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New Liquid Metal Wearable Pressure Sensor Created for Health Monitoring Applications
Soft pressure sensors have received significant research attention in a variety of fields, including soft robotics, electronic skin, and wearable electronics. Wearable soft pressure sensors have great potential for the real-time health monitoring and for the early diagnosis of diseases. A KAIST research team led by Professor Inkyu Park from the Department of Mechanical Engineering developed a highly sensitive wearable pressure sensor for health monitoring applications. This work was reported in Advanced Healthcare Materials on November 21 as a front cover article. This technology is capable of sensitive, precise, and continuous measurement of physiological and physical signals and shows great potential for health monitoring applications and the early diagnosis of diseases. A soft pressure sensor is required to have high compliance, high sensitivity, low cost, long-term performance stability, and environmental stability in order to be employed for continuous health monitoring. Conventional solid-state soft pressure sensors using functional materials including carbon nanotubes and graphene have showed great sensing performance. However, these sensors suffer from limited stretchability, signal drifting, and long-term instability due to the distance between the stretchable substrate and the functional materials. To overcome these issues, liquid-state electronics using liquid metal have been introduced for various wearable applications. Of these materials, Galinstan, a eutectic metal alloy of gallium, indium, and tin, has great mechanical and electrical properties that can be employed in wearable applications. But today’s liquid metal-based pressure sensors have low-pressure sensitivity, limiting their applicability for health monitoring devices. The research team developed a 3D-printed rigid microbump array-integrated, liquid metal-based soft pressure sensor. With the help of 3D printing, the integration of a rigid microbump array and the master mold for a liquid metal microchannel could be achieved simultaneously, reducing the complexity of the manufacturing process. Through the integration of the rigid microbump and the microchannel, the new pressure sensor has an extremely low detection limit and enhanced pressure sensitivity compared to previously reported liquid metal-based pressure sensors. The proposed sensor also has a negligible signal drift over 10,000 cycles of pressure, bending, and stretching and exhibited excellent stability when subjected to various environmental conditions. These performance outcomes make it an excellent sensor for various health monitoring devices. First, the research team demonstrated a wearable wristband device that can continuously monitor one’s pulse during exercise and be employed in a noninvasive cuffless BP monitoring system based on PTT calculations. Then, they introduced a wireless wearable heel pressure monitoring system that integrates three 3D-BLiPS with a wireless communication module. Professor Park said, “It was possible to measure health indicators including pulse and blood pressure continuously as well as pressure of body parts using our proposed soft pressure sensor. We expect it to be used in health care applications, such as the prevention and the monitoring of the pressure-driven diseases such as pressure ulcers in the near future. There will be more opportunities for future research including a whole-body pressure monitoring system related to other physical parameters.” This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT. < Figure 1. The front cover image of Advanced Healthcare Materials, Volume 8, Issue 22. > < Figure 2. Highly sensitive liquid metal-based soft pressure sensor integrated with 3D-printed microbump array. > < Figure 3. High pressure sensitivity and reliable sensing performances of the proposed sensor and wireless heel pressure monitoring application. > Profile: Prof. Inkyu Park firstname.lastname@example.org Micro/Nano Transducers Laboratory http://mintlab1.kaist.ac.kr/ Department of Mechanical Engineering KAIST
Team Geumo Wins Consecutive Victories in K-Cyber Security Challenge
