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Bioinformatics Advance Access published online on April 26, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm151
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Time-varying modeling of gene expression regulatory networks using the wavelet dynamic vector autoregressive method

A Fujita 1,2, JR Sato 1, HM Garay-Malpartida 2, PA Morettin 1, MC Sogayar 2 and CE Ferreira 1,*

1Institute of Mathematics and Statistics, University of São Paulo, Rua do Matão, 1010 – São Paulo, 05508-090, SP, Brazil.
2Chemistry Institute, University of São Paulo, Cell and Molecular Biology Laboratory, Av. Lineu Prestes, 748 – São Paulo, 05508-900, SP, Brazil.

*To whom correspondence should be addressed. Ferreira CE, E-mail: cef{at}ime.usp.br


  Abstract

Motivation: A variety of biological cellular processes are achieved through a variety of extracellular regulators, signal transduction, protein-protein interactions and differential gene expression. Understanding of the mechanisms underlying these processes requires detailed molecular description of the protein and gene networks involved. To better understand these molecular networks, we propose a statistical method to estimate time-varying gene regulatory networks from time series microarray data. One well known problem when inferring connectivity in gene regulatory networks is the fact that the relationships found constitute correlations that do not allow inferring causation, for which, a priori biological knowledge is required. Moreover, it is also necessary to know the time period at which this causation occurs. Here, we present the Dynamic Vector Autoregressive model as a solution to these problems.

Results: We have applied the Dynamic Vector Autoregressive model to estimate time-varying gene regulatory networks based on gene expression profiles obtained from microarray experiments. The network is determined entirely based on gene expression profiles data, without any prior biological knowledge. Through construction of three gene regulatory networks (of p53, NF-{kappa}B and c-myc) for HeLa cells, we were able to predict the connectivity, Granger-causality and dynamics of the information flow in these networks.

Supplementary information: Additional figures may be found at http://mariwork.iq.usp.br/dvar/.

Associate Editor: Prof. John Quackenbush

Received on January 31, 2007; revised on March 27, 2007; accepted on April 15, 2007

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