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NoC Architectures as Enablers of Biological Discovery for Personalized and Precision Medicine

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Published:28 September 2015Publication History

ABSTRACT

This paper overviews the main computational issues in personalized and precision medicine (PPM), and present a cogent case for network-on-chip (NoC)-based multicore platforms as enablers in the process. We identify a series of challenges for the design and optimization of NoC-based solutions for PPM. To capture the characteristics of the cyber-physical sensing and processing, we propose a new computational model built on a dynamical heterogeneous hyper-graph description of application-to-architecture interactions. Starting from these premises, we summarize a few implications on NoC design methodologies, present some NoC-based solutions that deal with some of the challenges, and outline a few open problems.

References

  1. A. Ossola: 'The race to build a search engine for your DNA', IEEE Spectrum: 'The race to build a search engine for your DNA', 2015Google ScholarGoogle Scholar
  2. A. Ramanathan et al: 'Discovering Conformational Sub-States Relevant to Protein Function', PLoS ONE, 2011Google ScholarGoogle Scholar
  3. A. Ramanathan, A. J. Savol, P. K. Agarwal, and C. S. Chennubhotla: 'Event detection and sub-state discovery from biomolecular simulations using higher-order statistics: Application to enzyme adenylate kinase', Proteins: Structure, Function, and Bioinformatics, 2012, 80, (11), pp. 2536--2551.Google ScholarGoogle Scholar
  4. A. Ramanathan, A. Savol, V. Burger, C. S. Chennubhotla, and P. K. Agarwal: 'Protein Conformational Populations and Functionally Relevant Substates', Accounts of Chemical Research, 2014, 47, (1), pp. 149--156.Google ScholarGoogle ScholarCross RefCross Ref
  5. A. Ramanathan, J. O. Yoo, and C. J. Langmead: 'On-the-Fly Identification of Conformational Substates from Molecular Dynamics Simulations', Journal of Chemical Theory and Computation, 2011, 7, (3), pp. 778--789.Google ScholarGoogle ScholarCross RefCross Ref
  6. A. Ramanathan, P. K. Agarwal, M. Kurnikova, and C. J. Langmead: 'An Online Approach for Mining Collective Behaviors from Molecular Dynamics Simulations', Journal of Computational Biology, 2010, 17, (3), pp. 309--324.Google ScholarGoogle ScholarCross RefCross Ref
  7. A. Stamatakis, "RAxML-VI-HPC: Maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models," Bioinformatics, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. B. Buchfink, C. Xie, D.H. Huson, "Fast and sensitive protein alignment using DIAMOND," Nature methods, 12, 2015.Google ScholarGoogle Scholar
  9. C. F. Lopez, J. L. Muhlich, J. A. Bachman and P. K. Sorger, Programming biological models in Python using PySB. Mol Syst Biol 9, (2013). doi:10.1038/msb.2013.1.Google ScholarGoogle Scholar
  10. C. I. Barash et al: 'Harnessing big data for precision medicine: A panel of experts elucidates the data challenges and proposes key strategic decisions points', Applied & Translational Genomics, 2015, 4, (0), pp. 10--13.Google ScholarGoogle ScholarCross RefCross Ref
  11. D.E. Shaw et al., "Anton, a special-purpose machine for molecular dynamics simulation," Commun. ACM, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. D.M. Roden, and R.F. Tyndale: 'Genomic Medicine, Precision Medicine, Personalized Medicine: What's in a Name?', Clinical Pharmacology & Therapeutics, 2013Google ScholarGoogle Scholar
  13. E. Chow et al. "Desmond performance on a cluster of multicore processors," DE Shaw Research Technical Report DESRES/TR-2008-01, http://www. deshawresearch.com/publications/Desmond% 20Performance% 20on%20a% 20Cluster 20 (2008)Google ScholarGoogle Scholar
  14. E.A. Ashley.: 'The precision medicine initiative: A new national effort', JAMA, 2015, 313, (21), pp. 2119--2120.Google ScholarGoogle ScholarCross RefCross Ref
  15. E.S. Lander : 'Cutting the Gordian Helix --- Regulating Genomic Testing in the Era of Precision Medicine', New England Journal of Medicine, 2015Google ScholarGoogle ScholarCross RefCross Ref
  16. EZ Chip TILE-Gx-36, Accessed on June 11, 2015, Online at: http://www.tilera.com/products/?ezchip=585&spage=621Google ScholarGoogle Scholar
