enabled them to solve problems that previously required only approximate searches through lengthy computer simulations in a fast and systematic way.
Professor Cho said, “This study is evaluated as a core original technology for the development of the Digital Cell Twin model*, which analyzes and controls the phenotype landscape of gene networks that determine cell fate. In the future, it is expected to be widely applicable across the life sciences and medicine, including new anticancer therapies through cancer reversibility, drug development, precision medicine, and reprogramming for cell therapy.”
*Digital Cell Twin model: a technology that digitally models the complex reactions occurring within cells, enabling virtual simulations of cellular responses instead of actual experiments
KAIST master’s student Insoo Jung, PhD student Corbin Hopper, PhD student Seong-Hoon Jang, and PhD student Hyunsoo Yeo participated in this study. The results were published online on August 22 in Science Advances, an international journal published by the American Association for the Advancement of Science (AAAS).
※ Paper title: “Reverse Control of Biological Networks to Restore Phenotype Landscapes”
※ DOI: https://www.science.org/doi/10.1126/sciadv.adw3995
This research was supported by the Mid-Career Researcher Program and the Basic Research Laboratory Program of the National Research Foundation of Korea, funded by the Ministry of Science and ICT.
2025.08.29 View 2112 -
KAIST Accelerates Synthetic Microbe Design by Discovering Novel Enzymes Using AI
< (From left) Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering (top), Hongkeun Ji, PhD candidate of the Department of Chemical and Biomolecular Engineering (top), Ha Rim Kim, PhD candidate of the Department of Chemical and Biomolecular Engineering, and Dr. Gi Bae Kim of the BioProcess Engineering Research Center >
Enzymes are proteins that catalyze biochemical reactions within cells and play a pivotal role in metabolic processes. Accordingly, identifying the functions of novel enzymes is a critical task in the construction of microbial cell factories.
A KAIST research team has leveraged artificial intelligence (AI) to design novel enzymes that do not exist in nature, significantly accelerating microbial cell factory development and boosting the potential for next-generation biotechnological applications such as drug development and biofuel production.
KAIST (represented by President Kwang-Hyung Lee) announced on the 21st of April that Distinguished Professor Sang Yup Lee and his team from the Department of Chemical and Biomolecular Engineering have published a review titled “Enzyme Functional Classification Using Artificial Intelligence,” which outlines the advancement of AI-based enzyme function prediction technologies and analyzes how AI has contributed to the discovery and design of new enzymes.
Professor Lee’s team systematically reviewed the development of enzyme function prediction technologies utilizing machine learning and deep learning, offering a comprehensive analysis.
From sequence similarity-based prediction methods to the integration of convolutional neural networks (CNNs), recurrent neural networks (RNNs), graph neural networks (GNNs), and transformer-based large language models, the paper covers a broad range of AI applications. It analyzes how these technologies extract meaningful information from protein sequences and enhance prediction accuracy.
In particular, enzyme function prediction using deep learning goes beyond simple sequence similarity analysis. By automatically extracting structural and evolutionary features embedded in amino acid sequences, deep learning enables more precise predictions of catalytic functions.
This highlights the unique advantages of AI models compared to traditional bioinformatics approaches.
Moreover, the review suggests that the advancement of generative AI will move future research beyond predicting existing functions to generating entirely new enzymes with functions not found in nature. This shift is expected to profoundly impact the trajectory of biotechnology and synthetic biology.
< Figure 1. Extraction of enzyme characteristics and function prediction using various deep learning structures >
Ha Rim Kim, a Ph.D. candidate and co-first author from the Department of Chemical and Biomolecular Engineering, stated, “AI-based enzyme function prediction and enzyme design are highly important across various fields including metabolic engineering, synthetic biology, and healthcare.”
Distinguished Professor Sang Yup Lee added, “AI-powered enzyme function prediction shows the potential to solve diverse biological problems and will significantly contribute to accelerating research across the entire field.”
The review was published on March 28 in Trends in Biotechnology, a leading biotechnology journal issued by Cell Press.