< Professor Sang Kil Cha > < Masters Candidate Kangsu Kim and Researcher Corentin Soulet > Team Geumo, led by Professor Sang Kil Cha from the Graduate School of Information Security, won the K-Cyber Security Challenge in the AI-based automatic vulnerability detection division for two consecutive years in 2018 and 2019. The K-Cyber Security Challenge is an inter-machine hacking competition. Participants develop and operate AI-based systems that are capable of independently identifying software vulnerabilities and gaining operating rights through hacking. The K-Cyber Security Challenge, inspired by the US Cyber Grand Challenge launched by the Defense Advanced Research Projects Agency (DARPA), is hosted by the Ministry of Science and ICT and organized by the Korea Internet and Security Agency. Researcher Corentin Soulet of the School of Computing and master’s student Kangsu Kim of the Graduate School of Information Security teamed up for the competition. Professor Cha, who has led the research on software and systems security since his days at Carnegie Mellon University, succeeded in establishing a world-class system using domestic technology. In a recent collaboration with the Cyber Security Research Center, Professor Cha achieved a ten-fold increase in the speed of binary analysis engines, a key component of AI-based hacking systems. For this accomplishment, he received the Best Paper Award at the 2019 Network and Distributed System Security Workshop on Binary Analysis Research (NDSS BAR). Kangsu Kim said, "It is a great honor to win the competition two years in a row. I will continue to work hard and apply my knowledge to serve society.” (END)
AI to Determine When to Intervene with Your Driving
(Professor Uichin Lee (left) and PhD candidate Auk Kim) Can your AI agent judge when to talk to you while you are driving? According to a KAIST research team, their in-vehicle conservation service technology will judge when it is appropriate to contact you to ensure your safety. Professor Uichin Lee from the Department of Industrial and Systems Engineering at KAIST and his research team have developed AI technology that automatically detects safe moments for AI agents to provide conversation services to drivers. Their research focuses on solving the potential problems of distraction created by in-vehicle conversation services. If an AI agent talks to a driver at an inopportune moment, such as while making a turn, a car accident will be more likely to occur. In-vehicle conversation services need to be convenient as well as safe. However, the cognitive burden of multitasking negatively influences the quality of the service. Users tend to be more distracted during certain traffic conditions. To address this long-standing challenge of the in-vehicle conversation services, the team introduced a composite cognitive model that considers both safe driving and auditory-verbal service performance and used a machine-learning model for all collected data. The combination of these individual measures is able to determine the appropriate moments for conversation and most appropriate types of conversational services. For instance, in the case of delivering simple-context information, such as a weather forecast, driver safety alone would be the most appropriate consideration. Meanwhile, when delivering information that requires a driver response, such as a “Yes” or “No,” the combination of driver safety and auditory-verbal performance should be considered. The research team developed a prototype of an in-vehicle conversation service based on a navigation app that can be used in real driving environments. The app was also connected to the vehicle to collect in-vehicle OBD-II/CAN data, such as the steering wheel angle and brake pedal position, and mobility and environmental data such as the distance between successive cars and traffic flow. Using pseudo-conversation services, the research team collected a real-world driving dataset consisting of 1,388 interactions and sensor data from 29 drivers who interacted with AI conversational agents. Machine learning analysis based on the dataset demonstrated that the opportune moments for driver interruption could be correctly inferred with 87% accuracy. The safety enhancement technology developed by the team is expected to minimize driver distractions caused by in-vehicle conversation services. This technology can be directly applied to current in-vehicle systems that provide conversation services. It can also be extended and applied to the real-time detection of driver distraction problems caused by the use of a smartphone while driving. Professor Lee said, “In the near future, cars will proactively deliver various in-vehicle conversation services. This technology will certainly help vehicles interact with their drivers safely as it can fairly accurately determine when to provide conversation services using only basic sensor data generated by cars.” The researchers presented their findings at the ACM International Joint Conference on Pervasive and Ubiquitous Computing (Ubicomp’19) in London, UK. This research was supported in part by Hyundai NGV and by the Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT. (Figure: Visual description of safe enhancement technology for in-vehicle conversation services)
Tungsten Suboxide Improves the Efficiency of Platinum in Hydrogen Production
