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KAIST to join Deep Space Exploration Project
KAIST agreed to launch the Deep Space Exploration Research Consortium with two key leading aerospace research institutes, the Korea Aerospace Research Institute (KARI) and the Korea Astronomy and Space Science Institute (KASI) during a recent meeting at the KAIST campus. President Kwang Hyung Lee, KARI President Sang-Yool Lee, KASI President Young-Deuk Park, and Vice Minister of Science and ICT Hong-taek Yong attended the meeting to discuss medium- and long-term deep space exploration plans and collaborations. The three entities have cooperated in scientific research for the last 30 years during which Korea has been developing its space exploration expertise. They signed the MoU for Cooperation for R&D and Industrialization on Deep Space Exploration’ last December. The research consortium will share and discuss research plans for space science research and exploration technology, and contribute to planning the nation’s deep space exploration. At the meeting, KAIST reported its plans to return KITSAT-1 to Earth, Korea’s first satellite using local technology, and to explore the radiation belt (the Van Allen belt) around Earth. KAIST launched Korea’s first satellite KITSAT-1 in 1992. Meanwhile, KARI shared their plans to launch a lunar landing module using a Korean Space Launch Vehicle by 2030 and explained the current technologies and research related to a lunar landing and space exploration. Based on the payload technology it has been building on for the last 20 years, KASI emphasized the importance of research for deep space exploration in relation to the formation of the universe and the origin of mankind. Vice Minister of Science and Technology Yong also stressed that “to enhance Korea’s capabilities for space research after launching our space launch vehicle, Nuri, in October, there must be continued efforts and preparation for higher level space research, including space exploration planning. The various experts’ opinions discussed in today’s meeting will be taken into consideration for governmental policies related to the ‘National Space Exploration Roadmap’ to be established in the latter half of this year.”
What Guides Habitual Seeking Behavior Explained
A new role of the ventral striatum explains habitual seeking behavior Researchers have been investigating how the brain controls habitual seeking behaviors such as addiction. A recent study by Professor Sue-Hyun Lee from the Department of Bio and Brain Engineering revealed that a long-term value memory maintained in the ventral striatum in the brain is a neural basis of our habitual seeking behavior. This research was conducted in collaboration with the research team lead by Professor Hyoung F. Kim from Seoul National University. Given that addictive behavior is deemed a habitual one, this research provides new insights for developing therapeutic interventions for addiction. Habitual seeking behavior involves strong stimulus responses, mostly rapid and automatic ones. The ventral striatum in the brain has been thought to be important for value learning and addictive behaviors. However, it was unclear if the ventral striatum processes and retains long-term memories that guide habitual seeking. Professor Lee’s team reported a new role of the human ventral striatum where long-term memory of high-valued objects are retained as a single representation and may be used to evaluate visual stimuli automatically to guide habitual behavior. “Our findings propose a role of the ventral striatum as a director that guides habitual behavior with the script of value information written in the past,” said Professor Lee. The research team investigated whether learned values were retained in the ventral striatum while the subjects passively viewed previously learned objects in the absence of any immediate outcome. Neural responses in the ventral striatum during the incidental perception of learned objects were examined using fMRI and single-unit recording. The study found significant value discrimination responses in the ventral striatum after learning and a retention period of several days. Moreover, the similarity of neural representations for good objects increased after learning, an outcome positively correlated with the habitual seeking response for good objects. “These findings suggest that the ventral striatum plays a role in automatic evaluations of objects based on the neural representation of positive values retained since learning, to guide habitual seeking behaviors,” explained Professor Lee. “We will fully investigate the function of different parts of the entire basal ganglia including the ventral striatum. We also expect that this understanding may lead to the development of better treatment for mental illnesses related to habitual behaviors or addiction problems.” This study, supported by the National Research Foundation of Korea, was reported at Nature Communications (https://doi.org/10.1038/s41467-021-22335-5.) -ProfileProfessor Sue-Hyun LeeDepartment of Bio and Brain EngineeringMemory and Cognition Laboratoryhttp://memory.kaist.ac.kr/lecture KAIST
Identification of How Chemotherapy Drug Works Could Deliver Personalized Cancer Treatment