※ Title: Enzyme Functional Classification Using Artificial Intelligence
※DOI: https://doi.org/10.1016/j.tibtech.2025.03.003
※ Author Information: Ha Rim Kim (KAIST, Co-first author), Hongkeun Ji (KAIST, Co-first author), Gi Bae Kim (KAIST, Third author), Sang Yup Lee (KAIST, Corresponding author)
This research was supported by the Ministry of Science and ICT under the project Development of Core Technologies for Advanced Synthetic Biology to Lead the Bio-Manufacturing Industry (aimed at replacing petroleum-based chemicals), and also by joint support from the Ministry of Science and ICT and the Ministry of Health and Welfare for the project Development of Novel Antibiotic Structures Using Deep Learning-Based Synthetic Biology.
2025.04.07 View 7673 -
A KAIST Research Team Identifies a Cancer Reversion Mechanism
Despite decades of intensive cancer research by numerous biomedical scientists, cancer still holds its place as the number one cause of death in Korea. The fundamental reason behind the limitations of current cancer treatment methods is the fact that they all aim to completely destroy cancer cells, which eventually allows the cancer cells to acquire immunity. In other words, recurrences and side-effects caused by the destruction of healthy cells are inevitable. To this end, some have suggested anticancer treatment methods based on cancer reversion, which can revert cancer cells back to normal or near-normal cells under certain conditions. However, the practical development of this idea has not yet been attempted.
On June 8, a KAIST research team led by Professor Kwang-Hyun Cho from the Department of Bio and Brain Engineering reported to have successfully identified the fundamental principle of a process that can revert cancer cells back to normal cells without killing the cells.
Professor Cho’s team focused on the fact that unlike normal cells, which react according to external stimuli, cancer cells tend to ignore such stimuli and only undergo uncontrolled cell division. Through computer simulation analysis, the team discovered that the input-output (I/O) relationships that were distorted by genetic mutations could be reverted back to normal I/O relationships under certain conditions. The team then demonstrated through molecular cell experiments that such I/O relationship recovery also occurred in real cancer cells.
The results of this study, written by Dr. Jae Il Joo and Dr. Hwa-Jeong Park, were published in Wiley’s Advanced Science online on June 2 under the title, "Normalizing input-output relationships of cancer networks for reversion therapy."
< Image 1. Input-output (I/O) relationships in gene regulatory networks >
Professor Kwang-Hyun Cho's research team classified genes into four types by simulation-analyzing the effect of gene mutations on the I/O relationship of gene regulatory networks. (Figure A-J) In addition, by analyzing 18 genes of the cancer-related gene regulatory network, it was confirmed that when mutations occur in more than half of the genes constituting each network, reversibility is possible through appropriate control. (Figure K)
Professor Cho’s team uncovered that the reason the distorted I/O relationships of cancer cells could be reverted back to normal ones was the robustness and redundancy of intracellular gene control networks that developed over the course of evolution. In addition, they found that some genes were more promising as targets for cancer reversion than others, and showed through molecular cell experiments that controlling such genes could revert the distorted I/O relationships of cancer cells back to normal ones.
< Image 2. Simulation results of restoration of bladder cancer gene regulation network and I/O relationship of bladder cancer cells. >
The research team classified the effects of gene mutations on the I/O relationship in the bladder cancer gene regulation network by simulation analysis and classified them into 4 types. (Figure A) Through this, it was found that the distorted input-output relationship between bladder cancer cell lines KU-1919 and HCT-1197 could be restored to normal. (Figure B)
< Image 3. Analysis of survival of bladder cancer patients according to reversible gene mutation and I/O recovery experiment of bladder cancer cells. >
As predicted through network simulation analysis, Professor Kwang-Hyun Cho's research team confirmed through molecular cell experiments that the response to TGF-b was normally restored when AKT and MAP3K1 were inhibited in the bladder cancer cell line KU-1919. (Figure A-G) In addition, it was confirmed that there is a difference in the survival rate of bladder cancer patients depending on the presence or absence of a reversible gene mutation. (Figure H)
The results of this research show that the reversion of real cancer cells does not happen by chance, and that it is possible to systematically explore targets that can induce this phenomenon, thereby creating the potential for the development of innovative anticancer drugs that can control such target genes.
< Image 4. Cancer cell reversibility principle >
The research team analyzed the reversibility, redundancy, and robustness of various networks and found that there was a positive correlation between them. From this, it was found that reversibility was additionally inherent in the process of evolution in which the gene regulatory network acquired redundancy and consistency.