< PhD Candidate Jinkyu Park and Professor Jinwoo Lee > Researchers presented a new strategy for enhancing catalytic activity using tungsten suboxide as a single-atom catalyst (SAC). This strategy, which significantly improves hydrogen evolution reaction (HER) in metal platinum (pt) by 16.3 times, sheds light on the development of new electrochemical catalyst technologies. Hydrogen has been touted as a promising alternative to fossil fuels. However, most of the conventional industrial hydrogen production methods come with environmental issues, releasing significant amounts of carbon dioxide and greenhouse gases. Electrochemical water splitting is considered a potential approach for clean hydrogen production. Pt is one of the most commonly used catalysts to improve HER performance in electrochemical water splitting, but the high cost and scarcity of Pt remain key obstacles to mass commercial applications. SACs, where all metal species are individually dispersed on a desired support material, have been identified as one way to reduce the amount of Pt usage, as they offer the maximum number of surface exposed Pt atoms. Inspired by earlier studies, which mainly focused on SACs supported by carbon-based materials, a KAIST research team led by Professor Jinwoo Lee from the Department of Chemical and Biomolecular Engineering investigated the influence of support materials on the performance of SACs. Professor Lee and his researchers suggested mesoporous tungsten suboxide as a new support material for atomically dispersed Pt, as this was expected to provide high electronic conductivity and have a synergetic effect with Pt. They compared the performance of single-atom Pt supported by carbon and tungsten suboxide respectively. The results revealed that the support effect occurred with tungsten suboxide, in which the mass activity of a single-atom Pt supported by tungsten suboxide was 2.1 times greater than that of single-atom Pt supported by carbon, and 16.3 times higher than that of Pt nanoparticles supported by carbon. The team indicated a change in the electronic structure of Pt via charge transfer from tungsten suboxide to Pt. This phenomenon was reported as a result of strong metal-support interaction between Pt and tungsten suboxide. HER performance can be improved not only by changing the electronic structure of the supported metal, but also by inducing another support effect, the spillover effect, the research group reported. Hydrogen spillover is a phenomenon where adsorbed hydrogen migrates from one surface to another, and it occurs more easily as the Pt size becomes smaller. The researchers compared the performance of single-atom Pt and Pt nanoparticles supported by tungsten suboxide. The single-atom Pt supported by tungsten suboxide exhibited a higher degree of hydrogen spillover phenomenon, which enhanced the Pt mass activity for hydrogen evolution up to 10.7 times compared to Pt nanoparticles supported by tungsten suboxide. Professor Lee said, “Choosing the right support material is important for improving electrocatalysis in hydrogen production. The tungsten suboxide catalyst we used to support Pt in our study implies that interactions between the well-matched metal and support can drastically enhance the efficiency of the process.” This research was supported by the Ministry of Science and ICT and introduced in the International Edition of the German journal Angewandte Chemie. Figure. Schematic representation of hydrogen evolution reaction (HER) of pseudo single-atom Pt supported by tungsten suboxide Publication: Jinkyu Park, Dr. Seonggyu Lee, Hee-Eun Kim, Ara Cho, Seongbeen Kim, Dr. Youngjin Ye, Prof. Jeong Woo Han, Prof. Hyunjoo Lee, Dr. Jong Hyun Jang, and Prof. Jinwoo Lee. 2019. Investigation of the Support Effect in Atomically Dispersed Pt on WO3−x for Utilization of Pt in the Hydrogen Evolution Reaction. International Edition of Angewandte Chemie. Volume No. 58. Issue No. 45. 6 pages. https://doi.org/10.1002/anie.201908122 Link to download the full-text paper: https://onlinelibrary.wiley.com/doi/epdf/10.1002/anie.201908122 Profile: Prof. Jinwoo Lee, MS, PhD email@example.com http://cens.kaist.ac.kr Professor Convergence of Energy and Nano Science Laboratory Department of Chemical and Biomolecular Engineering Korea Advanced Institute of Science and Technology (KAIST) https://www.kaist.ac.kr Daejeon 34141, Korea Profile: Jinkyu Park, PhD Candidate firstname.lastname@example.org (END)
Two Professors Recognized for the National R&D Excellence 100
< Professor Haeng-Ki Lee (left) and Professor Jeong-Ho Lee (right) > Two KAIST professors were listed among the 2019 National R&D Excellence 100 announced by the Ministry of Science and ICT and the Korea Institute of S&T Evaluation and Planning. Professor Haeng-Ki Lee from the Department of Civil and Environmental Engineering was recognized in the field of mechanics and materials for his research on developing new construction materials through the convergence of nano- and biotechnologies. In the field of life and marine science, Professor Jeong-Ho Lee from the Graduate School of Medical Science and Engineering was lauded for his research of diagnostic tools and therapies for glioblastoma and pediatric brain tumors. A certificate from the Minister of Ministry of Science and ICT will be conferred to these two professors, and their names will be inscribed on a special 2019 National R&D Excellence 100 plaque to celebrate their achievements. The professors will also be given privileges during the process of new R&D project selection. (END)
