Engineering
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One-Step Production of Aromatic Polyesters by E. coli Strains
KAIST systems metabolic engineers defined a novel strategy for microbial aromatic polyesters production fused with synthetic biology from renewable biomass. The team of Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering produced aromatic polyesters from Escherichia coli (E. coli) strains by applying microbial fermentation, employing direct microbial fermentation from renewable feedstock carbohydrates.
This is the first report to determine a platform strain of engineered E. coli capable of producing environmentally friendly aromatic polyesters. This engineered E. coli strain, if desired, has the potential to be used as a platform strain capable of producing various high-valued aromatic polyesters from renewable biomass. This research was published in Nature Communications on January 8.
Conventionally, aromatic polyesters boast solid strength and heat stability so that there has been a great deal of interest in fermentative production of aromatic polyesters from renewable non-food biomass, but without success.
However, aromatic polyesters are only made by feeding the cells with corresponding aromatic monomers as substrates, and have not been produced by direct fermentation from renewable feedstock carbohydrates such as glucose.
To address this issue, the team prescribed the detailed procedure for aromatic polyester production through identifying CoA-transferase that activates phenylalkanoates into their corresponding CoA derivatives. In this process, researchers employed metabolic engineering of E. coli to produce phenylalkanoates from glucose based on genome-scale metabolic flux analysis. In particular, the KAIST team made a modulation of gene expression to produce various aromatic polyesters having different monomer fractions.
The research team successfully produced aromatic polyesters, a non-natural polymer using the strategy that combines systems metabolic engineering and synthetic biology. They succeeded in biosynthesis of various kinds of aromatic polyesters through the system, thus proving the technical excellence of the environmentally friendly biosynthetic system of this research. Furthermore, his team also proved the potential of expanding the range of aromatic polyesters from renewable resources, which is expected to play an important role in the bio-plastic industry.
Professor Lee said, “An eco-friendly and sustainable chemical industry is the key global agenda every nation faces. We are making a research focus to a biochemical industry free from petroleum dependence, and conducting diverse research activities to address the issue. This novel technology we are presenting will serve as an opportunity to advance the biochemical industry moving forward.”
This work was supported by the Intelligent Synthetic Biology Center through the Global Frontier Project (2011-0031963) and also by the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries (NRF-2012M1A2A2026556 and NRF-2012M1A2A2026557) from the Ministry of Science and ICT through the National Research Foundation of Korea.
Figure: Biosynthesis of aromatic polyesters by metabolically engineered E. coli.This schematic diagram shows the overall conceptualization of how metabolically engineered E. coli produced aromatic polyesters from glucose.
2018.01.09 View 6783 -
Fiber OLEDs, Thinner Than a Hair
(Seonil Kwon, PhD Candidate)
Professor Kyung Cheol Choi from the School of Electrical Engineering and his team succeeded in fabricating highly efficient Organic Light-Emitting Diodes (OLEDs) on an ultra-thin fiber.
The team expects the technology, which produces high-efficiency, long-lasting OLEDs, can be widely utilized in wearable displays.
Existing fiber-based wearable displays’ OLEDs show much lower performance compared to those fabricated on planar substrates. This low performance caused a limitation for applying it to actual wearable displays.
In order to solve this problem, the team designed a structure of OLEDs compatible to fiber and used a dip-coating method in a three-dimensional structure of fibers. Through this method, the team successfully developed efficient OLEDs that are designed to last a lifetime and are still equivalent to those on planar substrates.
The team identified that solution process planar OLEDs can be applied to fibers without any reduction in performance through the technology. This fiber OLEDs exhibited luminance and current efficiency values of over 10,000 cd/m^2(candela/square meter) and 11 cd/A (candela/ampere).
The team also verified that the fiber OLEDs withstood tensile strains of up to 4.3% while retaining more than 90% of their current efficiency. In addition, they could be woven into textiles and knitted clothes without causing any problems.
Moreover, the technology allows for fabricating OLEDs on fibers with diameters ranging from 300㎛ down to 90㎛, thinner than a human hair, which attests to the scalability of the proposed fabrication scheme.
Noting that every process is carried out at a low temperature (~105℃), fibers vulnerable to high temperatures can also employ this fabrication scheme.
