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Bioinformatics Advance Access published online on February 4, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti308
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received January 9, 2005
Revised February 2, 2005
Accepted February 3, 2005

Applications note

Time accelerated monte carlo simulations of biological networks using the binomial {tau}-leap method

Abhijit Chatterjee 1, Kapil Mayawala 1, Jeremy S. Edwards 1, and Dionisios G. Vlachos 1*

1 Department of Chemical Engineering, University of Delaware, Newark, DE 19716, USA

* To whom correspondence should be addressed.
Dionisios G. Vlachos, E-mail: vlachos{at}che.udel.edu


  Abstract

Summary: Developing a quantitative understanding of intracellular networks requires simulations and computational analyses. However, traditional differential equation modeling tools are often inadequate due to the stochasticity of intracellular reaction networks that can potentially influence the phenotypic characteristics. Unfortunately, stochastic simulations are computationally too intense for most biological systems. Herein, we have utilized the recently developed binomial {tau}-leap method (Chatterjee et al., 2005) to carry out stochastic simulations of the epidermal growth factor (EGF) receptor induced mitogen activated protein (MAP) kinase cascade. Results indicate that the binomial {tau}-leap method is computationally 100-1000 times more efficient than the exact stochastic simulation algorithm of Gillespie. Furthermore, the binomial t-leap method avoids negative populations and accurately captures the species populations along with their fluctuations despite the large difference in their size.

Availability: http://dion.che.udel.edu/multiscale/Introduction.html. Fortran 90 code available for academic use by email.

Supplementary information: Details about the binomial {tau}-leap algorithm, software and a manual are available at the above website.


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