Simulation of Scale Free Gene Regulatory Networks based on Threshold Functions on GPU
Resumo
Gene regulatory networks have been used to study diseases and cell evolution, where Random Boolean graphs are one of computational approaches. A Boolean graph is a simple and effective model, and its dynamic behavior has been used in several works. This article proposes an efficient environment to simulate Boolean graph on GPU (Graphics Processing Units). The dynamic behavior of a Boolean graph is computed by visiting the whole or a subset of state space. The proposed tool is based on statistical approaches to evaluate large graphs. Moreover, it can take into account scale free graphs with threshold functions. The experimental results show a speed-up factor of up to 40 times. In addition, the exploration of state spaces three orders of magnitude greater than previous approaches have been evaluated.
Referências
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M. Aldana. Boolean dynamics of networks with scale-free topology. Physica D, 185:45–66, 2003.
A.-L. Barabasi and Z. N. Oltvai. Network biology: Understanding the cell’s function organization. Nature Reviews - Genetics, 5:101–113, 2004.
A. P. L. Carvalho, L. Ribeiro, and et alli. Grandes desafios da pesquisa em computação no brasil 2006-2016. Technical report, Sociedade Brasileira de Computação, 2006.
C. Darabos, F. Di Cunto, M. Tomassini, J. Moore, P. Provero, and M. Giacobini. Validating a threshold-based boolean model of regulatory networks on a biological organism. In C. Pizzuti, M. Ritchie, and M. Giacobini, editors, Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, volume 6623 of Lecture Notes in Computer Science, pages 59–68. Springer Berlin/Heidelberg, 2011.
A. Garg, A. D. Cara, I. Xenarios, L. Mendoza, and G. D. Micheli. Synchronous versus asynchronous modeling of gene regulatory networks. Bioinformatics Systems Biology, 24(17):1917–1925, 2008.
P. Harish and P. Narayanan. Accelerating large graph algorithms on the gpu using cuda. In S. Aluru, M. Parashar, R. Badrinath, and V. Prasanna, editors, High Performance Computing – HiPC 2007, volume 4873 of Lecture Notes in Computer Science, pages 197–208. Springer Berlin/Heidelberg, 2007.
K. Iguchi, S. Kinoshita, and H. Yamada. Boolean dynamics of kauffman models with scale-free networks. Journal of theorical biology, 247:138–151, 2007.
D. J. Irons. Improving the efficiency of attractor cycle identification in boolean networks. Physica D, 217:7–21, 2006.
S. A. Kauffman. Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theoret. Biology, 22:437–467, 1969.
S. A. Kauffman. The Origins of Order: Self-Organization and Selection in Evolution. Oxford University Press, USA, 1 edition, June 1993.
G. D. Micheli. An outlook on design technologies for future integrated systems. IEEE Trans. Comp.-Aided Des. Integ. Cir. Sys., 28(6):777–790, 2009.
J. Nickolls and W. J. Dally. The gpu computing era. IEEE Micro, 30(2):56–69, 2010.
F. Pereira. Atualizações em informática 2011, pages 259–302. Sociedade Brasileira de Computação, Editora PuC Rio, Natal, Brasil, 2011.
I. Pournara, C. Bouganis, and G. Constantinides. Fpgaaccelerated bayesian learning for reconstruction of gene regulatory networks. In IEEE Field Programmable Logic Conference, pages 323–328. IEEE Computer Society, 2005.
I. Tagkopoulos, C. Zukowski, G. Cavelier, and D. Anastassiou. A custom fpga for the simulation of gene regulatory networks. In 13th ACM Great Lakes Symposium on VLSI, 2003.
M. Zerarka, J. David, and E. M. Aboulhamid. High speed emulation of gene regulatory networks using fpgas. In 47th IEEE International MidWest Symposium on Circuits and Systems, pages 545–548, 2004.
