Skip Navigation



Bioinformatics Advance Access published online on January 22, 2004

Bioinformatics, doi:10.1093/bioinformatics/btg442
Bioinformatics © Oxford University Press 2004; all rights reserved
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow All Versions of this Article:
20/4/538    most recent
btg442v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Takahashi, K.
Right arrow Articles by Tomita, M.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Takahashi, K.
Right arrow Articles by Tomita, M.
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Received April 29, 2003
Revised July 14, 2003
Accepted August 19, 2003

Article

A multi-algorithm, multi-timescale method for cell simulation

Kouichi Takahashi 1*, Kazunari Kaizu 1, Bin Hu 1, Masaru Tomita 1

1 Institute for Advanced Biosciences, Keio University, Fujisawa, Kanagawa, 252-8520, Japan

* To whom correspondence should be addressed. E-mail: shafi{at}e-cell.org.


   Abstract

Motivation Many important problems in cell biology require the dense nonlinear interactions between functional modules to be considered. The importance of computer simulation in understanding cellular processes is now widely accepted, and a variety of simulation algorithms useful for studying certain sub-systems have been designed. Many of these are already widely used, and a large number of models constructed on these existing formalisms are available. A significant computational challenge is how we can integrate such sub-cellular models running on different types of algorithms to construct higher order models.

Results A modular, object-oriented simulation meta-algorithm based on a discrete-event scheduler and Hermite polynomial interpolation has been developed and implemented. It is shown that this new method can efficiently handle many components driven by different algorithms and different timescales. The utility of this simulation framework is further demonstrated with a "composite" heat-shock response model that combines the Gillespie-Gibson stochastic algorithm and deterministic differential equations. Dramatic improvements in performance were obtained without significant accuracy drawbacks. A multi-timescale demonstration of coupled harmonic oscillators is also shown.

Availability An implementation of the method is available as part of E-Cell Simulation Environment Version 3 downloadable from http://www.e-cell.org/software. Benchmark models are included in the package, and also available upon request.

Supplement Complete lists of reactions and parameters of the heat-shock model, and more results are available at http://www.e-cell.org/bioinfo/takahashi03-1-supp.pdf.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
Brief BioinformHome page
J. Pahle
Biochemical simulations: stochastic, approximate stochastic and hybrid approaches
Brief Bioinform, January 16, 2009; (2009) bbn050v1.
[Abstract] [Full Text] [PDF]


Home page
Brief BioinformHome page
D. Gilbert, H. Fuss, X. Gu, R. Orton, S. Robinson, V. Vyshemirsky, M. J. Kurth, C. S. Downes, and W. Dubitzky
Computational methodologies for modelling, analysis and simulation of signalling networks
Brief Bioinform, December 1, 2006; 7(4): 339 - 353.
[Abstract] [Full Text] [PDF]


Home page
BioinformaticsHome page
M. Griffith, T. Courtney, J. Peccoud, and W. H. Sanders
Dynamic partitioning for hybrid simulation of the bistable HIV-1 transactivation network
Bioinformatics, November 15, 2006; 22(22): 2782 - 2789.
[Abstract] [Full Text] [PDF]


Home page
BioinformaticsHome page
T. Emonet, C. M. Macal, M. J. North, C. E. Wickersham, and P. Cluzel
AgentCell: a digital single-cell assay for bacterial chemotaxis
Bioinformatics, June 1, 2005; 21(11): 2714 - 2721.
[Abstract] [Full Text] [PDF]


Home page
BioinformaticsHome page
A. Pettinen, T. Aho, O.-P. Smolander, T. Manninen, A. Saarinen, K.-L. Taattola, O. Yli-Harja, and M.-L. Linne
Simulation tools for biochemical networks: evaluation of performance and usability
Bioinformatics, February 1, 2005; 21(3): 357 - 363.
[Abstract] [Full Text] [PDF]



Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.