• 제목/요약/키워드: Network biology

검색결과 507건 처리시간 0.028초

Identification of Amino Acid Residues in the Carboxyl Terminus Required for Malonate-Responsive Transcriptional Regulation of MatR in Rhizobium leguminosarum bv. trifolii

  • Lee, Hwan-Young;Kim, Yu-Sam
    • BMB Reports
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    • 제34권4호
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    • pp.305-309
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    • 2001
  • MatR in Rhizobium trifolii is a malonate-responsive transcription factor that regulates the expression of genes, matABC, enabling decarboxylation of malonyl-CoA into acetyl-CoA, synthesis of malonyl-CoA from malonate and CoA, and malonate transport. According to an analysis of the amino acid sequence homology, MatR belongs to the GntR family The proteins of this family have two-domain folds, the N-terminal helix-turn-helix DNA-binding domain and the C-terminal ligand-binding domain. In order to End the malonate binding site and amino acid residues that interact with RNA polymerase, a site-directed mutagenesis was performed. Analysis of the mutant MatR suggests that Arg-160 might be involved in malonate binding, whereas Arg-102 and Arg-174 are critical for the repression activity by interacting with RNA polymerase.

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CD137-CD137 Ligand Interactions in Inflammation

  • Kwon, Byung-Suk
    • IMMUNE NETWORK
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    • 제9권3호
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    • pp.84-89
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    • 2009
  • The main stream of CD137 studies has been directed to the function of CD137 in $CD8^+$ T-cell immunity, including its anti-tumor activity, and paradoxically the immunosuppressive activity of CD137, which proves to be of a great therapeutic potential for animal models of a variety of autoimmune and inflammatory diseases. Recent studies, however, add complexes to the biology of CD137. Accumulating is evidence supporting that there exists a bidirectional signal transduction pathway for the CD137 receptor and its ligand (CD137L). CD137/CD137L interactions are involved in the network of hematopoietic and nonhematopoietic cells in addition to the well characterized antigen-presenting cell-T cell interactions. Signaling through CD137L plays a critical role in the differentiation of myeloid cells and their cellular activities, suggesting that CD137L signals trigger and sustain inflammation. The overall consequence might be that the amplified inflammation by CD137L enhances the T-cell activity together with CD137 signals by upregulating costimulatory molecules, MHC molecules, cell adhesion molecules, cytokines, and chemokines. Solving this outstanding issue is urgent and will have an important clinical implication.

유전자 알고리즘으로 학습한 베이지안 네트워크에 기초한 질병 모듈 추론 (Inference of Disease Module using Bayesian Network by Genetic Algorithm)

  • 정다예;여윤구;안재균;박상현
    • 한국정보처리학회:학술대회논문집
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    • 한국정보처리학회 2013년도 추계학술발표대회
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    • pp.1117-1120
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    • 2013
  • 사람의 질병은 여러 요인의 복합적인 작용으로 발생하는데 이 중 유전적인 요인에는 유전자 간의 상호작용을 들 수 있다. 마이크로어레이(Microarray) 데이터로부터 유전자의 활성화 및 억제 관계를 밝히려는 다양한 시도는 계속되어왔다. 그러나 마이크로어레이 자체가 갖는 불안정성과 실험조건 수의 제약이 커다란 장애가 되어 왔다. 이에 생물학적 사전 지식을 포함하는 방법들이 제안되었다. 본 논문에서는 질병과 관련된 유전자 간의 상호작용의 집합을 질병 모듈이라 정의하고 이를 유전자 알고리즘으로 학습한 베이지안 네트워크(Bayesian network)로 추론하는 방법을 제안한다.

Uncovering the Role of External APIs in Driving Dynamic Ecosystem Growth

  • Um, Sungyong;Kang, Martin;Son, Insoo
    • 한국정보시스템학회지:정보시스템연구
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    • 제33권2호
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    • pp.143-168
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    • 2024
  • Purpose This study highlights the crucial role of external APIs in driving dynamic evolution within a digital ecosystem. Drawing on the concept of evolutionary network biology perspective, this study hypothesizes that APIs' (non)network properties can significantly impact a digital ecosystem's product variety. Design/methodology/approach This study analyzes plug-in source code data from WordPress.org between January 2004 and December 2014, using survival analysis to test this hypothesis. Findings The empirical results demonstrate that external APIs have a more significant impact on promoting ecosystem evolution over time than those offered by a focal platform system. This research enhances our understanding of ecosystem dynamics and emphasizes the critical role of the generative nature of APIs in fostering ecosystem growth.