Professor Byong-Guk Park Named Scientist of October
< Professor Byong-Guk Park > Professor Byong-Guk Park from the Department of Materials Science and Engineering was selected as the ‘Scientist of the Month’ for October 2019 by the Ministry of Science and ICT and the National Research Foundation of Korea. Professor Park was recognized for his contributions to the advancement of spin-orbit torque (SOT)-based magnetic random access memory (MRAM) technology. He received 10 million KRW in prize money. A next-generation, non-volatile memory device MRAM consists of thin magnetic films. It can be applied in “logic-in-memory” devices, in which logic and memory functionalities coexist, thus drastically improving the performance of complementary metal-oxide semiconductor (CMOS) processors. Conventional MRAM technology is limited in its ability to increase the operation speed of a memory device while maintaining a high density. Professor Park tackled this challenge by introducing a new material, antiferromagnet (IrMn), that generates a sizable amount of SOT as well as an exchange-bias field, which makes successful data writing possible without an external magnetic field. This research outcome paved the way for the development of MRAM, which has a simple device structure but features high speeds and density. Professor Park said, “I feel rewarded to have forwarded the feasibility and applicability of MRAM. I will continue devoting myself to studying further on the development of new materials that can help enhance the performance of memory devices." (END)
Object Identification and Interaction with a Smartphone Knock
(Professor Lee (far right) demonstrate 'Knocker' with his students.) A KAIST team has featured a new technology, “Knocker”, which identifies objects and executes actions just by knocking on it with the smartphone. Software powered by machine learning of sounds, vibrations, and other reactions will perform the users’ directions. What separates Knocker from existing technology is the sensor fusion of sound and motion. Previously, object identification used either computer vision technology with cameras or hardware such as RFID (Radio Frequency Identification) tags. These solutions all have their limitations. For computer vision technology, users need to take pictures of every item. Even worse, the technology will not work well in poor lighting situations. Using hardware leads to additional costs and labor burdens. Knocker, on the other hand, can identify objects even in dark environments only with a smartphone, without requiring any specialized hardware or using a camera. Knocker utilizes the smartphone’s built-in sensors such as a microphone, an accelerometer, and a gyroscope to capture a unique set of responses generated when a smartphone is knocked against an object. Machine learning is used to analyze these responses and classify and identify objects. The research team under Professor Sung-Ju Lee from the School of Computing confirmed the applicability of Knocker technology using 23 everyday objects such as books, laptop computers, water bottles, and bicycles. In noisy environments such as a busy café or on the side of a road, it achieved 83% identification accuracy. In a quiet indoor environment, the accuracy rose to 98%. The team believes Knocker will open a new paradigm of object interaction. For instance, by knocking on an empty water bottle, a smartphone can automatically order new water bottles from a merchant app. When integrated with IoT devices, knocking on a bed’s headboard before going to sleep could turn off the lights and set an alarm. The team suggested and implemented 15 application cases in the paper, presented during the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2019) held in London last month. Professor Sung-Ju Lee said, “This new technology does not require any specialized sensor or hardware. It simply uses the built-in sensors on smartphones and takes advantage of the power of machine learning. It’s a software solution that everyday smartphone users could immediately benefit from.” He continued, “This technology enables users to conveniently interact with their favorite objects.” The research was supported in part by the Next-Generation Information Computing Development Program through the National Research Foundation of Korea funded by the Ministry of Science and ICT and an Institute for Information & Communications Technology Promotion (IITP) grant funded by the Ministry of Science and ICT. Figure: An example knock on a bottle. Knocker identifies the object by analyzing a unique set of responses from the knock, and automatically launches a proper application or service.