Professor Choi said, “Existing fiber-based wearable displays had limitations for applicability due to their low performance. However, this technology can fabricate OLEDs with high performance on fibers. This simple, low-cost process opens a way to commercialize fiber-based wearable displays.”
This research led by a PhD candidate Seonil Kwon was published online in the international journal for nanoscience, Nano Letters, on December 6.
(Fiber-based OLEDs woven into knitted clothes)
This work was funded by the Engineering Research Center of Excellence Program (Grant No. NRF-2017R1A5A1014708) and Nano-Material Technology Development Program (Grant No. NRF-2016M3A7B4910635) by the National Research Foundation of Korea, the Ministry of Science and ICT of Korea.
2018.01.09 View 13419 -
Ultra-Low Power Flexible Memory Using 2D Materials
(Professor Choi and Ph.D. candidate Jang)
KAIST research team led by Professor Sung-Yool Choi at School of Electrical Engineering and Professor Sung Gap Im at the Department of Chemical and Biomolecular Engineering developed high-density, ultra-low power, non-volatile, flexible memory technology using 2D materials. The team used ultrathin molybdenum disulfide (MoS2) with atomic-scale thickness as the channel material and high-performance polymeric insulator film as the tunneling dielectric material. This research was published on the cover of Advanced Functional Materials on November 17. KAIST graduate Myung Hun Woo, a researcher at Samsung Electronics and Ph.D. candidate Byung Chul Jang are first authors.
The surge of new technologies such as Internet of Things (IoT), Artificial Intelligence (AI), and cloud server led to the paradigm shift from processor-centric computing to memory-centric computing in the industry, as well as the increase in demand of wearable devices. This led to an increased need for high-density, ultra-low power, non-volatile flexible memory. In particular, ultrathin MoS2 as semiconductor material has been recently regarded as post-silicon material. This is due to its ultrathin thickness of atomic-scale which suppresses short channel effect observed in conventional silicon material, leading to advantages in high- density and low-power consumption. Further, this thickness allows the material to be flexible, and thus the material is applicable to wearable devices.
However, due to the dangling-bond free surface of MoS2 semiconductor material, it is difficult to deposit the thin insulator film to be uniform and stable over a large area via the conventional atomic layer deposition process. Further, the currently used solution process makes it difficult to deposit uniformly low dielectric constant (k) polymeric insulator film with sub-10 nm thickness on a large area, thus indicating that the memory device utilizing the conventional solution-processed polymer insulator film cannot be operated at low-operating voltage and is not compatible with photolithography.
The research team tried to overcome the hurdles and develop high-density, ultra-low power, non-volatile flexible memory by employing a low-temperature, solvent-free, and all-dry vapor phase technique named initiated chemical vapor deposition (iCVD) process. Using iCVD process, tunneling polymeric insulator film with 10 nm thickness was deposited uniformly on MoS2 semiconductor material without being restricted by the dangling bond-free surface of MoS2. The team observed that the newly developed MoS2-based non-volatile memory can be operated at low-voltage (around 10V), in contrast to the conventional MoS2-based non-volatile memory that requires over 20V.
Professor Choi said, “As the basis for the Fourth Industrial revolution technologies including AI and IoT, semiconductor device technology needs to have characteristics of low-power and flexibility, in clear contrast to conventional memory devices.” He continued, “This new technology is significant in developing source technology in terms of materials, processes, and devices to contribute to achieve these characteristics.”
This research was supported by the Global Frontier Center for Advanced Soft Electronics and the Creative Materials Discovery Program by funded the National Research Foundation of Korea of Ministry of Science and ICT.
( Figure 1. Cover of Advanced Functional Materials)
(Figure 2. Concept map for the developed non-volatile memory material and high-resolution transmission electron microscopy image for material cross-section )
2018.01.02 View 7997 -
Technology to Find Optimum Drug Target for Cancer Developed
(Professor Kwang-Hyun Cho (right) and lead author Dr. Minsoo Choi)
A KAIST research team led by Professor Kwang-Hyun Cho of the Department of Bio and Brain Engineering developed technology to find the optimum drug target according to the type of cancer cell. The team used systems biology to analyze molecular network dynamics that reflect genetic mutations in cancer cells and to predict drug response. The technology could contribute greatly to future anti-cancer drug development.
