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In silico simulation of biological network dynamics

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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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Figure 1
Figure 2: Stochastic simulation of the Michaelis-Menten kinetics.
Figure 3: Stochastic simulation of lacZ expression.

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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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Authors and Affiliations

  1. UCLA-DOE Institute for Genomics and Proteomics, Howard Hughes Medical Institute, Molecular Biology Institute, Box 951570, Los Angeles, 90095-1570, California, USA

    Lukasz Salwinski & David Eisenberg

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  1. Lukasz Salwinski
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  2. David Eisenberg
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Correspondence to David Eisenberg.

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The authors declare no competing financial interests.

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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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