S.-Q. Zhang, M. Hayashida, T. Akutsu, W.-K. Ching, and M. Ng. Algorithms for finding small attractors in boolean networks. Eurasip Journal on Bioinformatics and System Biology, pages 1–13, 2007.
M. Aldana. Boolean dynamics of networks with scale-free topology. Physica D, 185:45–66, 2003.
A.-L. Barabasi and Z. N. Oltvai. Network biology: Understanding the cell’s function organization. Nature Reviews - Genetics, 5:101–113, 2004.
A. P. L. Carvalho, L. Ribeiro, and et alli. Grandes desafios da pesquisa em computação no brasil 2006-2016. Technical report, Sociedade Brasileira de Computação, 2006.
C. Darabos, F. Di Cunto, M. Tomassini, J. Moore, P. Provero, and M. Giacobini. Validating a threshold-based boolean model of regulatory networks on a biological organism. In C. Pizzuti, M. Ritchie, and M. Giacobini, editors, Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, volume 6623 of Lecture Notes in Computer Science, pages 59–68. Springer Berlin/Heidelberg, 2011.
A. Garg, A. D. Cara, I. Xenarios, L. Mendoza, and G. D. Micheli. Synchronous versus asynchronous modeling of gene regulatory networks. Bioinformatics Systems Biology, 24(17):1917–1925, 2008.
P. Harish and P. Narayanan. Accelerating large graph algorithms on the gpu using cuda. In S. Aluru, M. Parashar, R. Badrinath, and V. Prasanna, editors, High Performance Computing – HiPC 2007, volume 4873 of Lecture Notes in Computer Science, pages 197–208. Springer Berlin/Heidelberg, 2007.
K. Iguchi, S. Kinoshita, and H. Yamada. Boolean dynamics of kauffman models with scale-free networks. Journal of theorical biology, 247:138–151, 2007.
D. J. Irons. Improving the efficiency of attractor cycle identification in boolean networks. Physica D, 217:7–21, 2006.
S. A. Kauffman. Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theoret. Biology, 22:437–467, 1969.
S. A. Kauffman. The Origins of Order: Self-Organization and Selection in Evolution. Oxford University Press, USA, 1 edition, June 1993.
G. D. Micheli. An outlook on design technologies for future integrated systems. IEEE Trans. Comp.-Aided Des. Integ. Cir. Sys., 28(6):777–790, 2009.
J. Nickolls and W. J. Dally. The gpu computing era. IEEE Micro, 30(2):56–69, 2010.
F. Pereira. Atualizações em informática 2011, pages 259–302. Sociedade Brasileira de Computação, Editora PuC Rio, Natal, Brasil, 2011.
I. Pournara, C. Bouganis, and G. Constantinides. Fpgaaccelerated bayesian learning for reconstruction of gene regulatory networks. In IEEE Field Programmable Logic Conference, pages 323–328. IEEE Computer Society, 2005.
I. Tagkopoulos, C. Zukowski, G. Cavelier, and D. Anastassiou. A custom fpga for the simulation of gene regulatory networks. In 13th ACM Great Lakes Symposium on VLSI, 2003.
M. Zerarka, J. David, and E. M. Aboulhamid. High speed emulation of gene regulatory networks using fpgas. In 47th IEEE International MidWest Symposium on Circuits and Systems, pages 545–548, 2004.
S.-Q. Zhang, M. Hayashida, T. Akutsu, W.-K. Ching, and M. Ng. Algorithms for finding small attractors in boolean networks. Eurasip Journal on Bioinformatics and System Biology, pages 1–13, 2007.
Publicado
26/10/2011
Como Citar
CAMPOS, Raphael R.; FERREIRA, Ricardo; VENDRAMINI, Julio C. Goldner; CERQUEIRA, Fábio; MARTINS, Marcelo Lobato.
Simulation of Scale Free Gene Regulatory Networks based on Threshold Functions on GPU. In: SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (SSCAD), 12. , 2011, Vitória.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2011
.
p. 81-88.
DOI: https://doi.org/10.5753/wscad.2011.17271.