There are many types of genetic variations found in cancer cells, including gene mutations and copy number variations. These variations differ in cancer cells even within the same type of cancer, and thus the drug response varies cell by cell. Cancer researchers worked towards identifying frequently occurring genetic variations in cancer patients and, in particular, the mutations that can be used as an index for specific drugs. Previous studies focused on identifying a single genetic mutation or creating an analysis of the structural characteristics of a gene network. However, this approach was limited in its inability to explain the biological properties of cancer which are induced by various gene and protein interactions in cancer cells, which result in differences in drug response.
Gene mutations in cancer cells not only affect the function of the affected gene, but also other genes that interact with the mutated gene and proteins. As a consequence, one mutation could lead to changes in the dynamical properties of the molecular network. Therefore, the responses to anti-cancer drugs by cancer cells differ. The current treatment approach that ignores molecular network dynamics and targets a few cancer-related genes is only effective on a fraction of patients, while many other patients exhibit resistance to the drug.
Professor Cho’s team integrated a large-scale computer simulation using super-computing and cellular experiments to analyze changes in molecular network dynamics in cancer cells.
This led to development of technology to find the optimum drug target according to the type of cancer cells by predicting drug response. This technology was applied to the molecular network of known tumor suppressor p53. The team used large-scale cancer cell genomic data available from The Cancer Cell Line Encyclopedia (CCLE) to construct different molecular networks specific to the characteristics of genetic variations.
Perturbation analysis on drug response in each molecular network was used to quantify changes in cancer cells from drug response and similar networks were clustered. Then, computer simulations were used to analyze the synergetic effects in terms of efficacy and combination to predict the level of drug response. Based on the simulation results from various cancer cell lines including lung, breast, bone, skin, kidney, and ovary cancers were used in drug response experiments for compare analysis.
This technique can be applied in any molecular network to identify the optimum drug target for personalized medicine.
The research team suggests that the technology can analyze varying drug response due to the heterogeneity of cancer cells by considering the overall modulatory interactions rather than focusing only on a specific gene or protein. Further, the technology aids the prediction of causes of drug resistance and thus the identification of the optimum drug target to inhibit the resistance. This could be core source technology that can be used in drug repositioning, a process of applying existing drugs to new disease targets.
Professor Cho said, “Genetic variations in cancer cells are the cause of diverse drug response, but a complete analysis had not yet been made.” He continued, “Systems biology allowed the simulation of drug responses by cancer cell molecular networks to identify fundamental principles of drug response and optimum drug targets using a new conceptual approach.”
This research was published in Nature Communications on December 5 and was funded by Ministry of Science and ICT and National Research Foundation of Korea.
(Figure 1. Drug response prediction for each cancer cell type from computer simulation and cellular experiment verification for comparison)
(Figure 2. Drug response prediction based on cancer cell molecular network dynamics and clustering of cancer cells by their molecular networks)
(Figure 3. Identification of drug target for each cancer cell type by cellular molecular network analysis and establishment for personalized medicine strategy for each cancer patient)
2017.12.15 View 6416 -
Distinguished Professor Sang Yup Lee Named NAI Fellow
(Distinguished Professor Sang Yup Lee)
Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering was named to the National Academy of Inventors in the US. He is the first Korean scholar ever elected as a NAI fellow.
The NAI is a non-profit member organization with over 4,000 individual inventors and fellows spanning more than 250 institutions worldwide. It is comprised of universities as well as governmental and non-profit research institutes. The academy was founded in 2010 to recognize and encourage inventors with patents from the US Patent and Trademark Office. So far, 575 fellows from 229 institutions have been elected.
The academy said Professor Lee has been recognized for fellowship induction as he has demonstrated a highly prolific spirit of innovation in creating or facilitating outstanding inventions that have made a tangible impact on quality of life, economic development, and the welfare of society.
Distinguished Professor Lee, a pioneering researcher and scholar in the field of systems metabolic engineering, was ranked in the top 1% of highly cited researchers (HCR) this year. Over the past 11 years, he published more than 130,000 articles in prestigious journals around the world. He has been cited more than 34,000 times since he started working at KAIST in 1994.
He is also the first Korean ever elected to both the National Academy of Sciences (NAS) and the National Academy of Engineering (NAE) in the US, becoming the one of 13 foreign scholars in the world holding two prestigious institutions’ fellowships.