Professor Cho said, “By uncovering the fundamental principles of a new cancer reversion treatment strategy that may overcome the unresolved limitations of existing chemotherapy, we have increased the possibility of developing new and innovative drugs that can improve both the prognosis and quality of life of cancer patients.”
< Image 5. Conceptual diagram of research results >
The research team identified the fundamental control principle of cancer cell reversibility through systems biology research. When the I/O relationship of the intracellular gene regulatory network is distorted by mutation, the distorted I/O relationship can be restored to a normal state by identifying and adjusting the reversible gene target based on the redundancy of the molecular circuit inherent in the complex network.
After Professor Cho’s team first suggested the concept of reversion treatment, they published their results for reverting colorectal cancer in January 2020, and in January 2022 they successfully re-programmed malignant breast cancer cells back into hormone-treatable ones. In January 2023, the team successfully removed the metastasis ability from lung cancer cells and reverted them back to a state that allowed improved drug reactivity. However, these results were case studies of specific types of cancer and did not reveal what common principle allowed cancer reversion across all cancer types, making this the first revelation of the general principle of cancer reversion and its evolutionary origins.
This research was funded by the Ministry of Science and ICT of the Republic of Korea and the National Research Foundation of Korea.
2023.06.20 View 16016 -
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 16318 -
Decoding Brain Signals to Control a Robotic Arm
Advanced brain-machine interface system successfully interprets arm movement directions from neural signals in the brain
Researchers have developed a mind-reading system for decoding neural signals from the brain during arm movement. The method, described in the journal Applied Soft Computing, can be used by a person to control a robotic arm through a brain-machine interface (BMI).
A BMI is a device that translates nerve signals into commands to control a machine, such as a computer or a robotic limb. There are two main techniques for monitoring neural signals in BMIs: electroencephalography (EEG) and electrocorticography (ECoG).
The EEG exhibits signals from electrodes on the surface of the scalp and is widely employed because it is non-invasive, relatively cheap, safe and easy to use. However, the EEG has low spatial resolution and detects irrelevant neural signals, which makes it difficult to interpret the intentions of individuals from the EEG.
On the other hand, the ECoG is an invasive method that involves placing electrodes directly on the surface of the cerebral cortex below the scalp. Compared with the EEG, the ECoG can monitor neural signals with much higher spatial resolution and less background noise. However, this technique has several drawbacks.
“The ECoG is primarily used to find potential sources of epileptic seizures, meaning the electrodes are placed in different locations for different patients and may not be in the optimal regions of the brain for detecting sensory and movement signals,” explained Professor Jaeseung Jeong, a brain scientist at KAIST. “This inconsistency makes it difficult to decode brain signals to predict movements.”
To overcome these problems, Professor Jeong’s team developed a new method for decoding ECoG neural signals during arm movement. The system is based on a machine-learning system for analysing and predicting neural signals called an ‘echo-state network’ and a mathematical probability model called the Gaussian distribution.
In the study, the researchers recorded ECoG signals from four individuals with epilepsy while they were performing a reach-and-grasp task. Because the ECoG electrodes were placed according to the potential sources of each patient’s epileptic seizures, only 22% to 44% of the electrodes were located in the regions of the brain responsible for controlling movement.
During the movement task, the participants were given visual cues, either by placing a real tennis ball in front of them, or via a virtual reality headset showing a clip of a human arm reaching forward in first-person view. They were asked to reach forward, grasp an object, then return their hand and release the object, while wearing motion sensors on their wrists and fingers. In a second task, they were instructed to imagine reaching forward without moving their arms.
The researchers monitored the signals from the ECoG electrodes during real and imaginary arm movements, and tested whether the new system could predict the direction of this movement from the neural signals. They found that the novel decoder successfully classified arm movements in 24 directions in three-dimensional space, both in the real and virtual tasks, and that the results were at least five times more accurate than chance. They also used a computer simulation to show that the novel ECoG decoder could control the movements of a robotic arm.
Overall, the results suggest that the new machine learning-based BCI system successfully used ECoG signals to interpret the direction of the intended movements. The next steps will be to improve the accuracy and efficiency of the decoder. In the future, it could be used in a real-time BMI device to help people with movement or sensory impairments.
