Journal of KIISE (정보과학회 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지
DOI QR코드

DOI QR Code

A Case Study of Drug Repositioning Simulation based on Distributed Supercomputing Technology

분산 슈퍼컴퓨팅 기술에 기반한 신약재창출 시뮬레이션 사례 연구

  • 김직수 (한국과학기술정보연구원 국가슈퍼컴퓨팅연구소) ;
  • 노승우 (한국과학기술정보연구원 국가슈퍼컴퓨팅연구소) ;
  • 이민호 (상지대학교 생명과학과) ;
  • 김서영 (한국과학기술정보연구원 국가슈퍼컴퓨팅연구소) ;
  • 김상완 (한국과학기술정보연구원 국가슈퍼컴퓨팅연구소) ;
  • 황순욱 (한국과학기술정보연구원 국가슈퍼컴퓨팅연구소)
  • Received : 2014.08.20
  • Accepted : 2014.10.08
  • Published : 2015.01.15
⟨ Previous Next ⟩

Abstract

In this paper, we present a case study for a drug repositioning simulation based on distributed supercomputing technology that requires highly efficient processing of large-scale computations. Drug repositioning is the application of known drugs and compounds to new indications (i.e., new diseases), and this process requires efficient processing of a large number of docking tasks with relatively short per-task execution times. This mechanism shows the main characteristics of a Many-Task Computing (MTC) application, and as a representative case of MTC applications, we have applied a drug repositioning simulation in our HTCaaS system which can leverage distributed supercomputing infrastructure, and show that efficient task dispatching, dynamic resource allocation and load balancing, reliability, and seamless integration of multiple computing resources are crucial to support these challenging scientific applications.

본 논문에서는 대규모의 계산 작업을 고성능으로 처리해야 하는 신약재창출 시뮬레이션 분야에 분산 슈퍼컴퓨팅 기술을 적용한 사례에 대해 논의하고자 한다. 신약재창출이란 기존에 알려진 약물의 새로운 적응증을 규명하는 것을 의미하며, 이러한 신약재창출은 비교적 짧은 수행시간을 갖는 대규모의 도킹(docking) 연산들을 고성능으로 처리해야한다는 점에서 Many-Task Computing (MTC) 성격을 지니고 있다. 이러한 MTC 응용들의 대표 사례로서 신약재창출 시뮬레이션을 분산 슈퍼컴퓨팅 환경 기반의 HTCaaS 시스템에 적용하였으며, 이를 통해 효율적인 작업 배포, 동적인 자원 할당 및 로드 밸런싱, 안정성 및 다양한 자원들의 효율적인 통합 등이 이러한 과학 응용들을 지원하는 데 있어 필수적인 기능임을 확인할 수 있었다.

