Table of Contents Author Guidelines Submit a Manuscript
Scientific Programming
Volume 2017, Article ID 8721612, 11 pages
https://doi.org/10.1155/2017/8721612
Research Article

Implementation of an Agent-Based Parallel Tissue Modelling Framework for the Intel MIC Architecture

1ICM, University of Warsaw, Pawińskiego 5a, Warszawa, Poland
2Intel Technology Poland, Słowackiego 173, Gdańsk, Poland

Correspondence should be addressed to Maciej Cytowski; lp.ude.mci@ikswotyc.m

Received 17 October 2016; Revised 23 December 2016; Accepted 1 February 2017; Published 23 February 2017

Academic Editor: Raphaël Couturier

Copyright © 2017 Maciej Cytowski et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M. H. Swat, G. L. Thomas, J. M. Belmonte, A. Shirinifard, D. Hmeljak, and J. A. Glazier, “Multi-scale modeling of tissues using CompuCell3D,” Methods in Cell Biology, vol. 110, pp. 325–366, 2012. View at Publisher · View at Google Scholar · View at Scopus
  2. J. Starruß, W. de Back, L. Brusch, and A. Deutsch, “Morpheus: a user-friendly modeling environment for multiscale and multicellular systems biology,” Bioinformatics, vol. 30, no. 9, pp. 1331–1332, 2014. View at Publisher · View at Google Scholar · View at Scopus
  3. B. R. Angermann, F. Klauschen, A. D. Garcia et al., “Computational modeling of cellular signaling processes embedded into dynamic spatial contexts,” Nature Methods, vol. 9, pp. 283–289, 2012. View at Publisher · View at Google Scholar
  4. S. Hoehme and D. Drasdo, “A cell-based simulation software for multi-cellular systems,” Bioinformatics, vol. 26, no. 20, pp. 2641–2642, 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. P. Macklin, M. E. Edgerton, A. M. Thompson, and V. Cristini, “Patient-calibrated agent-based modelling of ductal carcinoma in situ (DCIS): from microscopic measurements to macroscopic predictions of clinical progression,” Journal of Theoretical Biology, vol. 301, pp. 122–140, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  6. S. Kang, S. Kahan, J. McDermott, N. Flann, and I. Shmulevich, “Biocellion: accelerating computer simulation of multicellular biological system models,” Bioinformatics, vol. 30, no. 21, pp. 3101–3108, 2014. View at Publisher · View at Google Scholar · View at Scopus
  7. G. R. Mirams, C. J. Arthurs, M. O. Bernabeu et al., “Chaste: an open source C++ library for computational physiology and biology,” PLoS Computational Biology, vol. 9, no. 3, Article ID e1002970, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  8. M. Cytowski and Z. Szymanska, “Large scale parallel simulations of 3-D cell colony dynamics,” Computing in Science & Engineering, vol. 16, no. 5, pp. 86–95, 2014. View at Publisher · View at Google Scholar
  9. M. Cytowski and Z. Szymanska, “Large-scale parallel simulations of 3D cell colony dynamics: the cellular environment,” Computing in Science and Engineering, vol. 17, no. 5, Article ID 7106395, pp. 44–48, 2015. View at Publisher · View at Google Scholar · View at Scopus
  10. M. Cytowski and Z. Szymanska, “Enabling large scale individual-based modelling through high performance computing,” in Proceedings of the Workshop on Multiscale and Hybrid Modelling in Cell and Cell Population Biology (UPMC '15), article 00014, p. 5, ITM Web of Conferences, Paris, France, March 2015. View at Publisher · View at Google Scholar
  11. L. Szustak, K. Rojek, T. Olas, L. Kuczynski, K. Halbiniak, and P. Gepner, “Adaptation of MPDATA heterogeneous stencil computation to intel xeon phi coprocessor,” Scientific Programming, vol. 2015, Article ID 642705, 14 pages, 2015. View at Publisher · View at Google Scholar · View at Scopus
  12. A. Lastovetsky, L. Szustak, and R. Wyrzykowski, “Model-based optimization of EULAG kernel on Intel Xeon Phi through load imbalancing,” IEEE Transactions on Parallel and Distributed Systems, vol. 28, no. 3, pp. 787–797, 2017. View at Publisher · View at Google Scholar
  13. “On selected individual-based approaches to the dynamics in multicellular systems,” in Polymer and Cell Dynamics-Multiscale Modeling and Numerical Simulations, D. Drasdo, W. Alt, M. Chaplain, M. Griebel, and J. Lenz, Eds., pp. 169–203, Birkhäuser, Basel, Switzerland, 2003.
  14. J. Galle, M. Loeffler, and D. Drasdo, “Modeling the effect of deregulated proliferation and apoptosis on the growth dynamics of epithelial cell populations in vitro,” Biophysical Journal, vol. 88, no. 1, pp. 62–75, 2005. View at Publisher · View at Google Scholar · View at Scopus
  15. K. S. Nowiński and B. Borucki, “VisNow-a modular, extensible visual analysis platform,” in Proceedings of the 22nd International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG '14), pp. 73–76, Pilsen, Czech Republic, 2014.
  16. J. Jeffers and J. Reinders, Intel® Xeon Phi™ Coprocessor High-Performance Programming, Morgan Kaufmann, 2013.
  17. J. Jeffers, J. Reinders, and A. Sodani, Intel® Xeon Phi™ Processor High Performance Programming, Morgan Kaufmann, Amsterdam, The Netherlands, 2nd edition, 2016.
  18. A. H. Baker, R. D. Falgout, T. V. Kolev, and U. M. Yang, “Scaling hypr's multigrid solvers to 100,000 cores,” in High-Performance Scientific Computing, M. W. Berry, K. A. Gallivan, E. Gallopoulos et al., Eds., pp. 261–279, Springer, London, UK, 2012. View at Publisher · View at Google Scholar
  19. E. G. Boman, Ü. V. Çatalyürek, C. Chevalier, and K. D. Devine, “The Zoltan and Isorropia parallel toolkits for combinatorial scientific computing: partitioning, ordering and coloring,” Scientific Programming, vol. 20, no. 2, pp. 129–150, 2012. View at Publisher · View at Google Scholar · View at Scopus
  20. M. Mascagni and A. Srinivasan, “Algorithm 806: SPRNG: a scalable library for pseudorandom number generation,” ACM Transactions on Mathematical Software, vol. 26, no. 3, pp. 436–461, 2000. View at Publisher · View at Google Scholar
  21. J. Zielinski, “Discover, extend and modernize your current development approach for heterogeneous compute with standards based OFI/MPI/OpenMP programming methods on Intel® Xeon Phi™ architectures,” in Proceedings of the Intel® HPC Developer Conference, Salt Lake City, Utah, USA, 2016, http://www.intel.com/content/www/us/en/events/hpcdevcon/systems-track.html#discover.
  22. P. Van Liedekerke, M. M. Palm, N. Jagiella, and D. Drasdo, “Simulating tissue mechanics with agent-based models: concepts, perspectives and some novel results,” Computational Particle Mechanics, vol. 2, no. 4, pp. 401–444, 2015. View at Publisher · View at Google Scholar
  23. U. Wilensky and W. Rand, An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with Netlogo, The MIT Press, 2015.