This research was supported by the KAIST Global Singularity Research Program of 2021, Brain Research Program of the National Research Foundation of Korea funded by the Ministry of Science, ICT, and Future Planning, and the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education.
-PublicationHoon-Hee Kim, Jaeseung Jeong, “An electrocorticographic decoder for arm movement for brain-machine interface using an echo state network and Gaussian readout,” Applied SoftComputing online December 31, 2021 (doi.org/10.1016/j.asoc.2021.108393)
-ProfileProfessor Jaeseung JeongDepartment of Bio and Brain EngineeringCollege of EngineeringKAIST
2022.03.18 View 17516 -
KAIST ISPI Releases Report on the Global AI Innovation Landscape
Providing key insights for building a successful AI ecosystem
The KAIST Innovation Strategy and Policy Institute (ISPI) has launched a report on the global innovation landscape of artificial intelligence in collaboration with Clarivate Plc. The report shows that AI has become a key technology and that cross-industry learning is an important AI innovation. It also stresses that the quality of innovation, not volume, is a critical success factor in technological competitiveness.
Key findings of the report include:
• Neural networks and machine learning have been unrivaled in terms of scale and growth (more than 46%), and most other AI technologies show a growth rate of more than 20%.
• Although Mainland China has shown the highest growth rate in terms of AI inventions, the influence of Chinese AI is relatively low. In contrast, the United States holds a leading position in AI-related inventions in terms of both quantity and influence.
• The U.S. and Canada have built an industry-oriented AI technology development ecosystem through organic cooperation with both academia and the Government. Mainland China and South Korea, by contrast, have a government-driven AI technology development ecosystem with relatively low qualitative outputs from the sector.
• The U.S., the U.K., and Canada have a relatively high proportion of inventions in robotics and autonomous control, whereas in Mainland China and South Korea, machine learning and neural networks are making progress. Each country/region produces high-quality inventions in their predominant AI fields, while the U.S. has produced high-impact inventions in almost all AI fields.
“The driving forces in building a sustainable AI innovation ecosystem are important national strategies. A country’s future AI capabilities will be determined by how quickly and robustly it develops its own AI ecosystem and how well it transforms the existing industry with AI technologies. Countries that build a successful AI ecosystem have the potential to accelerate growth while absorbing the AI capabilities of other countries. AI talents are already moving to countries with excellent AI ecosystems,” said Director of the ISPI Wonjoon Kim.
“AI, together with other high-tech IT technologies including big data and the Internet of Things are accelerating the digital transformation by leading an intelligent hyper-connected society and enabling the convergence of technology and business. With the rapid growth of AI innovation, AI applications are also expanding in various ways across industries and in our lives,” added Justin Kim, Special Advisor at the ISPI and a co-author of the report.
2021.12.21 View 12422 -
Scientists Develop Wireless Networks that Allow Brain Circuits to Be Controlled Remotely through the Internet
Wireless implantable devices and IoT could manipulate the brains of animals from anywhere around the world due to their minimalistic hardware, low setup cost, ease of use, and customizable versatility
A new study shows that researchers can remotely control the brain circuits of numerous animals simultaneously and independently through the internet. The scientists believe this newly developed technology can speed up brain research and various neuroscience studies to uncover basic brain functions as well as the underpinnings of various neuropsychiatric and neurological disorders.
A multidisciplinary team of researchers at KAIST, Washington University in St. Louis, and the University of Colorado, Boulder, created a wireless ecosystem with its own wireless implantable devices and Internet of Things (IoT) infrastructure to enable high-throughput neuroscience experiments over the internet. This innovative technology could enable scientists to manipulate the brains of animals from anywhere around the world. The study was published in the journal Nature Biomedical Engineering on November 25
“This novel technology is highly versatile and adaptive. It can remotely control numerous neural implants and laboratory tools in real-time or in a scheduled way without direct human interactions,” said Professor Jae-Woong Jeong of the School of Electrical Engineering at KAIST and a senior author of the study. “These wireless neural devices and equipment integrated with IoT technology have enormous potential for science and medicine.”