Dr. Lee is currently the dean of KAIST Institutes, the world-leading institute for multi and interdisciplinary research. He is also serving as co-chair of the Global Council on Biotechnology and is a member of the Global Future Council on the Fourth Industrial Revolution at the World Economic Forum.
2017.12.13 View 7228 -
Hubo Completes New Mission at the Winter Olympic Torch Relay
KAIST-born humanoid robot, Hubo, completed its special new mission: carrying the Olympic torch. The Winter Olympics will be held in PyeongChang for two weeks beginning February 9.
On December 11, the final leg of the torch relay in Daejeon for the PyeongChang Olympics 2018 took place inside KAIST. A city known for science and technology hosted special torch relay runners over three days.
Hubo arrived at the campus with Dr. Dennis Hong, a professor from the University of California at Los Angeles, in an autonomous vehicle. Then, Hubo received the flame from Professor Hong. Hubo, a robot developed by Professor Jun Ho Oh from the Department of Mechanical Engineering at KAIST, is best known for being the winner of the DARPA Robotics Challenge in 2015.
Hubo successfully completed its Olympic mission. That is, it had to drill through a wall to deliver the torch to the next runner. After completing the mission successfully, the torch was passed to Professor Oh. He ran a few steps and handed it over to the last runner of the Daejeon leg.
The last runner was Jung Jae Lee, who is a winning team member of the Samsung Junior Software Cup. Lee also had the honor of riding and controlling FX-2 which is another robot developed by Professor Oh for this peace torch relay. FX-2 took a few steps to finalize the relay.
Lee said, “I would like to become an expert in security. As I was riding the robot, I felt every step I took was one step closer to achieving of making major developments in the field of security.
Professor Oh said, “It is meaningful to see humans and robots cooperating with each other to carry out the torch relay.”
The torch relay, participated in by both humans and robots in Daejeon, was successfully completed and the torch headed off to Boryeong, Chungcheongnam-do.
2017.12.12 View 9221 -
A New Spin Current Generating Material Developed
(Professor Park(left) and Ph.D. candidate Kim)
Magnetic random-access memory (MRAM) is a non-volatile device made of thin magnetic film that can maintain information without an external power supply, in contrast to conventional silicon-based semiconductor memory. It also has the potential for high-density integration and high-speed operation.
The operation of MRAM involves the control of the magnetization direction by exerting spin current-induced torque on a magnetic material. Spin current is generated using electricity in conventional MRAM, but this study developed materials technology that generates spin current using heat.
A KAIST research team led by Professor Byong-Guk Park of the Department of Materials Science and Engineering developed a material that generates spin current from heat, which can be utilized for a new operation principle for MRAM.
There have been theoretical reports on the spin Nernst effect, the phenomenon of the thermal generation of spin current, but is yet to have been experimentally proven due to technological limitations. However, the research team introduced a spin Nernst magnetoresistance measurement method using tungsten (W) and platinum (Pt) with high spin orbit coupling which allows for the experimental identification of the spin Nernst effect. They also demonstrated that the efficiency of spin current generation from heat is similar to that of spin current generated from electricity.
Professor Park said, “This research has great significance in experimentally proving spin current generation from heat, a new physical phenomenon. We aim to develop the technology as a new operational method for MRAM through further research. This can lower power consumption, and is expected to contribute to the advancement of electronics requiring low power requirement such as wearable, mobile, and IOT devices”.
This research was conducted as a joint research project with Professor Kyung-Jin Lee at Korea University and Professor Jong-Ryul Jeong at Chungnam National University. It was published in Nature Communications online on November 9 titled “Observation of transverse spin Nernst magnetoresistance induced by thermal spin current in ferromagnet/non-magnet bilayers.” Ph.D. candidate Dong-Jun Kim at KAIST is the first author. This research was funded by the Ministry of Science and ICT.
(Schematic diagram of spin Nernst magnetoresistance)
(Research result of new spin current generating materials)
2017.12.08 View 7247 -
Professor Dong Ho Cho Awarded at the Haedong Conference 2017
Professor Dong Ho Cho of the School of Electrical Engineering at KAIST received an award at the 13th Haedong Conference 2017 in Seoul on the first of December.
The Korean Institute of Communications and Information Sciences recognized Professor Cho for his significant contributions in the field of mobile communication networks. He has carried out groundbreaking research on mobile systems, including architecture, protocols, algorithms, optimization, and efficiency analysis.
