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A System for Stable Simultaneous Communication among Thousands of IoT Devices
A mmWave Backscatter System, developed by a team led by Professor Song Min Kim is exciting news for the IoT market as it will be able to provide fast and stable connectivity even for a massive network, which could finally allow IoT devices to reach their full potential. A research team led by Professor Song Min Kim of the KAIST School of Electrical Engineering developed a system that can support concurrent communications for tens of millions of IoT devices using backscattering millimeter-level waves (mmWave). With their mmWave backscatter method, the research team built a design enabling simultaneous signal demodulation in a complex environment for communication where tens of thousands of IoT devices are arranged indoors. The wide frequency range of mmWave exceeds 10GHz, which provides great scalability. In addition, backscattering reflects radiated signals instead of wirelessly creating its own, which allows operation at ultralow power. Therefore, the mmWave backscatter system offers internet connectivity on a mass scale to IoT devices at a low installation cost. This research by Kangmin Bae et al. was presented at ACM MobiSys 2022. At this world-renowned conference for mobile systems, the research won the Best Paper Award under the title “OmniScatter: Sensitivity mmWave Backscattering Using Commodity FMCW Radar”. It is meaningful that members of the KAIST School of Electrical Engineering have won the Best Paper Award at ACM MobiSys for two consecutive years, as last year was the first time the award was presented to an institute from Asia. IoT, as a core component of 5G/6G network, is showing exponential growth, and is expected to be part of a trillion devices by 2035. To support the connection of IoT devices on a mass scale, 5G and 6G each aim to support ten times and 100 times the network density of 4G, respectively. As a result, the importance of practical systems for large-scale communication has been raised. The mmWave is a next-generation communication technology that can be incorporated in 5G/6G standards, as it utilizes carrier waves at frequencies between 30 to 300GHz. However, due to signal reduction at high frequencies and reflection loss, the current mmWave backscatter system enables communication in limited environments. In other words, it cannot operate in complex environments where various obstacles and reflectors are present. As a result, it is limited to the large-scale connection of IoT devices that require a relatively free arrangement. The research team found the solution in the high coding gain of an FMCW radar. The team developed a signal processing method that can fundamentally separate backscatter signals from ambient noise while maintaining the coding gain of the radar. They achieved a receiver sensitivity of over 100 thousand times that of previously reported FMCW radars, which can support communication in practical environments. Additionally, given the radar’s property where the frequency of the demodulated signal changes depending on the physical location of the tag, the team designed a system that passively assigns them channels. This lets the ultralow-power backscatter communication system to take full advantage of the frequency range at 10 GHz or higher. The developed system can use the radar of existing commercial products as gateway, making it easily compatible. In addition, since the backscatter system works at ultralow power levels of 10uW or below, it can operate for over 40 years with a single button cell and drastically reduce installation and maintenance costs. The research team confirmed that mmWave backscatter devices arranged randomly in an office with various obstacles and reflectors could communicate effectively. The team then took things one step further and conducted a successful trace-driven evaluation where they simultaneously received information sent by 1,100 devices. Their research presents connectivity that greatly exceeds network density required by next-generation communication like 5G and 6G. The system is expected to become a stepping stone for the hyper-connected future to come. Professor Kim said, “mmWave backscatter is the technology we’ve dreamt of. The mass scalability and ultralow power at which it can operate IoT devices is unmatched by any existing technology”. He added, “We look forward to this system being actively utilized to enable the wide availability of IoT in the hyper-connected generation to come”. To demonstrate the massive connectivity of the system, a trace-driven evaluation of 1,100 concurrent tag transmissions are made. Figure shows the demodulation result of each and every 1,100 tags as red triangles, where they successfully communicate without collision. This work was supported by Samsung Research Funding & Incubation Center of Samsung Electronics and by the ITRC (Information Technology Research Center) support program supervised by the IITP (Institute of Information & Communications Technology Planning & Evaluation). Profile: Song Min Kim, Ph.D.Professorsongmin@kaist.ac.krhttps://smile.kaist.ac.kr SMILE Lab.School of Electrical Engineering
Professor Jae-Woong Jeong Receives Hyonwoo KAIST Academic Award