The wireless ecosystem only requires a mini-computer that can be purchased for under $45, which connects to the internet and communicates with wireless multifunctional brain probes or other types of conventional laboratory equipment using IoT control modules. By optimally integrating the versatility and modular construction of both unique IoT hardware and software within a single ecosystem, this wireless technology offers new applications that have not been demonstrated before by a single standalone technology. This includes, but is not limited to minimalistic hardware, global remote access, selective and scheduled experiments, customizable automation, and high-throughput scalability.
“As long as researchers have internet access, they are able to trigger, customize, stop, validate, and store the outcomes of large experiments at any time and from anywhere in the world. They can remotely perform large-scale neuroscience experiments in animals deployed in multiple countries,” said one of the lead authors, Dr. Raza Qazi, a researcher with KAIST and the University of Colorado, Boulder. “The low cost of this system allows it to be easily adopted and can further fuel innovation across many laboratories,” Dr. Qazi added.
One of the significant advantages of this IoT neurotechnology is its ability to be mass deployed across the globe due to its minimalistic hardware, low setup cost, ease of use, and customizable versatility. Scientists across the world can quickly implement this technology within their existing laboratories with minimal budget concerns to achieve globally remote access, scalable experimental automation, or both, thus potentially reducing the time needed to unravel various neuroscientific challenges such as those associated with intractable neurological conditions.
Another senior author on the study, Professor Jordan McCall from the Department of Anesthesiology and Center for Clinical Pharmacology at Washington University in St. Louis, said this technology has the potential to change how basic neuroscience studies are performed. “One of the biggest limitations when trying to understand how the mammalian brain works is that we have to study these functions in unnatural conditions. This technology brings us one step closer to performing important studies without direct human interaction with the study subjects.”
The ability to remotely schedule experiments moves toward automating these types of experiments. Dr. Kyle Parker, an instructor at Washington University in St. Louis and another lead author on the study added, “This experimental automation can potentially help us reduce the number of animals used in biomedical research by reducing the variability introduced by various experimenters. This is especially important given our moral imperative to seek research designs that enable this reduction.”
The researchers believe this wireless technology may open new opportunities for many applications including brain research, pharmaceuticals, and telemedicine to treat diseases in the brain and other organs remotely. This remote automation technology could become even more valuable when many labs need to shut down, such as during the height of the COVID-19 pandemic.
This work was supported by grants from the KAIST Global Singularity Research Program, the National Research Foundation of Korea, the United States National Institute of Health, and Oak Ridge Associated Universities.
-PublicationRaza Qazi, Kyle Parker, Choong Yeon Kim, Jordan McCall, Jae-Woong Jeong et al. “Scalable and modular wireless-network infrastructure for large-scale behavioral neuroscience,” Nature Biomedical Engineering, November 25 2021 (doi.org/10.1038/s41551-021-00814-w)
-ProfileProfessor Jae-Woong JeongBio-Integrated Electronics and Systems LabSchool of Electrical EngineeringKAIST
2021.11.29 View 21597 -
A Mechanism Underlying Most Common Cause of Epileptic Seizures Revealed
An interdisciplinary study shows that neurons carrying somatic mutations in MTOR can lead to focal epileptogenesis via non-cell-autonomous hyperexcitability of nearby nonmutated neurons
During fetal development, cells should migrate to the outer edge of the brain to form critical connections for information transfer and regulation in the body. When even a few cells fail to move to the correct location, the neurons become disorganized and this results in focal cortical dysplasia. This condition is the most common cause of seizures that cannot be controlled with medication in children and the second most common cause in adults.
Now, an interdisciplinary team studying neurogenetics, neural networks, and neurophysiology at KAIST has revealed how dysfunctions in even a small percentage of cells can cause disorder across the entire brain. They published their results on June 28 in Annals of Neurology.
The work builds on a previous finding, also by a KAIST scientists, who found that focal cortical dysplasia was caused by mutations in the cells involved in mTOR, a pathway that regulates signaling between neurons in the brain.
“Only 1 to 2% of neurons carrying mutations in the mTOR signaling pathway that regulates cell signaling in the brain have been found to include seizures in animal models of focal cortical dysplasia,” said Professor Jong-Woo Sohn from the Department of Biological Sciences. “The main challenge of this study was to explain how nearby non-mutated neurons are hyperexcitable.”
