Time accelerated Monte Carlo simulations of biological networks using the binomial τ-leap method

Authors:
Abhijit Chatterjee;Kapil Mayawala;Jeremy S. Edwards;Dionisios G. Vlachos
Affiliations:
Department of Chemical Engineering, University of Delaware Newark, DE 19716, USA;Department of Chemical Engineering, University of Delaware Newark, DE 19716, USA;Department of Chemical Engineering, University of Delaware Newark, DE 19716, USA;Department of Chemical Engineering, University of Delaware Newark, DE 19716, USA
Venue:
Bioinformatics
Year:
2005

Citing 0
Cited 5

Parallel Implementation of Stochastic Simulation for Large-scale Cellular Processes

HPCASIA '05 Proceedings of the Eighth International Conference on High-Performance Computing in Asia-Pacific Region
Temporal acceleration of spatially distributed kinetic Monte Carlo simulations

Journal of Computational Physics
SSALeaping: efficient leap condition based direct method variant for the stochastic simulation of chemical reacting system

Proceedings of the 3rd International ICST Conference on Simulation Tools and Techniques
A Survey on Methods for Modeling and Analyzing Integrated Biological Networks

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Stronger computational modelling of signalling pathways using both continuous and discrete-state methods

CMSB'06 Proceedings of the 2006 international conference on Computational Methods in Systems Biology

Quantified Score

Hi-index	3.84

Visualization

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 τ-leap method to carry out stochastic simulations of the epidermal growth factor receptor induced mitogen activated protein kinase cascade. Results indicate that the binomial τ-leap method is computationally 100--1000 times more efficient than the exact stochastic simulation algorithm of Gillespie. Furthermore, the binomial τ-leap method avoids negative populations and accurately captures the species populations along with their fluctuations despite the large difference in their size. Availability:http://www.dion.che.udel.edu/multiscale/Introduction.html. Fortran 90 code available for academic use by email. Contact: vlachos@che.udel.edu Supplementary information: Details about the binomial τ-leap algorithm, software and a manual are available at the above website.