The chemotherapy drug decitabine is commonly used to treat patients with blood cancers, but its response rate is somewhat low. Researchers have now identified why this is the case, opening the door to more personalized cancer therapies for those with these types of cancers, and perhaps further afield. Researchers have identified the genetic and molecular mechanisms within cells that make the chemotherapy drug decitabine—used to treat patients with myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) —work for some patients but not others. The findings should assist clinicians in developing more patient-specific treatment strategies. The findings were published in the Proceedings of the National Academies of Science on March 30. The chemotherapy drug decitabine, also known by its brand name Dacogen, works by modifying our DNA that in turn switches on genes that stop the cancer cells from growing and replicating. However, decitabine’s response rate is somewhat low (showing improvement in just 30-35% of patients), which leaves something of a mystery as to why it works well for some patients but not for others. To find out why this happens, researchers from the KAIST investigated the molecular mediators that are involved with regulating the effects of the drug. Decitabine works to activate the production of endogenous retroviruses (ERVs), which in turn induces an immune response. ERVs are viruses that long ago inserted dormant copies of themselves into the human genome. Decitabine in essence, ‘reactivates’ these viral elements and produces double-stranded RNAs (dsRNAs) that the immune system views as a foreign body. “However, the mechanisms involved in this process, in particular how production and transport of these ERV dsRNAs were regulated within the cell were understudied,” said corresponding author Yoosik Kim, professor in the Department of Chemical and Biomolecular Engineering at KAIST. “So to explain why decitabine works in some patients but not others, we investigated what these molecular mechanisms were,” added Kim. To do so, the researchers used image-based RNA interference (RNAi) screening. This is a relatively new technique in which specific sequences within a genome are knocked out of action or “downregulated.” Large-scale screening, which can be performed in cultured cells or within live organisms, works to investigate the function of different genes. The KAIST researchers collaborated with the Institut Pasteur Korea to analyze the effect of downregulating genes that recognize ERV dsRNAs and could be involved in the cellular response to decitabine. From these initial screening results, they performed an even more detailed downregulation screening analysis. Through the screening, they were able to identify two particular gene sequences involved in the production of an RNA-binding protein called Staufen1 and the production of a strand of RNA that does not in turn produce any proteins called TINCR that play a key regulatory role in response to the drug. Staufen1 binds directly to dsRNAs and stabilizes them in concert with the TINCR. If a patient is not producing sufficient Staufen1 and TINCR, then the dsRNA viral mimics quickly degrade before the immune system can spot them. And, crucially for cancer therapy, this means that patients with lower expression (activation) of these sequences will show inferior response to decitabine. Indeed, the researchers confirmed that MDS/AML patients with low Staufen1 and TINCR expression did not benefit from decitabine therapy. “We can now isolate patients who will not benefit from the therapy and direct them to a different type of therapy,” said first author Yongsuk Ku. “This serves as an important step toward developing a patient-specific treatment cancer strategy.” As the researchers used patient samples taken from bone marrow, the next step will be to try to develop a testing method that can identify the problem from just blood samples, which are much easier to acquire from patients. The team plans to investigate if the analysis can be extended to patients with solid tumors in addition to those with blood cancers. -Profile Professor Yoosik Kim https://qcbio.kaist.ac.kr/ Department of Chemical and Biomolecular Engineering KAIST -Publication Noncanonical immune response to the inhibition of DNA methylation by Staufen1 via stabilization of endogenous retrovirus RNAs, PNAS
Dr. Won-Joon Lee from the ADD Wins the Jeong Hun Cho Award
Dr. Won-Joon Lee from the Agency for Defense Development (ADD) became the 17th Jeong Hun Cho Award recipient. KAIST PhD candidate Sok-Min Choi from the Department of Aerospace Engineering, Master’s-PhD combined course student Hyong-Won Choi from Korea University, and Chong-Ho Park from Kongju National University High School were also selected. The award recognizes promising young scientists who makes significant achievements in the field of aerospace engineering in honor of Jeong Hun Cho, the former PhD candidate in the Department of Aerospace Engineering who died in a lab accident in May in 2003. Cho’s family endowed the award and scholarship to honor him. Three scholarship recipients from Cho’s alma mater, KAIST, Korea University, and Kongju National High School are selected every year. Dr. Lee from the ADD has conducted research on shape design methods and radar absorbing structures for unmanned aerial vehicles, publishing more than 24 articles in SCI-level journals and 17 at academic conferences. Dr. Lee was awarded 25 million KRW in prize money. The two students from KAIST and Korea University each received a 4 million KRW scholarship and Park received 3 million KRW.
