The sorting direct method for stochastic simulation of biochemical systems with varying reaction execution behavior

Authors:
James M. McCollum;Gregory D. Peterson;Chris D. Cox;Michael L. Simpson;Nagiza F. Samatova
Affiliations:
Computational Biology Institute, Oak Ridge National Laboratory, P.O. Box 2008 MS6164, Oak Ridge, TN 37831, USA and Department of Electrical and Computer Engineering, University of Tennessee, 414 F ...;Department of Electrical and Computer Engineering, University of Tennessee, 414 Ferris Hall, Knoxville, TN 37996-2100, USA and Center for Environmental Biotechnology, University of Tennessee, 676 ...;Center for Environmental Biotechnology, University of Tennessee, 676 Dabney Hall, Knoxville, TN 37996-1605, USA and Department of Civil and Environmental Engineering, University of Tennessee, 233 ...;Center for Environmental Biotechnology, University of Tennessee, 676 Dabney Hall, Knoxville, TN 37996-1605, USA and Molecular Scale Engineering and Nanoscale Technologies Research Group, Oak Ridge ...;Computational Biology Institute, Oak Ridge National Laboratory, P.O. Box 2008 MS6164, Oak Ridge, TN 37831, USA
Venue:
Computational Biology and Chemistry
Year:
2006

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Abstract

A key to advancing the understanding of molecular biology in the post-genomic age is the development of accurate predictive models for genetic regulation, protein interaction, metabolism, and other biochemical processes. To facilitate model development, simulation algorithms must provide an accurate representation of the system, while performing the simulation in a reasonable amount of time. Gillespie's stochastic simulation algorithm (SSA) accurately depicts spatially homogeneous models with small populations of chemical species and properly represents noise, but it is often abandoned when modeling larger systems because of its computational complexity. In this work, we examine the performance of different versions of the SSA when applied to several biochemical models. Through our analysis, we discover that transient changes in reaction execution frequencies, which are typical of biochemical models with gene induction and repression, can dramatically affect simulator performance. To account for these shifts, we propose a new algorithm called the sorting direct method that maintains a loosely sorted order of the reactions as the simulation executes. Our measurements show that the sorting direct method performs favorably when compared to other well-known exact stochastic simulation algorithms.