  17. F. Sievers et al: 'Fast, scalable generation of high--quality protein multiple sequence alignments using Clustal Omega', Molecular Systems Biology, 2011, 7, (1).Google ScholarGoogle ScholarCross RefCross Ref
  18. G. R. Bowman, V.A. Voelz, and V.S. Pande,: 'Taming the complexity of protein folding', Current Opinion in Structural Biology, 2011, 21, (1), pp. 4--11.Google ScholarGoogle ScholarCross RefCross Ref
  19. H. Li, and R. Durbin: 'Fast and accurate long-read alignment with Burrows-Wheeler transform', Bioinformatics, 2010 Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. J. Felsenstein, "Evolutionary trees from DNA sequences: A maximum likelihood approach," J. Mol. Evol. 1981.Google ScholarGoogle ScholarCross RefCross Ref
  21. J.L. Jameson and D.L. Longo. "Precision Medicine --- Personalized, Problematic, and Promising," New England Journal of Medicine, 2015, 372, (23), pp. 2229--2234.Google ScholarGoogle ScholarCross RefCross Ref
  22. L. Song, L. Florea, and B. Langmead: 'Lighter: fast and memory-efficient sequencing error correction without counting', Genome Biology, 2014, 15, (11), pp. 509.Google ScholarGoogle ScholarCross RefCross Ref
  23. L.A. Garraway, J. Verweij, and K.V. Ballman: 'Precision Oncology: An Overview', J. of Clinical Oncology, 2013Google ScholarGoogle ScholarCross RefCross Ref
  24. N. Alachiotis, E. Sotiriades, A. Dollas, and A. Stamatakis, "Exploring FPGAs for accelerating the phylogenetic likelihood function," IEEE Intl. Symp. Parallel Distributed Process., 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. N. Homer, B. Merriman, and S.F. Nelson: 'BFAST: An Alignment Tool for Large Scale Genome Resequencing', PLoS ONE, 2009, 4, (11), pp. e7767.Google ScholarGoogle ScholarCross RefCross Ref
  26. N. Malhis et al: 'Slider---maximum use of probability information for alignment of short sequence reads and SNP detection', Bioinformatics, 2009, 25, (1), pp. 6--13. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. P. Bogdan and R. Marculescu, "Non-stationary traffic analysis and its implications on multicore platform design," IEEE Trans. Computer-Aided Design of Integrated Circuits & Systems, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. P. Bogdan and R. Marculescu, "Towards a science of cyber-physical systems design," IEEE/ACM 2nd International Conference on Cyber-Physical Systems (ICCPS), 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. P. Bogdan et al, "Heterogeneous structure of stem cells dynamics: statistical models and quantitative predictions," Scientific reports, 4, 2014.Google ScholarGoogle Scholar
  30. P. Bogdan, "A cyber-physical systems approach to personalized medicine: challenges and opportunities for NoC-based multicore platforms," Design, Automation & Test in Europe Conference & Exhibition (DATE), 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. P. Bogdan, "Mathematical modeling and control of multifractal workloads for data-center-on-a-chip optimization," 9th IEEE/ ACM Intl. Symp. on Networks-on-Chip (NoCS), Sept. 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. P. Bogdan, T. Sauerwald, A. Stauffer, and H. Sun, "Balls into Bins via Local Search," 24th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. D. F. Easton et al, 'Gene-Panel Sequencing and the Prediction of Breast-Cancer Risk', New England Journal of Medicine, 2015, 372, (23), pp. 2243--2257.Google ScholarGoogle ScholarCross RefCross Ref
  34. R. Li et al, "De novo assembly of human genomes with massively parallel short read sequencing," Genome Research, 2010, 20, (2), pp. 265--272.Google ScholarGoogle ScholarCross RefCross Ref
  35. R. Baheti and H. Gill, "Cyber-physical systems," The Impact of Control Technology, T. Samad, A. M. Annaswamy (eds.) 2011.Google ScholarGoogle Scholar
  36. R. Durbin et al 'A map of human genome variation from population-scale sequencing', Nature, 2010, 467, (7319).Google ScholarGoogle Scholar
  37. R. Marculescu and P. Bogdan, "Cyberphysical systems: workload modeling and design optimization," IEEE Des. Test, vol. 28, issue 4, pp. 78--87, July 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. R. Marculescu and P. Bogdan, "The Chip Is the Network: Toward a Science of Network-on-Chip Design," Foundations and Trends in Electronic Design Automation, 2009.Google ScholarGoogle Scholar
