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Abstract

Modeling gene regulatory networks has, in some cases, enabled biologists to predict cellular behavior long before such behavior can be experimentally validated. Unfortunately, the extent to which biologists can take advantage of these modeling techniques is limited by the computational complexity of gene regulatory network simulation algorithms. This study presents a new platform-independent, grid-based distributed computing environment that accelerates biological model simulation and, ultimately, development. Applying this environment to gene regulatory network simulation shows a significant reduction in execution time versus running simulation jobs locally. To analyze this improvement, a performance model of the distributed computing environment is built. Although this grid-based system was specifically developed for biological simulation, the techniques discussed are applicable to a variety of simulation performance problems.

Keywords

Computational biology grid-based simulation gene regulatory networks performance model

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References

[1] Casanova, H. , and J. Dongarra . 1997. NetSolve: A network-enabled server for solving computational science problems . International Journal of Supercomputer Applications and High Performance Computing 11 (3): 212-223 .

[2] Thain, D. , T. Tannenbaum , and M. Livny . 2003. Condor and the Grid. In Grid computing: Making the global infrastructure a reality, edited by F. Berman , A. J. G. Hey , and G. Fox . New York: John Wiley .

[3] Foster, I. , and C. Kesselman , eds. 1999. The grid: Blueprint for a new computing infrastructure. New York: Morgan Kaufmann .

[4] Smolen, P. , D. A. Baxter , and J. H. Byrne . 2000. Modeling transcriptional control in gene networks: Methods, recent results, and future directions . Bulletin of Mathematical Biology 62: 247-292 .

[5] Marlovits, G. , C. J. Tyson , B. Novak , and J. J. Tyson . 1998. Modeling M-phase control in Xenopus oocyte extracts: The surveillance mechanism for unreplicated DNA . Biophysical Chemistry 72: 169-184 .

[6] Novak, B. , and J. J. Tyson . 1993. Modeling the cell-division cycle: M-phase trigger, oscillations, and size control . Journal of Theoretical Biology 165: 101-134 .

[7] Sha, W. , J. Moore , K. Chen , A. D. Lassaletta , C.-S. Yi , J. J. Tyson , and J. C. Sible . 2003. Hysteresis drives cell-cycle transitions in Xenopus laevis egg extracts . Proceedings of the National Academy of Sciences of the United States of America 100: 975-980 .

[8] Arkin, A. , J. Ross , and H. H. McAdams . 1998. Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells . Genetics 149: 1633-1648 .

[9] Endy, D. , D. Kong , and J.Yin . 1997. Intracellular kinetics of a growing virus: A genetically structured simulation for bacteriophage T7 . Biotechnology and Bioengineering 55: 375-389 .

10.

[10] Gillespie, D. T. 1977. Exact stochastic simulation of coupled chemical reactions . Journal of Physical Chemistry 81: 2340-2361 .

11.

[11] Gibson, M. A. , and J. Bruck . 2000. Efficient exact stochastic simulation of chemical systems with many species and many channels . Journal of Physical Chemistry A 104: 1876-1889 .

12.

[12] Cox, C. D. , G. D. Peterson , M. S. Allen , J. M. Lancaster , J. M. McCollum , D. Austin , L. Yan , G. S. Sayler , and M. L. Simpson . 2003. Analysis of noise in quorum sensing . OMICS: A Journal of Integrative Biology 7 (3): 317-334 .

13.

[13] Gillespie, D. T. 2001. Approximate accelerated stochastic simulation of chemically reacting systems . Journal of Chemical Physics 115 (4): 1716-1733 .

14.

[14] Endy, D. , and R. Brent . 2001. Modeling cellular behaviour . Nature 409: 391-395 .

15.

[15] Lehrter, J. M. , F. N.Abu-Khzam , D.W. Bouldin , M.A. Langston , and G. D. Peterson . 2002. On special-purpose hardware clusters for high-performance computational grids . In Proceedings of the 14th IASTED International Conference on Parallel and Distributed Computing and Systems.

16.

[16] McCollum, J. M. , J. M. Lancaster , and G. D. Peterson . 2003. Using reconfigurable computing to accelerate simulation applications . In Proceedings of the International Conference on Engineering of Reconfigurable Systems and Algorithms, pp. 308-311 .

17.

[17] Sunderam, V. S. 1990. PVM: A framework for parallel distributed computing . Currency: Practice and Experience 2 (4): 315-330 .

18.

[18] Snir, M. , S. Otto , S. Huss-Lederman , D. Walker , and J. Dongarra . 1998. MPI: The complete reference. 2d ed. Cambridge, MA: MIT Press .

19.

[19] Papoulis, A. 1984. Probability, random variables, and stochastic processes. New York: McGraw-Hill .

20.

[20] Peterson, G. D. , and R. D. Chamberlain . 1996. Parallel application performance in a shared resource environment . IEE Distributed Systems Engineering Journal 3 (1): 9-19 .

21.

[21] Leland, W. , and T. Ott . 1986. Load balancing heuristics and process behavior . In Proceedings of PERFORMANCE ’86 and ACM SIGMETRICS, Raleigh, NC, pp. 54-69 .

22.

[22] Fujimoto, R. M. 1990. Parallel discrete event simulation . Communications of the ACM 33 (10): 30-53 .

23.

[23] Peterson, G. D. 2001. Performance tradeoffs for emulation, hardware acceleration, and simulation. In System on chip methodologies and design languages, edited by Jean Mermet . New York: Kluwer Academic .

24.

[24] Greenberg, E. P. 2000. Acyl-homoserine lactone quorum sensing in bacteria . Journal of Microbiology 38: 117-121 .

25.

[25] Withers, H. , S. Swift , and P. Williams . 2001. Quorum sensing as an integral component of gene regulatory networks in Gram-negative bacteria . Current Opinions on Microbiology 4: 186-193 .

Accelerating Gene Regulatory Network Modeling Using Grid-Based Simulation

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