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SIAM J. on Scientific Computing / Volume 33 / Issue 6

SIAM J. Sci. Comput. 33, pp. 3402-3424 (23 pages)

Simulation, Characterization, and Optimization of Metabolic Models with the High Performance Systems Biology Toolkit

Monte Lunacek, Ambarish Nag, David M. Alber, Kenny Gruchalla, Christopher H. Chang, and Peter A. Graf

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The High Performance Systems Biology Toolkit (HiPer SBTK) is a collection of simulation and optimization components for metabolic modeling and the means to assemble these components into large parallel processing hierarchies suiting a particular simulation and optimization need. The components come in a variety of different categories: model translation, model simulation, parameter sampling, sensitivity analysis, parameter estimation, and optimization. They can be configured at runtime into hierarchically parallel arrangements to perform nested combinations of simulation characterization tasks with excellent parallel scaling to thousands of processors. We describe the observations that led to the system, the components, and how one can arrange them. We show nearly 90% efficient scaling to over 13,000 processors, and we demonstrate three complex yet typical examples that have run on $\sim$1000 processors and accomplished billions of stiff ordinary differential equation simulations. This work opens the door for the systems biology metabolic modeling community to take effective advantage of large scale high performance computing resources for the first time.

© 2011 Society for Industrial and Applied Mathematics

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KEYWORDS

AMS Subject Headings

92C42, 93C15, 90C31, 34A55, 65L04, 65L05

PUBLICATION DATA

ISSN

1064-8275 (print)  
1095-7197 (online)

Publisher

$abstract.society.value is a Member of CrossRef Society for Industrial and Applied Mathematics

ARTICLE DATA

History
Received January 28, 2011
Accepted August 12, 2011
Published online December 13, 2011
Digital Object Identifier
http://dx.doi.org/10.1137/110822402

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