Keywords

Acknowledgement

Supported by : KISTI

References

  1. M. Lee, D. Kim, "Large-scale reverse docking profiles and their applications," BMC Bioinformatics, Vol. 13, No. 17, 2012.
  2. J. Lee, J-W. Choi, J. Shin, K-T. No, "Trends in Computer Aided Drug Discovery, Next Generation," Communications of the Korea Information Science Society, Vol. 31, No. 8, pp. 35-54, 2013.
  3. Yakup.com, Available: http://goo.gl/xEnUka
  4. I. Raicu, I. Foster and Y. Zhao, "Many-Task Computing for Grids and Supercomputers," Proc. of the IEEE/ACM Workshop on Many-Task Computing on Grids and Supercomputers (MTAGS'08), 2008.
  5. I. Raicu, Y. Zhao, C. Dumitrescu, I. Foster, and M.Wilde, "Falkon: a Fast and Light-weight tasK executiON framework," Proc. of the 2007 ACM/ IEEE conference on Supercomputing (SC'07), Nov. 2007.
  6. J.-S. Kim, S. Rho, S. Kim, S. Kim, S. Kim, and S. Hwang, "HTCaaS: Leveraging Distributed Supercomputing Infrastructures for Large-Scale Scientific Computing," Proc. of the IEEE/ACM 6th Workshop on Many-Task Computing on Clouds, Grids, and Supercomputers (MTAGS'13), Nov. 2013.
  7. S. Rho, S. Kim, S. Kim, S. Kim, J.-S. Kim, and S. Hwang, "HTCaaS: A Large-Scale High-Throughput Computing by Leveraging Grids, Supercomputers and Cloud," Proc. of the IEEE/ACM International Conference for High Performance Computing, Networking, Storage and Analysis (SC12), Nov. 2012.
  8. Open Grid Forum Job Submission Description Language [Online]. Available: http://www.gridforum.org/documents/GFD.56.pdf
  9. Partnership & Leadership for the nationwide Supercomputing Infrastructure [Online]. Available: http:// www.plsi.or.kr/
  10. Jik-Soo Kim, Seok-Kyoo Kim, Sangwan Kim, Seungwoo Rho, Seoyoung Kim, Soonwook Hwang, "An Analysis of Multi-level Scheduling Mechanism for Large-scale Scientific Computing," Journal of KISS(C): Computing Practices and Letters, Vol. 20, No. 7, pp. 379-385, 2014.
  11. A. Luckow, M. Santcroos, O. Weidner, A. Merzky, P. Mantha, and S. Jha, "P*: A Model of Pilot- Abstractions," Proc. of the 8th IEEE International Conference on eScience (eScience 2012), Oct. 2012.
  12. A. Luckow, L. Lacinski, and S. Jha, "SAGA BigJob: An Extensible and Interoperable Pilot-Job Abstraction for Distributed Applications and Systems," Proc. of the 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGrid 2010), May 2010.
  13. E. Walker, J. P. Gardner, V. Litvin, and E. L. Turner, "Creating Personal Adaptive Clusters for Managing Scientific Jobs in a Distributed Computing Environment," Proc. of the Challenges of Large Applications in Distributed Environments (CLADE' 06), Jun. 2006.
  14. A. Tsaregorodtsev, M. Bargiotti, N. Brook, A. C. Ramo, G. Castellani, P. Charpentier, C. Cioffi, J. Closier, R. G. Diaz, G. Kuznetsov, Y. Y. Li, R. Nandakumar, S. Paterson, R. Santinelli, A. C. Smith, M. S. Miguelez, and S. G. Jimenez, "DIRAC: a community grid solution," Journal of Physics: Conference Series, Vol. 119, 2008.
  15. C. Andronis, A. Sharma, V. Virvilis, S. Deftereos, and A. Persidis, "Literature mining, ontologies and information visualization for drug repurposing," Briefings in Bioinformatics, Vol. 12, pp. 357-368, 2011. https://doi.org/10.1093/bib/bbr005
  16. M. Campillos, M. Kuhn, A. C. Gavin, L. J. Jensen, and P. Bork, "Drug target identification using side-effect similarity," Science, Vol. 321, pp. 263-266, 2008. https://doi.org/10.1126/science.1158140
  17. M. J. Keiser MJ et al., "Predicting new molecular targets for known drugs," Nature, Vol. 462, pp. 175- 181, 2009. https://doi.org/10.1038/nature08506
  18. J. T. Dudley, T. Deshpande, and A. J. Butte, "Exploiting drug-disease relationships for computational drug repositioning," Briefings in Bioinformatics, Vol. 12, pp. 303-311, 2011. https://doi.org/10.1093/bib/bbr013
  19. S. Daminelli, V. J. Haupt, M. Reimann, and M. Schroeder, "Drug repositioning through incomplete bi-cliques in an integrated drug-target-disease network," Integrative Biology (Camb.), Vol. 7, pp. 778- 788, 2012.
  20. O. Trott, A. J. Olson, "AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading," Journal of Computational Chemistry, Vol. 31, pp. 455-461, 2010.