Prof. Sang Yup Lee Elected as a Foreign Member of the Royal Society
Vice President for Research Distinguished Professor Sang Yup Lee was elected as a foreign member of the Royal Society in the UK. On May 6, the Society announced the list of distinguished new 52 fellows and 10 foreign members who achieved exceptional contributions to science. Professor Lee and Professor V. Narry Kim from Seoul National University are the first foreign members ever elected from Korea. The Royal Society, established in 1660, is one of the most prestigious national science academies and a fellowship of 1,600 of the world’s most eminent scientists. From Newton to Darwin, Einstein, Hawking, and beyond, pioneers and paragons in their fields are elected by their peers. To date, there are 280 Nobel prize winners among the fellows. Distinguished Professor Lee from the Department of Chemical and Biomolecular Engineering at KAIST is one of the Highly Cited Researchers (HCRs) who pioneered systems metabolic engineering and developed various micro-organisms for producing a wide range of fuels, chemicals, materials, and natural compounds. His seminal scholarship and research career have already been recognized worldwide. He is the first Korean ever elected into the National Academy of Inventors (NAI) in the US and one of 13 scholars elected as an International Member of both the National Academy of Sciences (NAS) and the National Academy of Engineering (NAE) in the US. With this fellowship, he added one more accolade of being the first non-US and British Commonwealth scientist elected into the three most prestigious science academies: the NAS, the NAE, and the Royal Society.
Professor Byungha Shin Named Scientist of the Month
Professor Byungha Shin from the Department of Materials Science and Engineering won the Scientist of the Month Award presented by the Ministry of Science and ICT (MSIT) and the National Research Foundation of Korea (NRF) on May 4. Professor Shin was recognized for his research in the field of next-generation perovskite solar cells and received 10 million won in prize money. To achieve ‘carbon neutrality,’ which many countries across the globe including Korea hope to realize, the efficiency of converting renewable energies to electricity must be improved. Solar cells convert solar energy to electricity. Since single solar cells show lower efficiency, the development of ‘tandem solar cells’ that connect two or more cells together has been popular in recent years. However, although ‘perovskite’ received attention as a next-generation material for tandem solar cells, it is sensitive to the external environment including light and moisture, making it difficult to maintain stability. Professor Shin discovered that, theoretically, adding certain anion additives to perovskite solar cells would allow the control of the electrical and structural properties of the two-dimensional stabilization layer that forms inside the film. He confirmed this through high-resolution transmission electron microscopy. Controlling the amount of anions in the additives allowed the preservation of over 80% of the initial stability even after 1000 hours of continuous exposure to sunlight. Based on this discovery, Professor Shin combined silicon with solar cells to create a tandem solar cell with 26.7% energy convergence efficiency. Considering that the highest-efficiency tandem solar cell in existence showed 29.5% efficiency, this figure is quite high. Professor Shin’s perovskite solar cell is also combinable with the CIGS (Cu(In,Ga)Se2) thin-film solar cell composed of copper (Cu), indium (In), gallium (Ga), and selenium (Se2). Professor Shin’s research results were published in the online edition of the journal Science in April of last year. “This research is meaningful for having suggested a direction for solar cell material stabilization using additives,” said Professor Shin. “I look forward to this technique being applied to a wide range of photoelectrical devices including solar cells, LEDs, and photodetectors,” he added. (END)
T-GPS Processes a Graph with Trillion Edges on a Single Computer