  39. R. Wijngaart, T. Mattson, and W. Haas, "Light-weight communications on Intel's single-chip cloud computer processor," SIGOPS Oper. Syst. Rev. 45, 1, pp. 73--83, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. S. Cooper et al: 'Predicting protein structures with a multiplayer online game', Nature, 2010Google ScholarGoogle Scholar
  41. S. Sarkar, G.R. Kulkarni, P.P. Pande, A. Kalyanaraman, "Network-on-chip hardware accelerators for biological sequence alignment," IEEE Trans. on Computers, vol.59, pp.29--41, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. S. Yooseph et al., "The Sorcerer II Global Ocean Sampling Expedition: Expanding the Universe of Protein Families," Public Library of Science Biology, vol. 5, no. 3, 2007.Google ScholarGoogle Scholar
  43. J.R. Stiles, et al., "Miniature endplate current rise times <100 μs from improved dual recordings can be modeled with passive acetylcholine diffusion from a synaptic vesicle," Proc. Natl. Acad. Sci. USA 93:5747--5752, 1996.Google ScholarGoogle ScholarCross RefCross Ref
  44. T. Majumder, M.E. Borgens, P.P. Pande, A. Kalyanaraman, "On-chip network-enabled multicore platforms targeting maximum likelihood phylogeny reconstruction," IEEE Trans. on Computer-Aided Design of Integrated Circuits & Systems, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. T. Majumder, P.P. Pande, A. Kalyanaraman, "Wireless NoC platforms with dynamic task allocation for maximum likelihood phylogeny reconstruction," IEEE Design & Test, vol.31, no.3, pp.54,64, June 2014.Google ScholarGoogle ScholarCross RefCross Ref
  46. T. Majumder, P.P. Pande, A. Kalyanaraman, "Accelerating Maximum Likelihood Based Phylogenetic Kernels Using Network-on-Chip," Intl. Symp. on Computer Architecture and High Performance Computing (SBAC-PAD), 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. T. Majumder, P.P. Pande, A. Kalyanaraman, "High-throughput, energy-efficient network-on-chip-based hardware accelerators," Sustainable Computing: Informatics and Systems, Volume 3, Issue 1, pp. 36--46, March 2013.Google ScholarGoogle ScholarCross RefCross Ref
  48. T. Majumder, P.P. Pande, A. Kalyanaraman, "Network-on-chip with long-range wireless links for high-throughput scientific computation," Intl. Parallel & Distributed Processing Symp. Workshops (IPDPSW), 2013 Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. T. Majumder, S. Sarkar, P. Pande, A. Kalyanaraman, "An optimized NoC architecture for accelerating TSP kernels in breakpoint median problem," IEEE Intl. Conf. on Application - specific Systems Architectures and Processors(ASAP),2010.Google ScholarGoogle Scholar
  50. T. Majumder, S. Sarkar, P.P. Pande, A. Kalyanaraman, "NoC-based hardware accelerator for breakpoint phylogeny," IEEE Transactions on Computers, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. T. Majumder, X. Li, P. Bogdan, P.P. Pande, "NoC-enabled multicore architectures for stochastic analysis of biomolecular reactions," Design, Automation & Test in Europe (DATE), 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. T.J. Sharpton: 'An Introduction to the Analysis of Shotgun Metagenomic Data', Frontiers in Plant Science, 2014, 5.Google ScholarGoogle Scholar
  53. UniProt, A comprehensive, high-quality and freely accessible database of protein sequence and functional information, Online at: http://www.uniprot.org, Accessed on June 2015.Google ScholarGoogle Scholar
  54. W. Lee et al: 'MOSAIK: A Hash-Based Algorithm for Accurate Next-Generation Sequencing Short-Read Mapping', PLoS ONE, 2014, 9, (3), pp. e90581.Google ScholarGoogle ScholarCross RefCross Ref
  55. X.C Morgan, and C. Huttenhower: 'Chapter 12: Human Microbiome Analysis', PLoS Comput Biol, 2012Google ScholarGoogle ScholarCross RefCross Ref
  56. Y. Xue and P. Bogdan, "User Cooperation Network Coding Approach for NoC Performance Improvement," 9th IEEE/ ACM Intl. Symp. on Networks-on-Chip (NoCS), Sept. 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. Y. Xue et al, "An Efficient Network-on-Chip (NoC) based Multicore Platform for Hierarchical Parallel Genetic Algorithms," 8th IEEE/ACM Intl. Symp. on Networks-on-Chip (NoCS), pp.17--24, September 2014.Google ScholarGoogle Scholar
  58. Y. Xue et al, "Disease diagnosis-on-a-chip: large scale networks-on-chip based multicore platform for protein folding analysis," Design Automation Conf. (DAC), 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library

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