As a result, he has produced 73 papers in renowned international journals, 138 papers at international conferences, and filed 52 international patents and 121 domestic patents. In addition, he transferred 14 of the patents he filed to Korean and international companies.
2017.12.07 View 5199 -
Expanding Gas Storage Capacity of Nanoporous Materials
A KAIST research team led by Professor Jihan Kim of the Department of Chemical and Biomolecular Engineering has successfully proposed a rational defect engineering methodology that can greatly enhance the gas storage capacity of nanoporous materials. The team conducted a high-throughput computational screening of a large experimental metal-organic framework database to identify 13 candidate materials that could experience significant methane uptake enhancement with only a small proportion of linker vacancy defects.
This research was published online on November 16 in Nature Communications, with M.S. candidate Sanggyu Chong from KAIST as the first author and post-doctorate researcher Günther Thiele from the Department of Chemistry at UC Berkeley as a contributing author.
Metal-organic frameworks, hereinafter MOF, are crystalline nanoporous materials that are comprised of metal clusters and organic linkers continuously bound together by coordination bonds. Due to their ultrahigh surface areas and pore volumes, they have been widely studied for various energy and environment applications.
Similar to other crystalline materials, MOFs are never perfectly crystalline and are likely to contain several different types of defects within their crystalline structures. Among these defects, linker vacancy defects, or the random absence of linker vacancies in their designated bonding positions, are known to be controllable by practicing careful control over the synthesis conditions.
The research team combined the concepts of rational defect engineering over the linker vacancy defects and the potential presence of inaccessible pores within MOFs to propose a methodology where controlled the introduction of linker vacancy defects could lead to a dramatic enhancement in gas adsorption and storage capacities.
The study utilized a Graphic Processing Unit (GPU) code developed by Professor Kim in a high-throughput computational screening of 12,000 experimentally synthesized MOFs to identify the structures with significant amounts of pores that were inaccessible for methane. In determining the presence of inaccessible pores, a flood-fill algorithm was performed over the energy-low regions of the structure, which is the same algorithm used for filling an area with color in Microsoft Paint.
For the MOFs with significant amounts of inaccessible pores, as determined from the screening, the research team emulated linker vacancy defects in their crystalline structures so that the previously inaccessible pores would be newly merged into the main adsorption channel with the introduction of defects for additional surface area and pore volume available for adsorption. The research team successfully identified 13 structures that would experience up to a 55.56% increase in their methane uptake with less than 8.33% of the linker vacancy defects.
The research team believes that this rational defect engineering scheme can be further utilized for many other applications in areas such as selective adsorption of an adsorbate from a gas mixture and the semi-permanent capture of gas molecules.
This research was conducted with the support of the Mid-career Research Program of the National Research Foundation of Korea.
Figure1. A diagram for flood fill algorithm and example of identification of inaccessible regions within the MOFs, using the flood fill algorithm
Figure2. Methane energy contours before and after detect introduction
2017.12.04 View 7147 -
Technology Detecting RNase Activity
(Ph.D. candidate Chang Yeol Lee)
A KAIST research team of Professor Hyun Gyu Park at Department of Chemical and Biomolecular Engineering developed a new technology to detect the activity of RNase H, a RNA degrading enzyme. The team used highly efficient signal amplification reaction termed catalytic hairpin assembly (CHA) to effectively analyze the RNase H activity. Considering that RNase H is required in the proliferation of retroviruses such as HIV, this research finding could contribute to AIDS treatments in the future, researchers say.
This study led by Ph.D. candidates Chang Yeol Lee and Hyowon Jang was chosen as the cover for Nanoscale (Issue 42, 2017) published in 14 November.
The existing techniques to detect RNase H require expensive fluorophore and quencher, and involve complex implementation. Further, there is no way to amplify the signal, leading to low detection efficiency overall. The team utilized CHA technology to overcome these limitations. CHA amplifies detection signal to allow more sensitive RNase H activity assay.
The team designed the reaction system so that the product of CHA reaction has G-quadruplex structures, which is suitable to generate fluorescence. By using fluorescent molecules that bind to G-quadruplexes to generate strong fluorescence, the team could develop high performance RNase H detection method that overcomes the limitations of existing techniques. Further, this technology could screen inhibitors of RNase H activity.