Trillion-scale graph processing simulation on a single computer presents a new concept of graph processing A KAIST research team has developed a new technology that enables to process a large-scale graph algorithm without storing the graph in the main memory or on disks. Named as T-GPS (Trillion-scale Graph Processing Simulation) by the developer Professor Min-Soo Kim from the School of Computing at KAIST, it can process a graph with one trillion edges using a single computer. Graphs are widely used to represent and analyze real-world objects in many domains such as social networks, business intelligence, biology, and neuroscience. As the number of graph applications increases rapidly, developing and testing new graph algorithms is becoming more important than ever before. Nowadays, many industrial applications require a graph algorithm to process a large-scale graph (e.g., one trillion edges). So, when developing and testing graph algorithms such for a large-scale graph, a synthetic graph is usually used instead of a real graph. This is because sharing and utilizing large-scale real graphs is very limited due to their being proprietary or being practically impossible to collect. Conventionally, developing and testing graph algorithms is done via the following two-step approach: generating and storing a graph and executing an algorithm on the graph using a graph processing engine. The first step generates a synthetic graph and stores it on disks. The synthetic graph is usually generated by either parameter-based generation methods or graph upscaling methods. The former extracts a small number of parameters that can capture some properties of a given real graph and generates the synthetic graph with the parameters. The latter upscales a given real graph to a larger one so as to preserve the properties of the original real graph as much as possible. The second step loads the stored graph into the main memory of the graph processing engine such as Apache GraphX and executes a given graph algorithm on the engine. Since the size of the graph is too large to fit in the main memory of a single computer, the graph engine typically runs on a cluster of several tens or hundreds of computers. Therefore, the cost of the conventional two-step approach is very high. The research team solved the problem of the conventional two-step approach. It does not generate and store a large-scale synthetic graph. Instead, it just loads the initial small real graph into main memory. Then, T-GPS processes a graph algorithm on the small real graph as if the large-scale synthetic graph that should be generated from the real graph exists in main memory. After the algorithm is done, T-GPS returns the exactly same result as the conventional two-step approach. The key idea of T-GPS is generating only the part of the synthetic graph that the algorithm needs to access on the fly and modifying the graph processing engine to recognize the part generated on the fly as the part of the synthetic graph actually generated. The research team showed that T-GPS can process a graph of 1 trillion edges using a single computer, while the conventional two-step approach can only process of a graph of 1 billion edges using a cluster of eleven computers of the same specification. Thus, T-GPS outperforms the conventional approach by 10,000 times in terms of computing resources. The team also showed that the speed of processing an algorithm in T-GPS is up to 43 times faster than the conventional approach. This is because T-GPS has no network communication overhead, while the conventional approach has a lot of communication overhead among computers. Professor Kim believes that this work will have a large impact on the IT industry where almost every area utilizes graph data, adding, “T-GPS can significantly increase both the scale and efficiency of developing a new graph algorithm.” This work was supported by the National Research Foundation (NRF) of Korea and Institute of Information & communications Technology Planning & Evaluation (IITP). Publication: Park, H., et al. (2021) “Trillion-scale Graph Processing Simulation based on Top-Down Graph Upscaling,” Presented at the IEEE ICDE 2021 (April 19-22, 2021, Chania, Greece) Profile: Min-Soo Kim Associate Professor email@example.com http://infolab.kaist.ac.kr School of Computing KAIST
Distinguished Professor Sang Yup Lee Honored with Charles D. Scott Award
Vice President for Research Sang Yup Lee received the 2021 Charles D. Scott Award from the Society for Industrial Microbiology and Biotechnology. Distinguished Professor Lee from the Department of Chemical and Biomolecular Engineering at KAIST is the first Asian awardee. The Charles D. Scott Award, initiated in 1995, recognizes individuals who have made significant contributions to enable and further the use of biotechnology to produce fuels and chemicals. The award is named in honor of Dr. Charles D. Scott, who founded the Symposium on Biomaterials, Fuels, and Chemicals and chaired the conference for its first ten years. Professor Lee has pioneered systems metabolic engineering and developed various micro-organisms capable of producing a wide range of fuels, chemicals, materials, and natural compounds, many of them for the first