ITBAM'10 Proceedings of the First international conference on Information technology in bio- and medical informatics
BSB'10 Proceedings of the Advances in bioinformatics and computational biology, and 5th Brazilian conference on Bioinformatics
Inferring Contagion in Regulatory Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
The inference of breast cancer metastasis through gene regulatory networks
Journal of Biomedical Informatics
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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-κB and c-myc) for HeLa cells, we were able to predict the connectivity, Granger-causality and dynamics of the information flow in these networks. Contact: cef@ime.usp.br Supplementary information: Additional figures may be found at http://mariwork.iq.usp.br/dvar/