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Parallel Biological In Silico Simulation

  • Patrick AmarEmail author
  • Muriel Baillieul
  • Dominique Barth
  • Bertrand LeCun
  • Franck Quessette
  • Sandrine Vial
Conference paper

Abstract

It is crucial to understand how biological systems work, in particular the metabolic pathways, if we want to be able to understand diseases. One of the author has developed HSIM, a simulator dedicated to the biochemical simulation of the reactions inside the compartments of a virtual cell. Another author is the leader in the development of bobpp a high level parallelization framework. In this article, we propose an important improvement of bobpp in order to run HSIM in parallel. This will allow us to simulate more complex models, involving more chemical species and reactions, leading to more realistic results using a smaller amount of computing time.

Notes

Acknowledgments

This work was partially supported by grant ANR MARMOTE (ANR-12-MONU-0019).

References

  1. 1.
    P. Amar, G. Bernot, V. Norris, Hsim: a simualtion programme to study large assemblies of proteins. J. Biol. Phys. Chem. 4(2), 50–63 (2004)Google Scholar
  2. 2.
    P. Amar, L. Paulevé, Hsim: an hybrid stochastic simulation system for systems biology. in: the Third International Conference on Static Analysis and Systems Biology (2012)Google Scholar
  3. 3.
    F. Galea, B. Le Cun, Bob++ : a framework for exact combinatorial optimization methods on parallel machines. in: International Conference High Performance Computing & Simulation 2007 (HPCS’07), pp. 779–785 (2007)Google Scholar
  4. 4.
    T. Gautier, J.V.F. Lima, N. Maillard, B. Raffin, XKaapi: a Runtime System for Data-Flow Task Programming on Heterogeneous Architectures. in: Proc. of the 27-th IEEE International Parallel and Distributed Processing Symposium (IPDPS). Boston, USA (2013)Google Scholar
  5. 5.
    L. Kale, B. Ramkunar, A. Sinha, A. Gursoy, The charm parallel programming language and system. Tech. Rep. 2, 1–27 (1994)Google Scholar
  6. 6.
    T. Crainic, B. Le Cun, C. Roucairol, Parallel Branch and Bound Algorithms (Wiley, USA, 2006), pp. 1–28Google Scholar
  7. 7.
    T. Menouer, B. Le Cun, P. Vander-Swalmen, Solving combinatorial problems on hpc with bobpp, WolfHPC in SuperComputing (2012)Google Scholar
  8. 8.
    M.J. Saltzman, Coin-or, Programming Languages and Systems in Computational Economics and Finance, An Open Source Library for Optimization (Springer, Boston, 2002)Google Scholar
  9. 9.
    J. Eckstein, C.A. Phillips, W.E. Hart, PEBBL 1.0 User Guide. RRR 19–2006, RUTCOR (2006)Google Scholar
  10. 10.
    T. Ralphs, L. Ladányi, M. Saltzman, A Library Hierarchy for Implementing Scalable Parallel Search algorithms. J. Supercomp. 28(2), 215–234 (2004)CrossRefzbMATHGoogle Scholar
  11. 11.
    Y. Xu, T. Ralphs, L. Ladányi, M. Saltzman, ALPS: A Framework for Implementing Parallel Search Algorithms. in: Proc. of the Ninth INFORMS Computing Society Conference (2005)Google Scholar
  12. 12.
    V.D. Cung, S. Dowaji, B. Le C, T. Mautor, C. Roucairol, Concurrent data structures and load balancing strategies for parallel branch-and-bound/a\(^{*}\) algorithms. in: III Annual Implementation Challenge Workshop, DIMACS. New Brunswick, USA (1994)Google Scholar
  13. 13.
    B. Le Cun, C. Roucairol, the PNN team: Bob : a unified platform for implementing branch-and-bound like algorithms. RR 95/16, Laboratoire PR iSM, Université de Versailles - Saint Quentin en Yvelines (1995)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Patrick Amar
    • 1
    Email author
  • Muriel Baillieul
    • 2
  • Dominique Barth
    • 2
  • Bertrand LeCun
    • 2
  • Franck Quessette
    • 2
  • Sandrine Vial
    • 2
  1. 1.LRIParis-Sud UniversityOrsay cedexFrance
  2. 2.PRiSMVersailles UniversityVersaillesFrance

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