time. Some of the breakthroughs include the microbial production of gasoline, diacids, diamines, PLA and PLGA polymers, and several natural products. More recently, his team has developed a microbial strain capable of the mass production of succinic acid, a monomer for manufacturing polyester, with the highest production efficiency to date, as well as a Corynebacterium glutamicum strain capable of producing high-level glutaric acid. They also engineered for the first time a bacterium capable of producing carminic acid, a natural red colorant that is widely used for food and cosmetics. Professor Lee is one of the Highly Cited Researchers (HCR), ranked in the top 1% by citations in their field by Clarivate Analytics for four consecutive years from 2017. He is the first Korean fellow ever elected into the National Academy of Inventors in the US and one of 13 scholars elected as an International Member of both the National Academy of Sciences and the National Academy of Engineering in the USA. The awards ceremony will take place during the Symposium on Biomaterials, Fuels, and Chemicals held online from April 26.
Mobile Clinic Module Wins Red Dot and iF Design Awards
The Mobile Clinic Module (MCM), an inflatable negative pressure ward building system developed by the Korea Aid for Respiratory Epidemic (KARE) initiative at KAIST, gained international acclaim by winning the prestigious Red Dot Design Award and iF Design Award. The MCM was recognized as one of the Red Dot Product Designs of the Year. It also won four iF Design Awards in communication design, interior architecture, user interface, and user experience. Winning the two most influential design awards demonstrates how product design can make a valuable contribution to help contain pandemics and reflects new consumer trends for dealing with pandemics. Designed to be patient friendly, even in the extreme medical situations such as pandemics or triage, the MCM is the result of collaborations among researchers in a variety of fields including mechanical engineering, computing, industrial and systems engineering, medical hospitals, and engineering companies. The research team was led by Professor Tek-Jin Nam from the Department of Industrial Design. The MCM is expandable, moveable, and easy to store through a combination of negative pressure frames, air tents, and multi-functional panels. Positive air pressure devices supply fresh air from outside the tent. An air pump and controller maintain air beam pressure, while filtering exhausted air from inside. An internal air information monitoring system efficiently controls inside air pressure and purifies the air. It requires only one-fourth of the volume of existing wards and takes up approximately 40% of their weight. The unit can be transported in a 40-foot container truck. MCMs are now located at the Korea Institute of Radiological & Medical Sciences and Jeju Vaccine Center and expect to be used at many other facilities. KARE is developing antiviral solutions and devices such as protective gear, sterilizers, and test kits to promptly respond to the pandemic. More than 100 researchers at KAIST are collaborating with industry and clinical hospitals to develop antiviral technologies that will improve preventive measures, diagnoses, and treatments. Professor Nam said, “Our designers will continue to identify the most challenging issues, and try to resolve them by realizing user-friendly functions. We believe this will significantly contribute to relieving the drastic need for negative pressure beds and provide a place for monitoring patients with moderate symptoms. We look forward to the MCM upgrading epidemic management resources around the globe.” (END)
Centrifugal Multispun Nanofibers Put a New Spin on COVID-19 Masks
KAIST researchers have developed a novel nanofiber production technique called ‘centrifugal multispinning’ that will open the door for the safe and cost-effective mass production of high-performance polymer nanofibers. This new technique, which has shown up to a 300 times higher nanofiber production rate per hour than that of the conventional electrospinning method, has many potential applications including the development of face mask filters for coronavirus protection. Nanofibers make good face mask filters because their mechanical interactions with aerosol particles give them a greater ability to capture more than 90% of harmful particles such as fine dust and virus-containing droplets. The impact of the COVID-19 pandemic has further accelerated the growing demand in recent years for a better kind of face mask. A polymer nanofiber-based mask filter that can more effectively block harmful particles