• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.10
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• pp.2054-2061
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• 2015

Biologically inspired modeling techniques have received considerable attention for their robustness, scalability, and adaptability with simple local interactions and limited information. Among these modeling techniques, Gene Regulatory Networks (GRNs) play a central role in understanding natural evolution and the development of biological organisms from cells. In this paper, we apply GRN principles to the WSN system and propose a new GRN model for decentralized node scheduling design to achieve energy balancing while meeting delay requirements. Through this scheme, each sensor node schedules its state autonomously in response to gene expression and protein concentration, which are controlled by the proposed GRN-inspired node scheduling model. Simulation results indicate that the proposed scheme achieves superior performance with energy balancing as well as desirable delay compared with other well-known schemes.

• Journal of Life Science
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• v.30 no.1
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• pp.45-57
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• 2020

The prenatal period in livestock animals is crucial for meat production because net increase in the number of muscle fibers is finished before birth. However, there is no study on the growth and development mechanism of muscles in Hanwoo during this period. Therefore, to find candidate genes involved in muscle growth and development during this period in Hanwoo, mRNA expression data of longissimus in Hanwoo at 6 and 9 months post-conceptional age (MPA) were analyzed. We independently identified differentially expressed genes (DEGs) using DESeq2 and edgeR which are R software packages, and considered the overlaps of the results as final-DEGs to use in downstream analysis. The DEGs were classified into several modules using WGCNA then the modules' functions were analyzed to identify modules which involved in myogenesis and adipogenesis. Finally, the hub genes which had the highest WGCNA module membership among the top 10% genes of the STRING network maximal clique centrality were identified. 913(6 MPA specific DEGs) and 233(9 MPA specific DEGs) DEGs were figured out, and these were classified into five and two modules, respectively. Two of the identified modules'(one was in 6, and another was in 9 MPA specific modules) functions was found to be related to myogenesis and adipogenesis. One of the hub genes belonging to the 6 MPA specific module was axin1 (AXIN1) which is known as an inhibitor of Wnt signaling pathway, another was succinate-CoA ligase ADP-forming beta subunit (SUCLA2) which is known as a crucial component of citrate cycle.

• Journal of Plant Biotechnology
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• v.43 no.3
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• pp.347-358
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• 2016

Brassinosteroid (BR), a plant steroid hormone, plays key roles in numerous growth and developmental processes as well as tolerance to both abiotic and biotic stress. To understand the biological networks involved in BR-mediated signaling pathways and stress tolerance, we performed comparative genome-wide transcriptome analysis of a constitutively activated BR bes1-D mutant with an Agilent Arabidopsis $4{\times}44K$ oligo chip. As a result, we newly identified 1,091 (562 up-regulated and 529 down-regulated) significant differentially expressed genes (DEGs). The combination of GO enrichment and protein network analysis revealed that stress-related processes, such as metabolism, development, abiotic/biotic stress, immunity, and defense, were critically linked to BR signaling pathways. Among the identified gene sets, we confirmed more than a 6-fold up-regulation of NB-ARC and FLS2 in bes1-D plants. However, some genes, including TIR1, TSA1 and OCP3, were down-regulated. Consistently, BR-activated plants showed higher tolerance to drought stress and pathogen infection compared to wild-type controls. In this study, we newly developed a useful, comprehensive method for large-scale identification of critical network and gene sets with global transcriptome analysis using a microarray. This study also showed that gain of function in the bes1-D gene can regulate the adaptive response of plants to various stressful conditions.