• Biotechnology and Bioprocess Engineering:BBE
• /
• v.10 no.5
• /
• pp.425-431
• /
• 2005

The systems-level analysis of microbes with myriad of heterologous data generated by omics technologies has been applied to improve our understanding of cellular function and physiology and consequently to enhance production of various bioproducts. At the heart of this revolution resides in silico genome-scale metabolic model, In order to fully exploit the power of genome-scale model, a systematic approach employing user-friendly software is required. Metabolic flux analysis of genome-scale metabolic network is becoming widely employed to quantify the flux distribution and validate model-driven hypotheses. Here we describe the development of an upgraded MetaFluxNet which allows (1) construction of metabolic models connected to metabolic databases, (2) calculation of fluxes by metabolic flux analysis, (3) comparative flux analysis with flux-profile visualization, (4) the use of metabolic flux analysis markup language to enable models to be exchanged efficiently, and (5) the exporting of data from constraints-based flux analysis into various formats. MetaFluxNet also allows cellular physiology to be predicted and strategies for strain improvement to be developed from genome-based information on flux distributions. This integrated software environment promises to enhance our understanding on metabolic network at a whole organism level and to establish novel strategies for improving the properties of organisms for various biotechnological applications.

• Molecules and Cells
• /
• v.39 no.2
• /
• pp.141-148
• /
• 2016

Oriental melon (Cucumis melo L. var. makuwa) is one of six subspecies of melon and is cultivated widely in East Asia, including China, Japan, and Korea. Although oriental melon is economically valuable in Asia and is genetically distinct from other subspecies, few reports of genome-scale research on oriental melon have been published. We generated 30.5 and 36.8 Gb of raw RNA sequence data from the female and male flowers, leaves, roots, and fruit of two oriental melon varieties, Korean landrace (KM) and Breeding line of NongWoo Bio Co. (NW), respectively. From the raw reads, 64,998 transcripts from KM and 100,234 transcripts from NW were de novo assembled. The assembled transcripts were used to identify molecular markers (e.g., single-nucleotide polymorphisms and simple sequence repeats), detect tissue-specific expressed genes, and construct a genetic linkage map. In total, 234 single-nucleotide polymorphisms and 25 simple sequence repeats were screened from 7,871 and 8,052 candidates, respectively, between the KM and NW varieties and used for construction of a genetic map with 94 F2 population specimens. The genetic linkage map consisted of 12 linkage groups, and 248 markers were assigned. These transcriptome and molecular marker data provide information useful for molecular breeding of oriental melon and further comparative studies of the Cucurbitaceae family.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.64 no.5
• /
• pp.27-39
• /
• 2022

The smart farm is recognized as a solution for future farmers having positive effects on the sustainability of the poultry industry. Intelligent microclimate control can be a key technology for broiler production which is extremely vulnerable to abnormal indoor air temperatures. Furthermore, better control of indoor microclimate can be achieved by accurate prediction of indoor air temperature. This study developed predictive models for internal air temperature in a mechanically-ventilated broiler house based on the data measured during three rearing periods, which were different in seasonal climate and ventilation operation. Three machine learning models and a mechanistic model based on thermal energy balance were used for the prediction. The results indicated that the all models gave good predictions for 1-minute future air temperature showing the coefficient of determination greater than 0.99 and the root-mean-square-error smaller than 0.306℃. However, for 1-hour future air temperature, only the mechanistic model showed good accuracy with the coefficient of determination of 0.934 and the root-mean-square-error of 0.841℃. Since the mechanistic model was based on the mathematical descriptions of the heat transfer processes that occurred in the broiler house, it showed better prediction performances compared to the black-box machine learning models. Therefore, it was proven to be useful for intelligent microclimate control which would be developed in future studies.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.63 no.5
• /
• pp.83-93
• /
• 2021

Agricultural reservoirs are important facilities for storing or managing water for the purpose of securing agricultural water, creating and expanding agricultural production bases, and using them to increase agricultural production. In particular, the Korea Rural Community Corporation (KRC) manages agricultural reservoirs scattered across the country, and officially recognizes and distributes hydrological data to increase their public utilization and aims to improve the value of water resources. Data on the water level of agricultural reservoirs are important. However, errors such as missing values and outliners limit utilization of the data in various fields of research and industry. Therefore, water quality data measures should be devised to increase reliability. this study categorized different error types and looked at automatic correction methods to enhance the reliability of the vast hydrological data. In addition, the water level data corrected from errors were compared to the reference hydrologic data through expert judgment in accordance with the quality control procedure, and the most appropriate measures were verified. As KRC manages more agricultural reservoirs than any other institution, the proposed method of efficient and automatic water level data correction in this study is expected to increase the availability and reliability of the hydrological data.