has also been in higher demand as the pandemic continues. ‘Electrospinning’ has been a common process used to prepare fine and uniform polymer nanofibers, but in terms of safety, cost-effectiveness, and mass production, it has several drawbacks. The electrospinning method requires a high-voltage electric field and electrically conductive target, and this hinders the safe and cost-effective mass production of polymer nanofibers. In response to this shortcoming, ‘centrifugal spinning’ that utilizes centrifugal force instead of high voltage to produce polymer nanofibers has been suggested as a safer and more cost-effective alternative to the electrospinning. Easy scalability is another advantage, as this technology only requires a rotating spinneret and a collector. However, since the existing centrifugal force-based spinning technology employs only a single rotating spinneret, productivity is limited and not much higher than that of some advanced electrospinning technologies such as ‘multi-nozzle electrospinning’ and ‘nozzleless electrospinning.’ This problem persists even when the size of the spinneret is increased. Inspired by these limitations, a research team led by Professor Do Hyun Kim from the Department of Chemical and Biomolecular Engineering at KAIST developed a centrifugal multispinning spinneret with mass-producibility, by sectioning a rotating spinneret into three sub-disks. This study was published as a front cover article of ACS Macro Letters, Volume 10, Issue 3 in March 2021. Using this new centrifugal multispinning spinneret with three sub-disks, the lead author of the paper PhD candidate Byeong Eun Kwak and his fellow researchers Hyo Jeong Yoo and Eungjun Lee demonstrated the gram-scale production of various polymer nanofibers with a maximum production rate of up to 25 grams per hour, which is approximately 300 times higher than that of the conventional electrospinning system. The production rate of up to 25 grams of polymer nanofibers per hour corresponds to the production rate of about 30 face mask filters per day in a lab-scale manufacturing system. By integrating the mass-produced polymer nanofibers into the form of a mask filter, the researchers were able to fabricate face masks that have comparable filtration performance with the KF80 and KF94 face masks that are currently available in the Korean market. The KF80 and KF94 masks have been approved by the Ministry of Food and Drug Safety of Korea to filter out at least 80% and 94% of harmful particles respectively. “When our system is scaled up from the lab scale to an industrial scale, the large-scale production of centrifugal multispun polymer nanofibers will be made possible, and the cost of polymer nanofiber-based face mask filters will also be lowered dramatically,” Kwak explained. This work was supported by the KAIST-funded Global Singularity Research Program for 2020. Publication: Byeong Eun Kwak, Hyo Jeong Yoo, Eungjun Lee, and Do Hyun Kim. (2021) Large-Scale Centrifugal Multispinning Production of Polymer Micro- and Nanofibers for Mask Filter Application with a Potential of Cospinning Mixed Multicomponent Fibers. ACS Macro Letters, Volume No. 10, Issue No. 3, pp. 382-388. Available online at https://doi.org/10.1021/acsmacrolett.0c00829 Profile: Do Hyun Kim, Sc.D. Professor firstname.lastname@example.org http://procal.kaist.ac.kr/ Process Analysis Laboratory Department of Chemical and Biomolecular Engineering https:/kaist.ac.kr/en/ Korea Advanced Institute of Science and Technology (KAIST)Daejeon 34141, Korea (END)
Microbial Production of a Natural Red Colorant Carminic Acid
Metabolic engineering and computer-simulated enzyme engineering led to the production of carminic acid, a natural red colorant, from bacteria for the first time A research group at KAIST has engineered a bacterium capable of producing a natural red colorant, carminic acid, which is widely used for food and cosmetics. The research team reported the complete biosynthesis of carminic acid from glucose in engineered Escherichia coli. The strategies will be useful for the design and construction of biosynthetic pathways involving unknown enzymes and consequently the production of diverse industrially important natural products for the food, pharmaceutical, and cosmetic industries. Carminic acid is a natural red colorant widely being used for products such as strawberry milk and lipstick. However, carminic acid has been produced by farming cochineals, a scale insect which only grows in the region around Peru and Canary Islands, followed by complicated multi-step purification processes. Moreover, carminic acid often contains protein contaminants that cause allergies so many