Accurate Detection of Low-Level Somatic Mutation in Intractable Epilepsy
KAIST medical scientists have developed an advanced method for perfectly detecting low-level somatic mutation in patients with intractable epilepsy. Their study showed that deep sequencing replicates of major focal epilepsy genes accurately and efficiently identified low-level somatic mutations in intractable epilepsy. According to the study, their diagnostic method could increase the accuracy up to 100%, unlike the conventional sequencing analysis, which stands at about 30% accuracy. This work was published in Acta Neuropathologica. Epilepsy is a neurological disorder common in children. Approximately one third of child patients are diagnosed with intractable epilepsy despite adequate anti-epileptic medication treatment. Somatic mutations in mTOR pathway genes, SLC35A2, and BRAF are the major genetic causes of intractable epilepsies. A clinical trial to target Focal Cortical Dysplasia type II (FCDII), the mTOR inhibitor is underway at Severance Hospital, their collaborator in Seoul, Korea. However, it is difficult to detect such somatic mutations causing intractable epilepsy because their mutational burden is less than 5%, which is similar to the level of sequencing artifacts. In the clinical field, this has remained a standing challenge for the genetic diagnosis of somatic mutations in intractable epilepsy. Professor Jeong Ho Lee’s team at the Graduate School of Medical Science and Engineering analyzed paired brain and peripheral tissues from 232 intractable epilepsy patients with various brain pathologies at Severance Hospital using deep sequencing and extracted the major focal epilepsy genes. They narrowed down target genes to eight major focal epilepsy genes, eliminating almost all of the false positive calls using deep targeted sequencing. As a result, the advanced method robustly increased the accuracy and enabled them to detect low-level somatic mutations in unmatched Formalin Fixed Paraffin Embedded (FFPE) brain samples, the most clinically relevant samples. Professor Lee conducted this study in collaboration with Professor Dong Suk Kim and Hoon-Chul Kang at Severance Hospital of Yonsei University. He said, “This advanced method of genetic analysis will improve overall patient care by providing more comprehensive genetic counseling and informing decisions on alternative treatments.” Professor Lee has investigated low-level somatic mutations arising in the brain for a decade. He is developing innovative diagnostics and therapeutics for untreatable brain disorders including intractable epilepsy and glioblastoma at a tech-startup called SoVarGen. “All of the technologies we used during the research were transferred to the company. This research gave us very good momentum to reach the next phase of our startup,” he remarked. The work was supported by grants from the Suh Kyungbae Foundation, a National Research Foundation of Korea grant funded by the Ministry of Science and ICT, the Korean Health Technology R&D Project from the Ministry of Health & Welfare, and the Netherlands Organization for Health Research and Development. (Figure: Landscape of somatic and germline mutations identified in intractable epilepsy patients. a Signaling pathways for all of the mutated genes identified in this study. Bold: somatic mutation, Regular: germline mutation. b The distribution of variant allelic frequencies (VAFs) of identified somatic mutations. c The detecting rate and types of identified mutations according to histopathology. Yellow: somatic mutations, green: two-hit mutations, grey: germline mutations.)