Professor Jae-Woong Jeong from the School of Electrical Engineering was selected for the Hyonwoo KAIST Academic Award, funded by the HyonWoo Cultural Foundation (Chairman Soo-il Kwak, honorary professor at Seoul National University Business School). The Hyonwoo KAIST Academic Award, presented for the first time in 2021, is an award newly founded by the donations of Chairman Soo-il Kwak of the HyonWoo Cultural Foundation, who aims to reward excellent KAIST scholars who have made outstanding academic achievements. Every year, through the strict evaluations of the selection committee of the HyonWoo Cultural Foundation and the faculty reward recommendation board, KAIST will choose one faculty member that may represent the school with their excellent academic achievement, and reward them with a plaque and 100 million won. Professor Jae-Woong Jeong, the winner of this year’s award, developed the first IoT-based wireless remote brain neural network control system to overcome brain diseases, and has been leading the field. The research was published in 2021 in Nature Biomedical Engineering, one of world’s best scientific journals, and has been recognized as a novel technology that suggested a new vision for the automation of brain research and disease treatment. This study, led by Professor Jeong’s research team, was part of the KAIST College of Engineering Global Initiative Interdisciplinary Research Project, and was jointly studied by Washington University School of Medicine through an international research collaboration. The technology was introduced more than 60 times through both domestic and international media, including Medical Xpress, MBC News, and Maeil Business News. Professor Jeong has also developed a wirelessly chargeable soft machine for brain transplants, and the results were published in Nature Communications. He thereby opened a new paradigm for implantable semi-permanent devices for transplants, and is making unprecedented research achievements.
VP Sang Yup Lee Receives Honorary Doctorate from DTU
Vice President for Research, Distinguished Professor Sang Yup Lee at the Department of Chemical & Biomolecular Engineering, was awarded an honorary doctorate from the Technical University of Denmark (DTU) during the DTU Commemoration Day 2022 on April 29. The event drew distinguished guests, students, and faculty including HRH The Crown Prince Frederik Andre Henrik Christian and DTU President Anders Bjarklev. Professor Lee was recognized for his exceptional scholarship in the field of systems metabolic engineering, which led to the development of microcell factories capable of producing a wide range of fuels, chemicals, materials, and natural compounds, many for the first time. Professor Lee said in his acceptance speech that KAIST’s continued partnership with DTU in the field of biotechnology will lead to significant contributions in the global efforts to respond to climate change and promote green growth. DTU CPO and CSO Dina Petronovic Nielson, who heads DTU Biosustain, also lauded Professor Lee saying, “It is not only a great honor for Professor Lee to be induced at DTU but also great honor for DTU to have him.” Professor Lee also gave commemorative lectures at DTU Biosustain in Lingby and the Bio Innovation Research Institute at the Novo Nordisk Foundation in Copenhagen while in Denmark. DTU, one of the leading science and technology universities in Europe, has been awarding honorary doctorates since 1921, including to Nobel laureate in chemistry Professor Frances Arnold at Caltech. Professor Lee is the first Korean to receive an honorary doctorate from DTU.
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
3D Visualization and Quantification of Bioplastic PHA in a Living Bacterial Cell
3D holographic microscopy leads to in-depth analysis of bacterial cells accumulating the bacterial bioplastic, polyhydroxyalkanoate (PHA) A research team at KAIST has observed how bioplastic granule is being accumulated in living bacteria cells through 3D holographic microscopy. Their 3D imaging and quantitative analysis of the bioplastic ‘polyhydroxyalkanoate’ (PHA) via optical diffraction tomography provides insights into biosynthesizing sustainable substitutes for petroleum-based plastics. The bio-degradable polyester polyhydroxyalkanoate (PHA) is being touted as an eco-friendly bioplastic to replace existing synthetic plastics. While carrying similar properties to general-purpose plastics such as polyethylene and polypropylene, PHA can be used in various industrial applications such as container packaging and disposable products. PHA is synthesized by numerous bacteria as an energy and carbon storage material under unbalanced growth conditions in the presence of excess carbon sources. PHA exists in the form of insoluble granules in the cytoplasm. Previous studies on investigating in vivo PHA granules have been performed by using fluorescence microscopy, transmission electron microscopy (TEM), and electron cryotomography. These techniques have generally relied on the statistical analysis of multiple 2D snapshots of fixed cells or the short-time monitoring of the cells. For the TEM analysis, cells need to be fixed and sectioned, and thus the investigation of living cells was not possible. Fluorescence-based techniques require fluorescence labeling or dye staining. Thus, indirect imaging with the use of reporter proteins cannot show the native state of PHAs or cells, and invasive exogenous dyes can affect the physiology and viability of the cells. Therefore, it was difficult to fully understand the formation of PHA granules in cells due to the technical limitations, and thus several mechanism models based on the observations