people are unwilling to consume products made of insect-driven colorants. On that account, manufacturers around the world are using alternative red colorants despite the fact that carminic acid is one of the most stable natural red colorants. These challenges inspired the metabolic engineering research group at KAIST to address this issue. Its members include postdoctoral researchers Dongsoo Yang and Woo Dae Jang, and Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering. This study entitled “Production of carminic acid by metabolically engineered Escherichia coli” was published online in the Journal of the American Chemical Society (JACS) on April 2. This research reports for the first time the development of a bacterial strain capable of producing carminic acid from glucose via metabolic engineering and computer simulation-assisted enzyme engineering. The research group optimized the type II polyketide synthase machinery to efficiently produce the precursor of carminic acid, flavokermesic acid. Since the enzymes responsible for the remaining two reactions were neither discovered nor functional, biochemical reaction analysis was performed to identify enzymes that can convert flavokermesic acid into carminic acid. Then, homology modeling and docking simulations were performed to enhance the activities of the two identified enzymes. The team could confirm that the final engineered strain could produce carminic acid directly from glucose. The C-glucosyltransferase developed in this study was found to be generally applicable for other natural products as showcased by the successful production of an additional product, aloesin, which is found in aloe leaves. “The most important part of this research is that unknown enzymes for the production of target natural products were identified and improved by biochemical reaction analyses and computer simulation-assisted enzyme engineering,” says Dr. Dongsoo Yang. He explained the development of a generally applicable C-glucosyltransferase is also useful since C-glucosylation is a relatively unexplored reaction in bacteria including Escherichia coli. Using the C-glucosyltransferase developed in this study, both carminic acid and aloesin were successfully produced from glucose. “A sustainable and insect-free method of producing carminic acid was achieved for the first time in this study. Unknown or inefficient enzymes have always been a major problem in natural product biosynthesis, and here we suggest one effective solution for solving this problem. As maintaining good health in the aging society is becoming increasingly important, we expect that the technology and strategies developed here will play pivotal roles in producing other valuable natural products of medical or nutritional importance,” said Distinguished Professor Sang Yup Lee. This work was supported by the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries of the Ministry of Science and ICT (MSIT) through the National Research Foundation (NRF) of Korea and the KAIST Cross-Generation Collaborative Lab project; Sang Yup Lee and Dongsoo Yang were also supported by Novo Nordisk Foundation in Denmark. Publication: Dongsoo Yang, Woo Dae Jang, and Sang Yup Lee. Production of carminic acid by metabolically engineered Escherichia coli. at the Journal of the American Chemical Society. https://doi.org.10.1021/jacs.0c12406 Profile: Sang Yup Lee, PhD Distinguished Professor email@example.com http://mbel.kaist.ac.kr Metabolic &Biomolecular Engineering National Research Laboratory Department of Chemical and Biomolecular Engineering KAIST
Streamlining the Process of Materials Discovery
The materials platform M3I3 reduces the time for materials discovery by reverse engineering future materials using multiscale/multimodal imaging and machine learning of the processing-structure-properties relationship Developing new materials and novel processes has continued to change the world. The M3I3 Initiative at KAIST has led to new insights into advancing materials development by implementing breakthroughs in materials imaging that have created a paradigm shift in the discovery of materials. The Initiative features the multiscale modeling and imaging of structure and property relationships and materials hierarchies combined with the latest material-processing data. The research team led by Professor Seungbum Hong analyzed the materials research projects reported by leading global institutes and research groups, and derived a quantitative model using machine learning with a scientific interpretation. This process embodies the research goal of the M3I3: Materials and Molecular Modeling, Imaging, Informatics and Integration. The researchers