Deep Learning-Powered 'DeepEC' Helps Accurately Understand Enzyme Functions
(Figure: Overall scheme of DeepEC) A deep learning-powered computational framework, ‘DeepEC,’ will allow the high-quality and high-throughput prediction of enzyme commission numbers, which is essential for the accurate understanding of enzyme functions. A team of Dr. Jae Yong Ryu, Professor Hyun Uk Kim, and Distinguished Professor Sang Yup Lee at KAIST reported the computational framework powered by deep learning that predicts enzyme commission (EC) numbers with high precision in a high-throughput manner. DeepEC takes a protein sequence as an input and accurately predicts EC numbers as an output. Enzymes are proteins that catalyze biochemical reactions and EC numbers consisting of four level numbers (i.e., a.b.c.d) indicate biochemical reactions. Thus, the identification of EC numbers is critical for accurately understanding enzyme functions and metabolism. EC numbers are usually given to a protein sequence encoding an enzyme during a genome annotation procedure. Because of the importance of EC numbers, several EC number prediction tools have been developed, but they have room for further improvement with respect to computation time, precision, coverage, and the total size of the files needed for the EC number prediction. DeepEC uses three convolutional neural networks (CNNs) as a major engine for the prediction of EC numbers, and also implements homology analysis for EC numbers if the three CNNs do not produce reliable EC numbers for a given protein sequence. DeepEC was developed by using a gold standard dataset covering 1,388,606 protein sequences and 4,669 EC numbers. In particular, benchmarking studies of DeepEC and five other representative EC number prediction tools showed that DeepEC made the most precise and fastest predictions for EC numbers. DeepEC also required the smallest disk space for implementation, which makes it an ideal third-party software component. Furthermore, DeepEC was the most sensitive in detecting enzymatic function loss as a result of mutations in domains/binding site residue of protein sequences; in this comparative analysis, all the domains or binding site residue were substituted with L-alanine residue in order to remove the protein function, which is known as the L-alanine scanning method. This study was published online in the Proceedings of the National Academy of Sciences of the United States of America (PNAS) on June 20, 2019, entitled “Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers.” “DeepEC can be used as an independent tool and also as a third-party software component in combination with other computational platforms that examine metabolic reactions. DeepEC is freely available online,” said Professor Kim. Distinguished Professor Lee said, “With DeepEC, it has become possible to process ever-increasing volumes of protein sequence data more efficiently and more accurately.” This work was supported by the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries from the Ministry of Science and ICT through the National Research Foundation of Korea. This work was also funded by the Bio & Medical Technology Development Program of the National Research Foundation of Korea funded by the Korean government, the Ministry of Science and ICT. Profile: -Professor Hyun Uk Kim (email@example.com) https://sites.google.com/view/ehukim Department of Chemical and Biomolecular Engineering -Distinguished Professor Sang Yup Lee (firstname.lastname@example.org) Department of Chemical and Biomolecular Engineering http://mbel.kaist.ac.kr
Two Alumni Win the Korea Best Scientist and Technologist Awards
Vice Chairman Ki-Nam Kim (Left) and Distinguished Professor Sukbok Chang (Right) <ⓒ Photo by MSIT and KOFST> Distinguished KAIST Professor Sukbok Chang from the Department of Chemistry and Vice Chairman Ki-Nam Kim of Samsung Electronics were selected as the winners of the “2019 Korea Best Scientist and Technologist Awards” by the Ministry of Science and ICT (MSIT) and the Korean Federation of Science and Technology Societies (KOFST). The awards, which were first handed out in 2003, are the highest honor bestowed to the two most outstanding scientists in Korea every year, and this year’s awardees are of greater significance as they are both KAIST alumni. Professor Chang was recognized for his pioneering achievements and lifetime contributions to the development of carbon-hydrogen activation strategies, especially for carbon-carbon, carbon-nitrogen, and carbon-oxygen formations. His research group has also been actively involved in the development of highly selective catalytic systems allowing the controlled defunctionalization of bio-derived platform substrates under mild conditions, and opening a new avenue for the utilization of biomass-derived platform chemicals. The results of his study have been introduced worldwide through many prestigious journals including Science, Nature Chemistry, and Nature Catalysis, making him one of the world's top 1% researchers by the number of references made to his papers by his peers over four consecutive years from 2015 to 2018. Vice Chairman Kim, who received his M.E. degree from KAIST’s School of Electrical Engineering in 1983, has been credited with playing a leading role in the development of system semiconductors. The awards were conferred on July 4 at the opening ceremony of the 2019 Korea Science and Technology Annual Meeting. (END)
