Skip Navigation


Bioinformatics Advance Access originally published online on July 9, 2008
Bioinformatics 2008 24(17):1917-1925; doi:10.1093/bioinformatics/btn336
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrowOA All Versions of this Article:
24/17/1917    most recent
btn336v1
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 ISI Web of Science
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
Google Scholar
Right arrow Articles by Garg, A.
Right arrow Articles by De Micheli, G.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Garg, A.
Right arrow Articles by De Micheli, G.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Synchronous versus asynchronous modeling of gene regulatory networks

Abhishek Garg 1,*, Alessandro Di Cara 2, Ioannis Xenarios 3, Luis Mendoza 4 and Giovanni De Micheli 1

1Ecole Polytechnique Federale de Lausanne, Station 14, 1015 Lausanne, 2Merck Serono, Geneva, 3Swiss Institute of Bioinformatics, Vital-IT Group, 1015 Lausanne, Switzerland and 4Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de Méexico, México

*To whom correspondence should be addressed.


  Abstract

Motivation: In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene–gene, protein–protein and gene–protein interactions. The two dynamical properties that are often experimentally testable are perturbations and stable steady states. Although a lot of work has been done on the identification of steady states, not much work has been reported on in silico modeling of cellular differentiation processes.

Results: In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks. Algorithms for synchronous and asynchronous transition models have been proposed and their corresponding computational properties have been analyzed. These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software.

Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes. These algorithms were validated on the T-helper model showing the correct steady state identification and Th1–Th2 cellular differentiation process.

Availability: The software binaries for Windows and Linux platforms can be downloaded from http://si2.epfl.ch/~garg/genysis.html.

Contact: abhishek.garg{at}epfl.ch

Associate Editor: Olga Troyanskaya

Received on February 1, 2008; revised on June 10, 2008; accepted on July 1, 2008

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
W.-P. Lee and W.-S. Tzou
Computational methods for discovering gene networks from expression data
Brief Bioinform, July 1, 2009; 10(4): 408 - 423.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
A. Garg, K. Mohanram, A. Di Cara, G. De Micheli, and I. Xenarios
Modeling stochasticity and robustness in gene regulatory networks
Bioinformatics, June 15, 2009; 25(12): i101 - i109.
[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.