Initially, the researchers hypothesized that the mutated cells affected the number of excitatory and inhibitory synapses in all neurons, mutated or not. These neural gates can trigger or halt activity, respectively, in other neurons. Seizures are a result of extreme activity, called hyperexcitability. If the mutated cells upend the balance and result in more excitatory cells, the researchers thought, it made sense that the cells would be more susceptible to hyperexcitability and, as a result, seizures.
“Contrary to our expectations, the synaptic input balance was not changed in either the mutated or non-mutated neurons,” said Professor Jeong Ho Lee from the Graduate School of Medical Science and Engineering. “We turned our attention to a protein overproduced by mutated neurons.”
The protein is adenosine kinase, which lowers the concentration of adenosine. This naturally occurring compound is an anticonvulsant and works to relax vessels. In mice engineered to have focal cortical dysplasia, the researchers injected adenosine to replace the levels lowered by the protein. It worked and the neurons became less excitable.
“We demonstrated that augmentation of adenosine signaling could attenuate the excitability of non-mutated neurons,” said Professor Se-Bum Paik from the Department of Bio and Brain Engineering.
The effect on the non-mutated neurons was the surprising part, according to Paik. “The seizure-triggering hyperexcitability originated not in the mutation-carrying neurons, but instead in the nearby non-mutated neurons,” he said.
The mutated neurons excreted more adenosine kinase, reducing the adenosine levels in the local environment of all the cells. With less adenosine, the non-mutated neurons became hyperexcitable, leading to seizures.
“While we need further investigate into the relationship between the concentration of adenosine and the increased excitation of nearby neurons, our results support the medical use of drugs to activate adenosine signaling as a possible treatment pathway for focal cortical dysplasia,” Professor Lee said.
The Suh Kyungbae Foundation, the Korea Health Technology Research and Development Project, the Ministry of Health & Welfare, and the National Research Foundation in Korea funded this work.
-Publication:Koh, H.Y., Jang, J., Ju, S.H., Kim, R., Cho, G.-B., Kim, D.S., Sohn, J.-W., Paik, S.-B. and Lee, J.H. (2021), ‘Non–Cell Autonomous Epileptogenesis in Focal Cortical Dysplasia’ Annals of Neurology, 90: 285 299. (https://doi.org/10.1002/ana.26149)
-ProfileProfessor Jeong Ho Lee Translational Neurogenetics Labhttps://tnl.kaist.ac.kr/ Graduate School of Medical Science and Engineering KAIST
Professor Se-Bum Paik Visual System and Neural Network Laboratory http://vs.kaist.ac.kr/ Department of Bio and Brain EngineeringKAIST
Professor Jong-Woo Sohn Laboratory for Neurophysiology, https://sites.google.com/site/sohnlab2014/home Department of Biological SciencesKAIST
Dr. Hyun Yong Koh Translational Neurogenetics LabGraduate School of Medical Science and EngineeringKAIST
Dr. Jaeson Jang Ph.D.Visual System and Neural Network LaboratoryDepartment of Bio and Brain Engineering KAIST
Sang Hyeon Ju M.D.Laboratory for NeurophysiologyDepartment of Biological SciencesKAIST
2021.08.26 View 19259 -
AI ｜QC ITRC Opens at KAIST
(from left: Dean of College of Engineering Jong-Hwan Kim, Director of AI│QC ITRC June-Koo Rhee, Vice President for R&DB Heekyung Park and Director General for Industrial Policy Hong Taek Yong)
Artificial Intelligence｜The Quantum Computing Information Technology Research Center (AI｜QC ITRC) opened at KAIST on October 2.
AI｜QC ITRC, established with government funding, is the first institute specializing in quantum computing. Three universities (Seoul National University, Korea University, and Kyung Hee University), and four corporations, KT, Homomicus, Actusnetworks, and Mirae Tech are jointly participating in the center. Over four years, the institute will receive 3.2 billion KRW of research funds.
Last April, KAIST selected quantum technology as one of its flagship research areas. AI｜QC ITRC will dedicate itself to developing quantum computing technology that provides the computability required for human-level artificial intelligence. It will also foster leaders in related industries by introducing industry-academic educational programs in graduate schools.