인체 SIP 단백질에 특이적인 단일클론 항체의 특성 (Characterization of a Monoclonal Antibody Specific to Human Siah-1 Interacting Protein)

  • 윤선영;주종혁;김주헌;강호범;김진숙;이영희;권두한;김창남;최인성;김재화
    • IMMUNE NETWORK
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    • 제4권1호
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    • pp.23-30
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    • 2004
  • Background: A human orthologue of mouse S100A6-binding protein (CacyBP), Siah-1-interacting protein (SIP) had been shown to be a component of novel ubiquitinylation pathway regulating $\beta$-catenin degradation. The role of the protein seems to be important in cell proliferation and cancer evolution but the expression pattern of SIP in actively dividing cancer tissues has not been known. For the elucidation of the role of SIP protein in carcinogenesis, it is essential to produce monoclonal antibodies specific to the protein. Methods: cDNA sequence coding for ORF region of human SIP gene was amplified and cloned into an expression vector to produce His-tag fusion protein. Recombinant SIP protein and monoclonal antibody to the protein were produced. The N-terminal specificity of anti-SIP monoclonal antibody was conformed by immunoblot analysis and enzyme linked immunosorbent assay (ELISA). To study the relation between SIP and colon carcinogenesis, the presence of SIP protein in colon carcinoma tissues was visualized by immunostaining using the monoclonal antibody produced in this study. Results: His-tag-SIP (NSIP) recombinant protein was produced and purified. A monoclonal antibody (Korea patent pending; #2003-45296) to the protein was produced and employed to analyze the expression pattern of SIP in colon carcinoma tissues. Conclusion: The data suggested that anti-SIP monoclonal antibody produced here was valuable for the diagnosis of colon carcinoma and elucidation of the mechanism of colon carcinogenesis.

Molecular Docking Study of Aminoacyl-tRNA Synthetases with Ligand Molecules from Four Different Scaffolds

  • Bharatham, Nagakumar;Bharatham, Kavitha;Lee, Yu-No;Kim, Song-Mi;Lazar, Prettina;Baek, A-Young;Park, Chan-In;Eum, Hee-Sung;Ha, Hyun-Joon;Yun, Sae-Young;Lee, Won-Koo;Kim, Sung-Hoon;Lee, Keun-Woo
    • Bulletin of the Korean Chemical Society
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    • 제31권3호
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    • pp.606-610
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    • 2010
  • Aminoacyl-tRNA synthetases (aaRSs) play vital roles in protein biosynthesis of living organisms and are interesting antibacterial drug targets. In order to find out new inhibitor candidate molecules as antibacterial agent, the binding modes of the candidate molecules were investigated at the active sites of aaRSs by molecular docking study. The docking simulations were performed with 48 compounds from four different scaffolds into the eight different aaRSs. The results show that scaffolds 3 and 4 compounds have consistently better binding capabilities, specifically for HisRS (E. coli) and IleRS (S. aureus). The binding modes of the best compounds with the proteins were well compatible with those of two ligands in crystal structures. Therefore, we expect that the final compounds we present may have reasonable aaRS inhibitory activity.

Protein Interaction Network in E-C Coupling

  • Kang Gil-Bu;Lee Jun-Hyuck;Kim Mun-Kyoung;Kim Sung-Hyun;Eom Soo-Hyun
    • 한국동물학회:학술대회논문집
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    • 한국동물학회 2006년도 제61회 한국생물과학협회 정기학술대회
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    • pp.21-21
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    • 2006
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Higher Order Knowledge Processing: Pathway Database and Ontologies

  • Fukuda, Ken Ichiro
    • Genomics & Informatics
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    • 제3권2호
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    • pp.47-51
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    • 2005
  • Molecular mechanisms of biological processes are typically represented as 'pathways' that have a graph­analogical network structure. However, due to the diversity of topics that pathways cover, their constituent biological entities are highly diverse and the semantics is embedded implicitly. The kinds of interactions that connect biological entities are likewise diverse. Consequently, how to model or process pathway data is not a trivial issue. In this review article, we give an overview of the challenges in pathway database development by taking the INOH project as an example.

Deep Learning Approach Based on Transcriptome Profile for Data Driven Drug Discovery

  • Eun-Ji Kwon;Hyuk-Jin Cha
    • Molecules and Cells
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    • 제46권1호
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    • pp.65-67
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    • 2023
  • SMILES (simplified molecular-input line-entry system) information of small molecules parsed by one-hot array is passed to a convolutional neural network called black box. Outputs data representing a gene signature is then matched to the genetic signature of a disease to predict the appropriate small molecule. Efficacy of the predicted small molecules is examined by in vivo animal models. GSEA, gene set enrichment analysis.