Abstract
Realistic simulation of biological networks requires stochastic simulation approaches because of the small numbers of molecules per cell. The high computational cost of stochastic simulation on conventional microprocessor-based computers arises from the intrinsic disparity between the sequential steps executed by a microprocessor program and the highly parallel nature of information flow within biochemical networks. This disparity is reduced with the Field Programmable Gate Array (FPGA)-based approach presented here. The parallel architecture of FPGAs, which can simulate the basic reaction steps of biological networks, attains simulation rates at least an order of magnitude greater than currently available microprocessors.
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References
Hartwell, L.H., Hopfield, J.J., Leibler, S. & Murray, A.W. From molecular to modular cell biology. Nature 402, C47–C52 (1999).
Bray, D. Protein molecules as computational elements in living cells. Nature 376, 307–312 (1995).
Tyson, J.J., Chen, K.C. & Novak, B. Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell. Curr. Opin. Cell Biol. 15, 221–231 (2003).
Levchenko, A. Dynamical and integrative cell signaling: Challenges for the new biology. Biotechnol. Bioeng. 84, 773–782 (2003).
Gillespie, D.T. Exact stochastic simulation of coupled chemical-reactions. J. Phys. Chem. Us. 81, 2340–2361 (1977).
Gibson, M.A. & Bruck, J. Efficient exact stochastic simulation of chemical systems with many species and many channels. J. Phys. Chem. A 104, 1876–1889 (2000).
Haseltine, E.L. & Rawlings, J.B. Approximate simulation of coupled fast and slow reactions for stochastic chemical kinetics. J. Chem. Phys. 117, 6959–6969 (2002).
Gillespie, D.T. & Petzold, L.R. Improved leap-size selection for accelerated stochastic simulation. J. Chem. Phys. 119, 8229–8234 (2003).
Rao, C.V. & Arkin, A.P. Stochastic chemical kinetics and the quasi-steady-state assumption: application to the Gillespie algorithm. J. Chem. Phys. 118, 4999–5010 (2003).
Arkin, A., Ross, J. & McAdams, H.H. Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells. Genetics 149, 1633–1648 (1998).
Kastner, J., Solomon, J. & Fraser, S. Modeling a Hox gene network in silico using a stochastic simulation algorithm. Dev. Biol. 246, 122–131 (2002).
Endy, D. & Brent, R. Modelling cellular behaviour. Nature 409, 391–395 (2001).
Kierzek, A.M. STOCKS: STOChastic kinetic Simulations of biochemical systems with Gillespie algorithm. Bioinformatics 18, 470–481 (2002).
Pearson, R.B. An algorithm for pseudo random number generation suitable for large-scale integration. J. Comput. Phys. 49, 478–489 (1983).
Kierzek, A.M., Zaim, J. & Zielenkiewicz, P. The effect of transcription and translation initiation frequencies on the stochastic fluctuations in prokaryotic gene expression. J. Biol. Chem. 276, 8165–8172 (2001).
Acknowledgements
We thank Robert Grothe for helpful discussion, Stefan Czarnecki for engineering advice and an anonymous reviewer for valuable comments. This work was supported by the Howard Hughes Medical Institute, Department of Energy and National Institutes of Health.
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Salwinski, L., Eisenberg, D. In silico simulation of biological network dynamics. Nat Biotechnol 22, 1017–1019 (2004). https://doi.org/10.1038/nbt991
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DOI: https://doi.org/10.1038/nbt991
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