• Horticultural Science & Technology
• /
• v.29 no.4
• /
• pp.336-344
• /
• 2011

Using the abundant available information about the tomato genome, we developed DNA markers that are linked to disease resistant loci and performed marker-assisted selection (MAS) to construct multi-disease resistant lines and varieties. Resistance markers of Ty-1, T2, and I2, which are linked to disease resistance to Tomato yellow leaf curl virus (TYLCV), Tomato mosaic virus (ToMV), and Fusarium wilt, respectively, were developed in a co-dominant fashion. DNA sequences near the resistance loci of TYLCV, ToMV, and Fusarium wilt were used for primer design. Reported candidate markers for powdery mildew-resistance were screened and the 32.5Cla marker was selected. All four markers (Ty-1, T2, I2, and 32.5Cla) were converted to cleavage amplification polymorphisms (CAPS) markers. Then, the CAPS markers were applied to 96 tomato lines to determine the phenetic relationships among the lines. This information yielded clusters of breeding lines illustrating the distribution of resistant and susceptible characters among lines. These data were utilized further in a MAS program for several generations, and a total of ten varieties and ten inbred lines were constructed. Among four traits, three were introduced to develop varieties and breeding lines through the MAS program; several cultivars possessed up to seven disease resistant traits. These resistant trait-related markers that were developed for the tomato MAS program could be used to select early stage seedlings, saving time and cost, and to construct multi-disease resistant lines and varieties.

• Korean Journal of Veterinary Service
• /
• v.46 no.4
• /
• pp.255-262
• /
• 2023

Caseous lymphadenitis (CLA) is an endemic but not well-studied disease of Korean native goats (KNG) in Korea. Corynebacterium pseudotuberculosis is the causative agent of the contagious and chronic CLA found in goats. This study aimed to validate the potential risk factors associated with CLA and assess its seasonal prevalence to mitigate this disease in KNG. Data were collected through a questionnaire from four high- and four low-prevalence farms randomly selected based on a prior investigation. The monthly assessments of CLA were conducted in a goat abattoir located in Jeonnam Province, Korea, to evaluate its seasonal prevalence. The associated risk factors for CLA in KNG herds imply that herd size, scratching against pillars, pipes, or walls in the herd, and disinfection of goat herds are potential risk factors for CLA (P<0.05). The type of floor and entry of new goats into the herd, which are potential risk factors, affected CLA prevalence in the KNG herd (P<0.2). The prevalence of CLA in KNG was significantly higher in spring (29.34%) than in autumn (14.61%), summer (15.31%), and winter (19.48%) (P<0.05). Based on the risk factor assessment, attention should be to establishing accurate preventive measures by avoiding these identified potential risk factors.

• Journal of Ecology and Environment
• /
• v.41 no.5
• /
• pp.142-144
• /
• 2017

In recent decades, as it becomes increasingly important to monitor and research long-term ecological changes, worldwide attempts are being conducted to integrate and manage ecological data in a unified framework. Especially domestic ecological data in South Korea should be first standardized based on predefined common protocols for data integration, since they are often scattered over many different systems in various forms. Additionally, foreign ecological data should be converted into a proper unified format to be used along with domestic data for association studies. In this study, our interest is to integrate ECN data with Korean domestic ecological data under our unified framework. For this purpose, we employed our semi-automatic data conversion tool to standardize foreign data and utilized ground beetle (Carabidae) datasets collected from 12 different observatory sites of ECN. We believe that our attempt to convert domestic and foreign ecological data into a standardized format in a systematic way will be quite useful for data integration and association analysis in many ecological and environmental studies.

• Biotechnology and Bioprocess Engineering:BBE
• /
• v.10 no.5
• /
• pp.385-399
• /
• 2005

The phenotypic response of a cell results from a well orchestrated web of complex interactions which propagate from the genetic architecture through the metabolic flux network. To rationally design cell factories which carry out specific functional objectives by controlling this hierarchical system is a challenge. Transcriptome analysis, the most mature high-throughput measurement technology, has been readily applied In strain improvement programs in an attempt to Identify genes involved in expressing a given phenotype. Unfortunately, while differentially expressed genes may provide targets for metabolic engineering, phenotypic responses are often not directly linked to transcriptional patterns, This limits the application of genome-wide transcriptional analysis for the design of cell factories. However, improved tools for integrating transcriptional data with other high-throughput measurements and known biological interactions are emerging. These tools hold significant promise for providing the framework to comprehensively dissect the regulatory mechanisms that identify the cellular control mechanisms and lead to more effective strategies to rewire the cellular control elements for metabolic engineering.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38A no.6
• /
• pp.528-534
• /
• 2013

The energy consumption problem should be taken into consideration in wireless sensor network. Many studies have been proposed to address the energy consumption and delay problem. In this paper, we propose BISA(Bio-inspired Scheduling Algorithm) to reduce the energy consumption and delay in wireless sensor networks based on biological system. BISA investigates energy-efficient routing path and minimizes the energy consumption and delay using multi-channel for data transmission by multiplexing data transmission path. Through simulation, we confirm that the proposed scheme guarantees the efficient energy consumption and delay requirement.

• Food Science of Animal Resources
• /
• v.31 no.6
• /
• pp.899-906
• /
• 2011

The objectives of this study were to apply the Baranyi model to predict the growth of natural microflora in liver sausage with added kimchi powder. Kimchi powder was added to the meat products at 0, 1, 2, and 3% levels. To determine and quantify the natural microflora in the meat products, total plate counts and counts of anaerobic bacteria and lactic acid bacteria were examined throughout the 28 d of storage. The obtained data were applied to the Baranyi growth model. The indices used for comparing predicted and observed data were $B_f$, $A_f$, root mean square error (RMSE), and $R^2$. Twelve predictive models were characterized by a high $R^2$ and small RMSE. The Baranyi model was useful in predicting natural microflora levels in these meat products with added kimchi powder during storage.