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Granger Causality: Its Foundation and Applications in Systems Biology

Granger Causality: Its Foundation and Applications in Systems Biology

Tian Ge, Jianfeng Feng
Copyright: © 2011 |Pages: 22
ISBN13: 9781609604912|ISBN10: 1609604911|EISBN13: 9781609604929
DOI: 10.4018/978-1-60960-491-2.ch022
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MLA

Ge, Tian, and Jianfeng Feng. "Granger Causality: Its Foundation and Applications in Systems Biology." Handbook of Research on Computational and Systems Biology: Interdisciplinary Applications, edited by Limin Angela Liu, et al., IGI Global, 2011, pp. 511-532. https://doi.org/10.4018/978-1-60960-491-2.ch022

APA

Ge, T. & Feng, J. (2011). Granger Causality: Its Foundation and Applications in Systems Biology. In L. Liu, D. Wei, Y. Li, & H. Lei (Eds.), Handbook of Research on Computational and Systems Biology: Interdisciplinary Applications (pp. 511-532). IGI Global. https://doi.org/10.4018/978-1-60960-491-2.ch022

Chicago

Ge, Tian, and Jianfeng Feng. "Granger Causality: Its Foundation and Applications in Systems Biology." In Handbook of Research on Computational and Systems Biology: Interdisciplinary Applications, edited by Limin Angela Liu, et al., 511-532. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60960-491-2.ch022

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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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