5 Biomarkers for Overcoming Colorectal Cancer Drug Resistance Identified
< Professor Kwang-Hyun Cho's Team > KAIST researchers have identified five biomarkers that will help them address resistance to cancer-targeting therapeutics. This new treatment strategy will bring us one step closer to precision medicine for patients who showed resistance. Colorectal cancer is one of the most common types of cancer worldwide. The number of patients has surpassed 1 million, and its five-year survival rate significantly drops to about 20 percent when metastasized. In Korea, the surge of colorectal cancer has been the highest in the last 10 years due to increasing Westernized dietary patterns and obesity. It is expected that the number and mortality rates of colorectal cancer patients will increase sharply as the nation is rapidly facing an increase in its aging population. Recently, anticancer agents targeting only specific molecules of colon cancer cells have been developed. Unlike conventional anticancer medications, these selectively treat only specific target factors, so they can significantly reduce some of the side-effects of anticancer therapy while enhancing drug efficacy. Cetuximab is the most well-known FDA approved anticancer medication. It is a biomarker that predicts drug reactivity and utilizes the presence of the ‘KRAS’ gene mutation. Cetuximab is prescribed to patients who don’t carry the KRAS gene mutation. However, even in patients without the KRAS gene mutation, the response rate of Cetuximab is only about fifty percent, and there is also resistance to drugs after targeted chemotherapy. Compared with conventional chemotherapy alone, the life expectancy only lasts five months on average. In research featured in the FEBS Journal as the cover paper for the April 7 edition, the KAIST research team led by Professor Kwang-Hyun Cho at the Department of Bio and Brain Engineering presented five additional biomarkers that could increase Cetuximab responsiveness using systems biology approach that combines genomic data analysis, mathematical modeling, and cell experiments. The experimental inhibition of newly discovered biomarkers DUSP4, ETV5, GNB5, NT5E, and PHLDA1 in colorectal cancer cells has been shown to overcome Cetuximab resistance in KRAS-normal genes. The research team confirmed that when suppressing GNB5, one of the new biomarkers, it was shown to overcome resistance to Cetuximab regardless of having a mutation in the KRAS gene. Professor Cho said, “There has not been an example of colorectal cancer treatment involving regulation of the GNB5 gene.” He continued, “Identifying the principle of drug resistance in cancer cells through systems biology and discovering new biomarkers that could be a new molecular target to overcome drug resistance suggest real potential to actualize precision medicine.” This study was supported by the National Research Foundation of Korea (NRF) and funded by the Ministry of Science and ICT (2017R1A2A1A17069642 and 2015M3A9A7067220). Image 1. The cover of FEBS Journal for April 2019
Engineered Microbial Production of Grape Flavoring
(Image 1: Engineered bacteria that produce grape flavoring.) Researchers report a microbial method for producing an artificial grape flavor. Methyl anthranilate (MANT) is a common grape flavoring and odorant compound currently produced through a petroleum-based process that uses large volumes of toxic acid catalysts. Professor Sang-Yup Lee’s team at the Department of Chemical and Biomolecular Engineering demonstrated production of MANT, a naturally occurring compound, via engineered bacteria. The authors engineered strains of Escherichia coli and Corynebacetrium glutamicum to produce MANT through a plant-based engineered metabolic pathway. The authors tuned the bacterial metabolic pathway by optimizing the levels of AAMT1, the key enzyme in the process. To maximize production of MANT, the authors tested six strategies, including increasing the supply of a precursor compound and enhancing the availability of a co-substrate. The most productive strategy proved to be a two-phase extractive culture, in which MANT was extracted into a solvent. This strategy produced MANT on the scale of 4.47 to 5.74 grams per liter, a significant amount, considering that engineered microbes produce most natural products at a scale of milligrams or micrograms per liter. According to the authors, the results suggest that MANT and other related molecules produced through industrial processes can be produced at scale by engineered microbes in a manner that would allow them to be marketed as natural one, instead of artificial one. This study, featured at the Proceeding of the National Academy of Sciences of the USA on May 13, was supported by the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries from the Ministry of Science and ICT. (Image 2. Overview of the strategies applied for the microbial production of grape flavoring.)
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