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Bioinformatics Advance Access published online on July 9, 2008

Bioinformatics, doi:10.1093/bioinformatics/btn336
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Synchronous vs. Asynchronous Modeling of Gene Regulatory Networks

Abhishek Garg a,*, Alessandro DiCara b, Ioannis Xenarios c, Luis Mendoza d and Giovanni De Micheli a

a Ecole Polytechnique Federale de Lausanne, Station 14, 1015 Lausanne, Switzerland.
b Merck Serono, Geneva, Switzerland.
c Swiss Institute of Bioinformatics. Vital-IT Group. 1015 Lausanne, Switzerland.
d Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, México.

*To whom correspondence should be addressed. Abhishek Garg, E-mail: abhishek.garg{at}epfl.ch


   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 analysed. 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: Dr. Olga Troyanskaya


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

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