Granger Causality: Its Foundation and Applications in Systems Biology

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Author(s): Tian Ge (Fudan University, China)and Jianfeng Feng (Fudan University, China & University of Warwick, UK)
Copyright: 2011
Pages: 22
Source title: Handbook of Research on Computational and Systems Biology: Interdisciplinary Applications
Source Author(s)/Editor(s): Limin Angela Liu (Shanghai Jiao Tong University, China), Dongqing Wei (Shanghai Jiao Tong University, China), Yixue Li (Shanghai Jiao Tong University, China)and Huimin Lei (Shanghai Jiao Tong University, China)
DOI: 10.4018/978-1-60960-491-2.ch022

Keywords: Bioinformatics / Biologically-Inspired Computing / Medical Information Science Reference / Medicine & Healthcare

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Abstract

As one of the most successful approaches to uncover complex network structures from experimental data, Granger causality has been widely applied to various reverse engineering problems. This chapter first reviews some current developments of Granger causality and then presents the graphical user interface (GUI) to facilitate the application. To make Granger causality more computationally feasible and satisfy biophysical constraints for dealing with increasingly large dynamical datasets, two attempts are introduced including the combination of Granger causality and Basis Pursuit when faced with non-uniformly sampled data and the unification of Granger causality and the Dynamic Causal Model as a novel Unified Causal Model (UCM) to bring in the notion of stimuli and modifying coupling. Several examples, both from toy models and real experimental data, are included to demonstrate the efficacy and power of the Granger causality approach.

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