Granger causality in systems biology: modeling gene networks in time series microarray data using vector autoregressive models

Authors:
André Fujita;Patricia Severino;João Ricardo Sato;Satoru Miyano
Affiliations:
Computational Science Research Program, RIKEN, Wako, Saitama, Japan;Center for Experimental Research, Albert Einstein Research and Education Institute, São Paulo, Brazil;Center of Mathematics, Computation and Cognition, Universidade Federal do ABC, Santo André, Brazil;Computational Science Research Program, RIKEN, Wako, Saitama, Japan and Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
Venue:
BSB'10 Proceedings of the Advances in bioinformatics and computational biology, and 5th Brazilian conference on Bioinformatics
Year:
2010

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Abstract

Understanding the molecular biological processes underlying disease onset requires a detailed description of which genes are expressed at which time points and how their products interact in so-called cellular networks. High-throughput technologies, such as gene expression analysis using DNA microarrays, have been extensively used with this purpose. As a consequence, mathematical methods aiming to infer the structure of gene networks have been proposed in the last few years. Granger causality-based models are among them, presenting well established mathematical interpretations to directionality at the edges of the regulatory network. Here, we describe the concept of Granger causality and explore recent advances and applications in gene expression regulatory networks by using extensions of Vector Autoregressive models.