have been only proposed. The team of metabolic engineering researchers led by Distinguished Professor Sang Yup Lee and Physics Professor YongKeun Park, who established the startup Tomocube with his 3D holographic microscopy, reported the results of 3D quantitative label-free analysis of PHA granules in individual live bacterial cells by measuring the refractive index distributions using optical diffraction tomography. The formation and growth of PHA granules in the cells of Cupriavidus necator, the most-studied native PHA (specifically, poly(3-hydroxybutyrate), also known as PHB) producer, and recombinant Escherichia coli harboring C. necator PHB biosynthesis pathway were comparatively examined. From the reconstructed 3D refractive index distribution of the cells, the team succeeded in the 3D visualization and quantitative analysis of cells and intracellular PHA granules at a single-cell level. In particular, the team newly presented the concept of “in vivo PHA granule density.” Through the statistical analysis of hundreds of single cells accumulating PHA granules, the distinctive differences of density and localization of PHA granules in the two micro-organisms were found. Furthermore, the team identified the key protein that plays a major role in making the difference that enabled the characteristics of PHA granules in the recombinant E. coli to become similar to those of C. necator. The research team also presented 3D time-lapse movies showing the actual processes of PHA granule formation combined with cell growth and division. Movies showing the living cells synthesizing and accumulating PHA granules in their native state had never been reported before. Professor Lee said, “This study provides insights into the morphological and physical characteristics of in vivo PHA as well as the unique mechanisms of PHA granule formation that undergo the phase transition from soluble monomers into the insoluble polymer, followed by granule formation. Through this study, a deeper understanding of PHA granule formation within the bacterial cells is now possible, which has great significance in that a convergence study of biology and physics was achieved. This study will help develop various bioplastics production processes in the future.” This work was supported by the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries (Grants NRF-2012M1A2A2026556 and NRF-2012M1A2A2026557) and the Bio & Medical Technology Development Program (Grant No. 2021M3A9I4022740) from the Ministry of Science and ICT (MSIT) through the National Research Foundation (NRF) of Korea to S.Y.L. This work was also supported by the KAIST Cross-Generation Collaborative Laboratory project. -PublicationSo Young Choi, Jeonghun Oh, JaeHwang Jung, YongKeun Park, and Sang Yup Lee. Three-dimensional label-free visualization and quantification of polyhydroxyalkanoates in individualbacterial cell in its native state. PNAS(https://doi.org./10.1073/pnas.2103956118) -ProfileDistinguished Professor Sang Yup LeeMetabolic Engineering and Synthetic Biologyhttp://mbel.kaist.ac.kr/ Department of Chemical and Biomolecular Engineering KAIST Endowed Chair Professor YongKeun ParkBiomedical Optics Laboratoryhttps://bmokaist.wordpress.com/ Department of PhysicsKAIST
Professor Hojong Chang’s Research Team Wins ISIITA 2020 Best Paper Award
The paper written by Professor Hojong Chang’s research team from KAIST Institute for IT Convergence won the best paper award from the International Symposium on Innovation in Information Technology Application (ISIITA) 2020, held this month at Ton Duc Thang University in Vietnam. ISIITA is a networking symposium where leading researchers from various fields including information and communications, biotechnology, and computer systems come together and share on the convergence of technology. Professor Chang’s team won the best paper award at this year’s symposium with its paper, “A Study of Single Photon Counting System for Quantitative Analysis of Luminescence”. The awarded paper discusses the realization of a signal processing system for silicon photomultipliers. The silicon photomultiplier is the core of a urinalysis technique that tests for sodium and potassium in the body using simple chemical reactions. If our bodily sodium and potassium levels exceed a certain amount, it can lead to high blood pressure, cardiovascular problems, and kidney damage. Through this research, the team has developed a core technique that quantifies the sodium and potassium discharged in the urine. When the reagent is injected into the urine, a very small amount of light is emitted as a result of the chemical reaction. However, if there is a large amount of sodium and potassium, they interrupt the reaction and reduce the emission. The key to this measurement technique is digitizing the strength of this very fine emission of light. Professor Chang’s team developed a system that uses a photomultiplier to measure the chemiluminescence. Professor Chang said, “I look forward for this signal processing system greatly helping to prevent diseases caused by the excessive consumption of sodium and potassium through quick and easy detection.” Researcher Byunghun Han who carried out the central research for the system design added, “We are planning to focus on miniaturizing the developed technique, so that anyone can carry our device around like a cellphone.” The research was supported by the Ministry of Science and ICT. (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.