discussed the role of multiscale materials and molecular imaging combined with machine learning and also presented a future outlook for developments and the major challenges of M3I3. By building this model, the research team envisions creating desired sets of properties for materials and obtaining the optimum processing recipes to synthesize them. “The development of various microscopy and diffraction tools with the ability to map the structure, property, and performance of materials at multiscale levels and in real time enabled us to think that materials imaging could radically accelerate materials discovery and development,” says Professor Hong. “We plan to build an M3I3 repository of searchable structural and property maps using FAIR (Findable, Accessible, Interoperable, and Reusable) principles to standardize best practices as well as streamline the training of early career researchers.” One of the examples that shows the power of structure-property imaging at the nanoscale is the development of future materials for emerging nonvolatile memory devices. Specifically, the research team focused on microscopy using photons, electrons, and physical probes on the multiscale structural hierarchy, as well as structure-property relationships to enhance the performance of memory devices. “M3I3 is an algorithm for performing the reverse engineering of future materials. Reverse engineering starts by analyzing the structure and composition of cutting-edge materials or products. Once the research team determines the performance of our targeted future materials, we need to know the candidate structures and compositions for producing the future materials.” The research team has built a data-driven experimental design based on traditional NCM (nickel, cobalt, and manganese) cathode materials. With this, the research team expanded their future direction for achieving even higher discharge capacity, which can be realized via Li-rich cathodes. However, one of the major challenges was the limitation of available data that describes the Li-rich cathode properties. To mitigate this problem, the researchers proposed two solutions: First, they should build a machine-learning-guided data generator for data augmentation. Second, they would use a machine-learning method based on ‘transfer learning.’ Since the NCM cathode database shares a common feature with a Li-rich cathode, one could consider repurposing the NCM trained model for assisting the Li-rich prediction. With the pretrained model and transfer learning, the team expects to achieve outstanding predictions for Li-rich cathodes even with the small data set. With advances in experimental imaging and the availability of well-resolved information and big data, along with significant advances in high-performance computing and a worldwide thrust toward a general, collaborative, integrative, and on-demand research platform, there is a clear confluence in the required capabilities of advancing the M3I3 Initiative. Professor Hong said, “Once we succeed in using the inverse “property−structure−processing” solver to develop cathode, anode, electrolyte, and membrane materials for high energy density Li-ion batteries, we will expand our scope of materials to battery/fuel cells, aerospace, automobiles, food, medicine, and cosmetic materials.” The review was published in ACS Nano in March. This study was conducted through collaborations with Dr. Chi Hao Liow, Professor Jong Min Yuk, Professor Hye Ryung Byon, Professor Yongsoo Yang, Professor EunAe Cho, Professor Pyuck-Pa Choi, and Professor Hyuck Mo Lee at KAIST, Professor Joshua C. Agar at Lehigh University, Dr. Sergei V. Kalinin at Oak Ridge National Laboratory, Professor Peter W. Voorhees at Northwestern University, and Professor Peter Littlewood at the University of Chicago (Article title: Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration).This work was supported by the KAIST Global Singularity Research Program for 2019 and 2020. Publication: “Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics and Integration,” S. Hong, C. H. Liow, J. M. Yuk, H. R. Byon, Y. Yang, E. Cho, J. Yeom, G. Park, H. Kang, S. Kim, Y. Shim, M. Na, C. Jeong, G. Hwang, H. Kim, H. Kim, S. Eom, S. Cho, H. Jun, Y. Lee, A. Baucour, K. Bang, M. Kim, S. Yun, J. Ryu, Y. Han, A. Jetybayeva, P.-P. Choi, J. C. Agar, S. V. Kalinin, P. W. Voorhees, P. Littlewood, and H. M. Lee, ACS Nano 15, 3, 3971–3995 (2021) https://doi.org/10.1021/acsnano.1c00211 Profile: Seungbum Hong, PhD Associate Professor firstname.lastname@example.org http://mii.kaist.ac.kr Department of Materials Science and Engineering KAIST (END)
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