The team expects that the research finding could contribute to AIDS treatment. AIDS is disease caused by HIV, a retrovirus that utilizes reverse transcription, during which RNA is converted to DNA. RNase H is essential for reverse transcription in HIV, and thus inhibition of RNase H could in turn inhibit transcription of HIV DNA.
Professor Park said, “This technology is applicable to detect various enzyme activities, as well as RNase H activity.” He continued, “I hope this technology could be widely used in research on enzyme related diseases.”
This study was funded by Global Frontier project and Mid-career Researcher Support project of the Ministry of Science and ICT.
2017.11.28 View 6197 -
Professor Je-Kyun Park, Awarded by The Korean BioChip Society
On November 9, Je-Kyun Park from the Department of Bio and Brain Engineering at KAIST received an award from the 2017 Fall Meeting of The Korean BioChip Society held in Paradise Hotel Busan, Korea. This year’s meeting recognized Professor Park for developing lab-on-a-chip and microfluidic analytical technologies.
The Korean BioChip Society is a corporation of biochip professional established in 2006 for the development of biochip technology. Every year, the Society selects a recipient based on the nominees’ academic achievements and contributions to bio-fusion industry.
Professor Park served on the international editorial boards of renowned international journals in related fields, including Biosensors and Bioelectronics and Lab on a Chip. He was also the Committee Chairman of MicroTas in 2015.
2017.11.22 View 6788 -
Mutant Gene Network in Colon Cancer Identified
The principles of the gene network for colon tumorigenesis have been identified by a KAIST research team. The principles will be used to find the molecular target for effective anti-cancer drugs in the future. Further, this research gained attention for using a systems biology approach, which is an integrated research area of IT and BT.
The KAIST research team led by Professor Kwang-Hyun Cho for the Department of Bio and Brain Engineering succeeded in the identification. Conducted by Dr. Dongkwan Shin and student researchers Jonghoon Lee and Jeong-Ryeol Gong, the research was published in Nature Communications online on November 2.
Human cancer is caused by genetic mutations. The frequency of the mutations differs by the type of cancer; for example, only around 10 mutations are found in leukemia and childhood cancer, but an average of 50 mutations are found in adult solid cancers and even hundreds of mutations are found in cancers due to external factors, such as with lung cancer.
Cancer researchers around the world are working to identify frequently found genetic mutations in patients, and in turn identify important cancer-inducing genes (called ‘driver genes’) to develop targets for anti-cancer drugs. However, gene mutations not only affect their own functions but also affect other genes through interactions. Therefore, there are limitations in current treatments targeting a few cancer-inducing genes without further knowledge on gene networks, hence current drugs are only effective in a few patients and often induce drug resistance.
Professor Cho’s team used large-scale genomic data from cancer patients to construct a mathematical model on the cooperative effects of multiple genetic mutations found in gene interaction networks. The basis of the model construction was The Cancer Genome Atlas (TCGA) presented at the International Cancer Genome Consortium. The team successfully quantified the effects of mutations in gene networks to group colon cancer patients by clinical characteristics.
Further, the critical transition phenomenon that occurs in tumorigenesis was identified using large-scale computer simulation analysis, which was the first hidden gene network principle to be identified. Critical transition is the phenomenon in which the state of matter is suddenly changed through phase transition. It was not possible to identify the presence of transition phenomenon in the past, as it was difficult to track the sequence of gene mutations during tumorigenesis.
The research team used a systems biology-based research method to find that colon cancer tumorigenesis shows a critical transition phenomenon if the known driver gene mutations follow sequentially. Using the developed mathematical model, it can be possible to develop a new anti-cancer targeting drug that most effectively inhibits the effects of many gene mutations found in cancer patients. In particular, not only driver genes, but also other passenger genes affected by the gene mutations, could be evaluated to find the most effective drug targets.
Professor Cho said, “Little was known about the contribution of many gene mutations during tumorigenesis.” He continued, “In this research, a systems biology approach identified the principle of gene networks for the first time to suggest the possibility of anti-cancer drug target identification from a new perspective.”
This research was funded by the Ministry of Science and ICT and the National Research Foundation of Korea.
Figure1. Formation of giant clusters via mutation propagation
Figure2. Critical transition phenomenon by cooperative effect of mutations in tumorigenesis
2017.11.10 View 7339