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
Efficiently Producing Fatty Acids and Biofuels from Glucose
Researchers have presented a new strategy for efficiently producing fatty acids and biofuels that can transform glucose and oleaginous microorganisms into microbial diesel fuel, with one-step direct fermentative production. The newly developed strain, created by Distinguished Professor Sang Yup Lee and his team, showed the highest efficiency in producing fatty acids and biodiesels ever reported. It will be expected to serve as a new platform to sustainably produce a wide array of fatty acid-based products from glucose and other carbon substrates. Fossil fuels, which have long been energy resources for our daily lives, are now facing serious challenges: depletion of their reserves and their role in global warming. The production of sustainable bio-based renewable energy has emerged as an essential alternative and many studies to replace fossil fuels are underway. One of the representative examples is biodiesel. Currently, it is mainly being produced through the transesterification of vegetable oils or animal fats. The research team engineered oleaginous microorganisms, Rhodococcus opacus, to produce fatty acids and their derivatives that can be used as biodiesel from glucose, one of the most abundant and cheap sugars derived from non-edible biomass. Professor Lee’s team has already engineered Escherichia coli to produce short-chain hydrocarbons, which can be used as gasoline (published in Nature as the cover paper in 2013). However, the production efficiency of the short-chain hydrocarbons using E. coli (0.58 g/L) fell short of the levels required for commercialization. To overcome these issues, the team employed oil-accumulating Rhodococcus opacus as a host strain in this study. First, the team optimized the cultivation conditions of Rhodococcus opacus to maximize the accumulation of oil (triacylglycerol), which serves as a precursor for the biosynthesis of fatty acids and their derivatives. Then, they systematically analyzed the metabolism of the strain and redesigned it to enable higher levels of fatty acids and two kinds of fatty acid-derived biodiesels (fatty acid ethyl esters and long-chain hydrocarbons) to be produced. They found that the resulting strains produced 50.2, 21.3, and 5.2 g/L of fatty acids, fatty acid ethyl esters, and long-chain hydrocarbons, respectively. These are all the highest concentrations ever reported by microbial fermentations. It is expected that these strains can contribute to the future industrialization of microbial-based biodiesel production. “This technology creates fatty acids and biodiesel with high efficiency by utilizing lignocellulose, one of the most abundant resources on the Earth, without depending on fossil fuels and vegetable or animal oils. This will provide new opportunities for oil and petroleum industries, which have long relied on fossil fuels, to turn to sustainable and eco-friendly biotechnologies,” said Professor Lee. This paper titled “Engineering of an oleaginous bacterium for the production of fatty acids and fuels” was published in Nature Chemical Biology on June 17. 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 (NRF) of Korea (NRF-2012M1A2A2026556 and NRF-2012M1A2A2026557). (Figure: Metabolic engineering for the production of free fatty acids (FFAs), fatty acid ethyl esters (FAEEs), and long-chain hydrocarbons (LCHCs) in Rhodococcus opacus PD630. Researchers have presented a new strategy for efficiently producing fatty acids and biofuels that can transform glucose and oleaginous microorganisms into microbial diesel fuel, with one-step direct fermentative production.) # # # Source: Hye Mi Kim, Tong Un Chae, So Young Choi, Won Jun Kim and Sang Yup Lee. Engineering of an oleaginous bacterium for the production of fatty acids and fuels. Nature Chemical Biology ( https://www.nature.com/nchembio/ ) DOI: 10.1038/s41589-019-0295-5 Profile Dr. Sang Yup Lee email@example.com Distinguished Professor at the Department of Chemical and Biomolecular Engineering KAIST
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.)