QC is receiving a great deal of attention for transcending current digital computers in terms of computability. World-class IT companies like IBM, Google, and Intel and ventures including D-Wave, Rigetti, and IonQ are currently leading the industry and investing heavily in securing source technologies.
Starting from the establishment of the ITRC, KAIST will continue to plan strategies to foster the field of QC. KAIST will carry out two-track strategies; one is to secure source technology of first-generation QC technology, and the other is to focus on basic research that can preoccupy next-generation QC technology.
Professor June-Koo Rhee, the director of AI│QC ITRC said, “I believe that QC will be the imperative technology that enables the realization of the Fourth Industrial Revolution. AIQC ITRC will foster experts required for domestic academia and industries and build a foundation to disseminate the technology to industries.”
Vice President for R&DB Heekyung Park, Director General for Industrial Policy Hong Taek Yong from the Ministry of Science and ICT, Seung Pyo Hong from the Institute for Information & communications Technology Promotion, Head of Technology Strategy Jinhyon Youn from KT, and participating companies attended and celebrated the opening of the AI│QC ITRC.
2018.10.05 View 9139 -
Three Professors Named KAST Fellows
(Professor Dan Keun Sung at the center)
(Professor Y.H. Cho at the center)
(Professor K.H. Cho at the center)
The Korean Academy of Science and Technology (KAST) inducted three KAIST professors as fellows at the New Year’s ceremony held at KAST on January 12. They were among the 24 newly elected fellows of the most distinguished academy in Korea. The new fellows are Professor Dan Keun Sung of the School of Electrical Engineering, Professor Kwang-Hyun Cho of the Department of Bio and Brain Engineering, and Professor Yong-Hoon Cho of the Department of Physics.
Professor Sung was recognized for his lifetime academic achievements in fields related with network protocols and energy ICT. He also played a crucial role in launching the Korean satellites KITSAT-1,2,3 and the establishment of the Satellite Technology Research Center at KAIST.
Professor Y.H.Cho has been a pioneer in the field of low-dimensional semiconductor-powered quantum photonics that enables quantum optical research in solid state. He has been recognized as a renowned scholar in this field internationally.
Professor K.H.Cho has conducted original research that combines IT and BT in systems biology and has applied novel technologies of electronic modeling and computer simulation analysis for investigating complex life sciences. Professor Cho, who is in his 40s, is the youngest fellow among the newly inducted fellows.
2018.01.16 View 20404 -
Professor Dong Ho Cho Awarded at the Haedong Conference 2017
Professor Dong Ho Cho of the School of Electrical Engineering at KAIST received an award at the 13th Haedong Conference 2017 in Seoul on the first of December.
The Korean Institute of Communications and Information Sciences recognized Professor Cho for his significant contributions in the field of mobile communication networks. He has carried out groundbreaking research on mobile systems, including architecture, protocols, algorithms, optimization, and efficiency analysis.
As a result, he has produced 73 papers in renowned international journals, 138 papers at international conferences, and filed 52 international patents and 121 domestic patents. In addition, he transferred 14 of the patents he filed to Korean and international companies.
2017.12.07 View 10439 -
KAIST's Doctoral Candidate Receives the 2015 Google Ph.D. Fellowship
Shin-Ae Woo, a doctoral student of Professor Su-Bok Moon of KAIST’s School of Computer Engineering, has received the 2015 Google Ph.D. Fellowship.
The fellowship’s term lasts one year, starting September 2015.
The fellowship awarded Ms. Woo with USD 10,000 of cash prize, an opportunity to meet a Google research mentor, and a summer internship at the company.
Created in 2009, the Google Ph.D. Fellowship annually recognizes outstanding doctoral students around the world in computer science and its related fields.
This year, a total of 44 doctoral students including Ms. Woo, who is studying networking and distributed system, have been nominated.
She has also received the NSDI (Networked Systems Design and Implementation) 2015 Community Award and the 2014 Samsung Human Technology Journal Silver Prize for her research work on “Design and Implementation of Highly Scalable User-level TCP Stack for Multicore Systems” and “Comparison of Caching Strategies in Modern Cellular Backhaul Networks.”
Currently, Ms. Woo is working with UC Berkeley faculty on next-generation data centers for a research exchange program.
2015.09.15 View 9424