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A Genetically Encoded Biosensor for the Detection of Levulinic Acid
Kim Tae Hyun,Woo Seung-Gyun,Kim Seong Keun,Yoo Byeong Hyeon,Shin Jonghyeok,Rha Eugene,Kim Soo Jung,Kwon Kil Koang,Lee Hyewon,Kim Haseong,Kim Hee-Taek,Sung Bong-Hyun,Lee Seung-Goo,Lee Dae-Hee 한국미생물·생명공학회 2023 Journal of microbiology and biotechnology Vol.33 No.4
Levulinic acid (LA) is a valuable chemical used in fuel additives, fragrances, and polymers. In this study, we proposed possible biosynthetic pathways for LA production from lignin and poly(ethylene terephthalate). We also created a genetically encoded biosensor responsive to LA, which can be used for screening and evolving the LA biosynthesis pathway genes, by employing an LvaR transcriptional regulator of Pseudomonas putida KT2440 to express a fluorescent reporter gene. The LvaR regulator senses LA as a cognate ligand. The LA biosensor was first examined in an Escherichia coli strain and was found to be non-functional. When the host of the LA biosensor was switched from E. coli to P. putida KT2440, the LA biosensor showed a linear correlation between fluorescence intensity and LA concentration in the range of 0.156–10 mM LA. In addition, we determined that 0.156 mM LA was the limit of LA detection in P. putida KT2440 harboring an LA-responsive biosensor. The maximal fluorescence increase was 12.3-fold in the presence of 10 mM LA compared to that in the absence of LA. The individual cell responses to LA concentrations reflected the population-averaged responses, which enabled high-throughput screening of enzymes and metabolic pathways involved in LA biosynthesis and sustainable production of LA in engineered microbes.
Kim, Jaehee,Ogden, Robert Todd,Kim, Haseong BioMed Central 2013 BMC bioinformatics Vol.14 No.-
<P><B>Background</B></P><P>Time course gene expression experiments are an increasingly popular method for exploring biological processes. Temporal gene expression profiles provide an important characterization of gene function, as biological systems are both developmental and dynamic. With such data it is possible to study gene expression changes over time and thereby to detect differential genes. Much of the early work on analyzing time series expression data relied on methods developed originally for static data and thus there is a need for improved methodology. Since time series expression is a temporal process, its unique features such as autocorrelation between successive points should be incorporated into the analysis.</P><P><B>Results</B></P><P>This work aims to identify genes that show different gene expression profiles across time. We propose a statistical procedure to discover gene groups with similar profiles using a nonparametric representation that accounts for the autocorrelation in the data. In particular, we first represent each profile in terms of a Fourier basis, and then we screen out genes that are not differentially expressed based on the Fourier coefficients. Finally, we cluster the remaining gene profiles using a model-based approach in the Fourier domain. We evaluate the screening results in terms of sensitivity, specificity, FDR and FNR, compare with the Gaussian process regression screening in a simulation study and illustrate the results by application to yeast cell-cycle microarray expression data with alpha-factor synchronization.</P><P>The key elements of the proposed methodology: (i) representation of gene profiles in the Fourier domain; (ii) automatic screening of genes based on the Fourier coefficients and taking into account autocorrelation in the data, while controlling the false discovery rate (FDR); (iii) model-based clustering of the remaining gene profiles.</P><P><B>Conclusions</B></P><P>Using this method, we identified a set of cell-cycle-regulated time-course yeast genes. The proposed method is general and can be potentially used to identify genes which have the same patterns or biological processes, and help facing the present and forthcoming challenges of data analysis in functional genomics.</P>
A portable FRET analyzer for rapid detection of sugar content
Kim, Haseong,Kim, Hyo Sang,Ha, Jae-Seok,Lee, Seung-Goo The Royal Society of Chemistry 2015 The Analyst Vol.140 No.10
<P>Fluorescence resonance energy transfer (FRET) is widely used as a core process in biometric sensors to detect small molecules such as sugars, calcium ions, or amino acids. However, FRET based biosensors with innate weak signal intensity require the use of expensive, high-sensitive equipment. In the present study, these shortcomings were overcome with the fabrication of a sensitive, inexpensive, and portable analyzer which provides quantitative detection of small molecules in a liquid sample. The usability of the developed analyzer was successfully tested by measuring sucrose and maltose contents in commercially available beverage samples, with better performance than the conventional monochromator-type spectrofluorometer. It is anticipated that miniaturization of the equipment and improving the FRET based biosensors will contribute to the practical use of this hand-held analyzer in conditions where high-end equipment is not available.</P> <P>Graphic Abstract</P><P>The proposed hand-held FRET analyzer measures sucrose and maltose contents with better performance than the conventional monochromator-type spectrofluorometer. <IMG SRC='http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=c4an02195a'> </P>
Inference of large-scale gene regulatory networks using regression-based network approach.
Kim, Haseong,Lee, Jae K,Park, Taesung Imperial College Press ; World Scientific Publishi 2009 JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOG Vol.7 No.4
<P>The gene regulatory network modeling plays a key role in search for relationships among genes. Many modeling approaches have been introduced to find the causal relationship between genes using time series microarray data. However, they have been suffering from high dimensionality, overfitting, and heavy computation time. Further, the selection of a best model among several possible competing models is not guaranteed that it is the best one. In this study, we propose a simple procedure for constructing large scale gene regulatory networks using a regression-based network approach. We determine the optimal out-degree of network structure by using the sum of squared coefficients which are obtained from all appropriate regression models. Through the simulated data, accuracy of estimation and robustness against noise are computed in order to compare with the vector autoregressive regression model. Our method shows high accuracy and robustness for inferring large-scale gene networks. Also it is applied to Caulobacter crescentus cell cycle data consisting of 1472 genes. It shows that many genes are regulated by two transcription factors, ctrA and gcrA, that are known for global regulators.</P>
AI, big data, and robots for the evolution of biotechnology
Kim, Haseong Korea Genome Organization 2019 Genomics & informatics Vol.17 No.4
Artificial intelligence (AI), big data, and ubiquitous robotic companions -the three most notable technologies of the 4th Industrial Revolution-are receiving renewed attention each day. Technologies that can be experienced in daily life, such as autonomous navigation, real-time translators, and voice recognition services, are already being commercialized in the field of information technology. In the biosciences field in Korea, such technologies have become known to the local public with the introduction of the AI doctor Watson in large number of hospitals. Additionally, AlphaFold, a technology resembling the AI AlphaGo for the game Go, has surpassed the limit on protein folding predictions-the most challenging problems in the field of protein biology. This report discusses the significance of AI technology and big data on the bioscience field. The introduction of automated robots in this field is not just only for the purpose of convenience but a prerequisite for the real sense of AI and the consequent accumulation of basic scientific knowledge.