Adding Smart to Science Museum
KAIST and the National Science Museum (NSM) created an Exhibition Research Center for Smart Science to launch exhibitions that integrate emerging technologies in the Fourth Industrial Revolution, including augmented reality (AR), virtual reality (VR), Internet of Things (IoTs), and artificial intelligence (AI). There has been a great demand for a novel technology for better, user-oriented exhibition services. The NSM continuously faces the problem of not having enough professional guides. Additionally, there have been constant complaints about its current mobile application for exhibitions not being very effective. To tackle these problems, the new center was founded, involving 11 institutes and universities. Sponsored by the National Research Foundation, it will oversee 15 projects in three areas: exhibition-based technology, exhibition operational technology, and exhibition content. The group first aims to provide a location-based exhibition guide system service, which allows it to incorporate various technological services, such as AR/VR to visitors. An indoor locating system named KAILOS, which was developed by KAIST, will be applied to this service. They will also launch a mobile application service that provides audio-based exhibition guides. To further cater to visitors’ needs, the group plans to apply a user-centered ecosystem, a living lab concept to create pleasant environment for visitors. “Every year, hundred thousands of young people visit the National Science Museum. I believe that the exhibition guide system has to be innovative, using cutting-edge IT technology in order to help them cherish their dreams and inspirations through science,” Jeong Heoi Bae, President of Exhibition and Research Bureau of NSM, emphasized. Professor Dong Soo Han from the School of Computing, who took the position of research head of the group, said, “We will systematically develop exhibition technology and contents for the science museum to create a platform for smart science museums. It will be the first time to provide an exhibition guide system that integrates AR/VR with an indoor location system.” The center will first apply the new system to the NSM and then expand it to 167 science museums and other regional museums.
Distinguished Professor Lee Receives 2018 George Washington Carver Award
(Distinguished Professor Lee) Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering will become the 11th recipient of the George Washington Carver Award. The award ceremony will be held during the 2018 Biotechnology Innovation Organization (BIO) World Congress on Industrial Biotechnology from July 16 through 19 at the Pennsylvania Convention Center in Philadelphia. The annual Carver award recognizes an individual who has made a significant contribution to building the bio-based economy by applying industrial biotechnology to create environmentally sustainable products. It serves as a lasting memorial to the original vision of George Washington Carver who, over a century ago, pioneered bio-based products, materials, and energy derived from renewable agricultural feedstock. Previous recipients include the founder and CEO of POET Jeff Broin, the CEO of DuPont Ellen Kullman, and Professor Gregory Stephanopoulos at MIT. Professor Lee is a pioneering scholar of systems metabolic engineering, leveraging technology to develop microbial bioprocesses for the sustainable and environment-friendly production of chemicals, fuels, and materials from non-food renewable biomass. He also serves as the dean of the multi-and interdisciplinary research center hub, KAIST Institute.Through his work, Professor Lee has garnered countless achievements, including being one of only 13 people in the world elected as a foreign member of both the National Academy of Sciences USA and the National Academy of Engineering USA. He has actively promoted the importance of industrial biotechnology through engagement with the public, policymakers, and decision makers around the world. He currently serves as the co-chairman of the Global Future Council on Biotechnology for the World Economic Forum and served as the Chairman of the Emerging Technologies Council and Biotechnology Council for the World Economic Forum. Upon the award announcement, Dr. Brent Erickson, executive vice president of BIO’s Industrial & Environmental Section lauded Professor Lee’s achievement, saying “Dr. Lee has advanced the bio-based economy by developing innovative products and processes that are sustainable and environmentally friendly. In doing so, he has become a leader in advocating on the importance of industrial biotechnology. His contributions to the advancement of the industry are a continuation of the legacy left behind by George Washington Carver.” Professor Lee thanked his research team who has worked together for the past few decades, adding, “Industrial biotechnology is becoming increasingly important to help achieve the UN’s Sustainable Development Goals. We should continue to work together to advance the field and establish a solid foundation for the sustainable future.” The George Washington Carver Award is sponsored by the Iowa Biotechnology Association. Joe Hrdlicka, executive director of the Iowa Biotechnology Association, said, “Dr. Sang Yup Lee’s significant contributions to the advancement of industrial biotechnology make him the perfect recipient for the George Washington Carver Award. Having published more than 575 peer-reviewed papers, contributed to 82 books, and holding 636 patents, the culmination of Dr. Lee’s work has led to the establishment of sustainable systems for bio-based production of chemicals, fuels, and materials, thus reducing environmental impact and improving quality of life for all.”
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