권호 표지

Bioinformatics and Biosystems

Korean Society for Bioinformatics (ksbsb)

  • Semi Annual
  • /
  • 1738-9798(pISSN)

Aim & Scope

amins Interdisciplinary Bio Central (IBC) aims to provide an interdisciplinary medium for open, interactive, and rapid communication and for archiving the scientific and technological achievements in the area of interdisciplinary bioscience and bioengineering. Scopes IBC covers areas of experimental, theoretical, fundamental and applied science and engineering dedicated to the understanding, analyzing, and applying biological phenomena and medical issues from interdisciplinary perspectives. The areas covered by IBC include but are not limited to: Bioinformatics/Computational biology/Molecular modeling, Systems biology, Cheminformatics/Chemical biology, Omics (Physiomics/metabolomics/proteomics/genomics), Synthetic biology, Biophysics, Biomathematics/Mathematical Biology and Medicine, Biomechanics/Biomachine interface/Biorobotics, Bioimaging, Biomaterials/Biomimetics, Bioelectronics, Medical informatics, Biological computation/Database, Pharmaceutical bioscience and technology, Resources, Nano-bio and Nano-medical science and technology, Environmental bioscience and technology/Bioenergy, Biological frontiers: In addition, any biological paper will be considered for publication if they may lead to promotion of interdisciplinary thinking and research.

Volume 2 Issue 2

  1. Kim, Tae-Ho;Kim, Eui-Yong;Youm, Jae-Boum;Kho, Weon-Gyu;Gwak, Heui-Chul;Joo, Hyun 37
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    생물정보학은 컴퓨터를 이용하여 방대한 양의 생물학적 데이터를 처리하고 그 결과를 분석하는 학문으로서 IT의 고속성장과 맞물려 점차 그 활용도를 넓혀가고 있다. 특히 의학, 생명과학 연구에 사용되는 데이터는 그 종류도 다양하고 크기가 매우 큰 것이 일반적인데, 이의 처리를 위해서는 고속 네트워크가 바탕이 된 그리드-컴퓨팅(Grid-Computing) 기술 접목이 필연적이다. 고속 네트워크 기술의 발전은 슈퍼컴퓨터를 대체해 컴퓨터 풀 내에 분산된 시스템들을 하나로 묶을 수 있는 그리드-컴퓨팅 분야를 선도하고 있다. 최근 생물정보학 분야에서도 이처럼 발전된 고성능 분산 컴퓨팅 기술을 이용하여 데이터의 신속한 처리와 관리의 효율성을 증대시키고 있는 추세이다. 그리드-컴퓨팅 기술은 크게 데이터 가공을 위한 응용 프로그램 개발과 데이터 관리를 위한 데이터베이스 구축으로 구분 지을 수 있다. 전자에 해당하는 생물정보 연구용 프로그램들은 mpiBLAST, ClustalW-MPI와 같은 MSA서열정렬 프로그램들을 꼽을 수 있으며, BioSimGrid, Taverna와 같은 프로젝트는 그리드-데이터베이스 (Grid-Database)기술을 바탕으로 개발되었다. 본 고에서는 미지의 생명현상을 탐구하고 연구하기 위하여 현재까지 개발된 그리드-컴퓨팅 환경과 의생명과학 연구를 위한 응용 프로그램들, 그리고 그리드-데이터베이스 기술 등을 소개한다.
  2. Kwon, Kyung-Hoon;Park, Gun-Wook;Kim, Jin-Young;Park, Young-Mok;Yoo, Jong-Shin 46
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    In the proteomics research using mass spectrometry, the protein database search gives the protein information from the peptide sequences that show the best match with the tandem mass spectra. The protein sequence database has been a powerful knowledgebase for this protein identification. However, as we accumulate the protein sequence information in the database, the database size gets to be huge. Now it becomes hard to consider all the protein sequences in the database search because it consumes much computing time. For the high-throughput analysis of the proteome, usually we have used the non-redundant refined database such as IPI human database of European Bioinformatics Institute. While the non-redundant database can supply the search result in high speed, it misses the variation of the protein sequences. In this study, we have concerned the proteomics data in the point of protein similarities and used the network analysis tool to build a new analysis method. This method will be able to save the computing time for the database search and keep the sequence variation to catch the modified peptides.
  3. Chang, Hye-Shik;Hwang, Kyu-Woong;Kim, Dong-Sup 52
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    The computational discovery of transcription factor binding site is one of the important tools in the genetic and genomic analysis. Rough prediction of gene regulation network and finding possible co-regulated genes are typical applications of the technique. Countless motif-discovery algorithms have been proposed for the past years. However, there is no dominant algorithm yet. Each algorithm does not give enough accuracy without extensive information. In this paper, we explore the possibility of combining multiple algorithms for the one integrated result in order to improve the performance and the convenience of researchers. Moreover, we apply new high order information that is reorganized from the set of basis predictions to the final prediction.
  4. Cho, Hye-Young;Lee, Do-Heon 62
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    We present here the strategy of data integration for inference of genetic regulatory modules. First, we construct all possible combinations of regulators of genes using chromatin-immunoprecipitation(ChIP)-chip data. Second, hierarchical clustering method is employed to analyze mRNA expression profiles. Third, integration method is applied to both of the data. Finally, we construct a genetic regulatory module which is involved in the function of ribosomal protein synthesis.
  5. Kim, Eun-Jin;Bae, Se-Eun;Son, Hyeon-Seok 66
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    과거 사회라는 개념 속에서의 관찰과 통계적 인과관계의 규명에 치중하여 이루어졌던 보건학 연구는 유전체 연구와 같은 증거 위주의 기초과학적 측면에서 이루어 지고 있는 것이 현재의 추세이다. 이러한 생물학적 데이터를 이용한 질병과 보건에 대한 연구가 활성화 됨에 따라 보건 분야에서도 컴퓨터를 이용하여 방대한 정보를 다룰 수 있는 생명정보학이 유용한 도구로서 인식되고 있다. 본고에서는 생명정보학이 보건학의 연구분야에서 어떻게 이용되는 가를 학술적인 의미에서 살펴보고 그 활용방안에 대해 논의해 보고자 한다.
  6. Kim, Jin-Ki;Lee, Do-Heon;Yi, Gwan-Su 71
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    Cell phenotypes are determined by groups of functionally related genes. Microarray profiling of gene expression provides us response of cellular state to its perturbation. Several methods for uncovering a cellular network show reliable network reconstruction. In this study, we present reconstruction of genetic regulatory network of inflammation bowel disease in human peripheral blood mononuclear cell. The microarray based on Affymetrix Gene Chip Human Genome U133 Array Set HG-U133A is processed and applied network reconstruction algorithm, ARACNe. As a result, we will show that inferred network composed of 450 nodes and 2017 edges is roughly scale-free network and hierarchical organization. The major hub, CCNL2 (cyclin A2), in inferred network is shown to be associated with inflammatory function as well as apoptotic function.