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.
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References
Arnold, A., Liu, Y., Abe, N.: Temporal causal modeling with graphical Granger methods. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, San Jose, pp. 66–75 (2007)
Baccala, L., Sameshima, K.: Partial directed coherence: a new concept in neural structure determination. Biological Cybernetics 84, 463–474 (2001)
Bigg, H.F., Shi, Y.E., Liu, Y.E., Steffensen, B., Overall, C.M.: Specific, high affinity binding of tissue inhibitor of metalloproteinases-4 (TIMP-4) to the COOH-terminal hemopexin-like domain of human gelatinase A. TIMP-4 binds progelatinase A and the COOH-terminal domain in a similar manner to TIMP-2. J. Biol. Chem. 272, 15496–15500 (1997)
Chang, H.C., Cho, C.Y., Hung, W.C.: Silencing of the metastasis suppressor RECK by RAS oncogene is mediated by DNA methyltransferase 3b-induced promoter methylation. Cancer Res. 66, 8413–8420 (2006)
Chang, H., Lee, J., Poo, H., Noda, M., Diaz, T., Wei, B., Stetler-Stevenson, W.G., Oh, J.: TIMP-2 promotes cell spreading and adhesion via upregulation of Rap1 signaling. Biochem. Biophys. Res. Commun. 7, 1201–1206 (2006)
Fujita, A., Sato, J.R., Garay-Malpartida, H.M., Morettin, P.A., Sogayar, M.C., Ferreira, C.E.: Time-varying modeling of gene expression regulatory networks using the wavelet dynamic vector autoregressive method. Bioinformatics 23, 1623–1630 (2007a)
Fujita, A., Sato, J.R., Garay-Malpartida, H.M., Yamaguchi, R., Miyano, S., Sogayar, M.C., Ferreira, C.E.: Modeling gene expression regulatory networks with the sparse vector autoregressive model. BMC Systems Biology 1, 39 (2007b)
Fujita, A., Sato, J.R., Garay-Malpartida, H.M., Sogayar, M.C., Ferreira, C.E., Miyano, S.: Modeling nonlinear gene regulatory networks from time series gene expression data. Journal of Bioinformatics and Computational Biology 6, 961–979 (2008)
Fujita, A., Patriota, A.G., Sato, J.R., Miyano, S.: The impact of measurement errors in the identification of regulatory networks. BMC Bioinformatics 10, 412 (2009)
Fujita, A., Sato, J.R., Kojima, K., Gomes, L.R., Nagasaki, M., Sogayar, M.C., Miyano, S.: Identification of Granger causality between gene sets. Journal of Bioinformatics and Computational Biology (in press)
Granger, C.W.J.: Investigating causal relationships by econometric models and cross-spectral methods. Econometrica 37, 424–438 (1969)
Guo, S., Wu, J., Ding, M., Feng, J.: Uncovering interactions in the frequency domain. PLoS Computational Biology 4, e1000087 (2008a)
Guo, S., Seth, A.K., Kendrick, K.M., Zhou, C., Feng, J.: Partial Granger causality - Eliminating exogenous inputs and latent variables. Journal of Neuroscience Methods 172, 79–83 (2008b)
Hsu, M.C., Chang, H.C., Hung, W.C.: HER-2/neu represses the metastasis suppressor RECK via ERK and Sp transcription factors to promote cell invasion. J. Biol. Chem. 281, 4718–4725 (2006)
Hu, J.: Estimating equation-based causality analysis with application to microarray time series data. Biostatistics 10, 468–480 (2009)
Hughes, M.E., DiTacchio, L., Hayes, K.R., Vollmers, C., Pulivarthy, S., Baggs, J.E., Panda, S., Hogenesch, J.B.: Harmonics of circadian gene transcription in mammals. PLoS Genetics 5, e1000442 (2009)
Jeong, H., Tombor, B., Albert, R., Oltvai, Z.N., Barabási, A.L.: The large-scale organization of metabolic networks. Nature 407, 651–654 (2000)
Johansson, N., Ahonen, M., Kähäri, V.-M.: Matrix metalloproteinases in tumor invasion. Cell. Mol. Life Sci. 57, 5–15 (2000)
Kojima, K., Fujita, A., Shimamura, T., Imoto, S., Miyano, S.: Estimation of nonlinear gene regulatory networks via L1 regularized NVAR from time series gene expression data. Genome Informatics 21, 37–51 (2008)
Krishna, R., Li, C.-T., Buchanan-Wollaston, V.: Interaction based functional clustering of genomic data. In: Ninth IEEE International Conference on Bioinformatics and Bioengineering, Washington, pp. 130–137 (2009)
Marinazzo, D., Pellicoro, M., Stramaglia, S.: Kernel-Granger causality and the analysis of dynamical networks. Physical Review E 7, 056215 (2008)
McCracken, M.W.: Asymptotics for out of sample tests of Granger causality. Journal of Econometrics 140, 719–752 (2007)
Mukhopadhyay, N.D., Chatterjee, S.: Causality and pathway search in microarray time series experiment. Bioinformatics 23, 442–449 (2007)
Nagarajan, R., Upreti, M.: Comment on causality and pathway search in microarray time series experiment. Bioinformatics 24, 1029–1032 (2008)
Nagarajan, R.: A note on inferring acyclic network structures using Granger causality tests. The International Journal of Biostatistics 5, article 10 (2009)
Oh, J., Diaz, T., Wei, B., Chang, H., Noda, M., Stetler-Stevenson, W.G.: TIMP-2 upregulates RECK expression via dephosphorylation of paxillin tyrosine residues 31 and 118. Oncogene 25, 4230–4234 (2006)
Opgen-Rhein, R., Strimmer, K.: Learning causal networks from systems biology time course data: effective model selection procedure for the vector autoregressive process. BMC Bioinformatics 8, S3 (2007)
Sato, J.R., Amaro Jr., E., Takahashi, D.Y., de Maria Felix, M., Brammer, M.J., Morettin, P.A.: A method to produce evolving functional connectivity maps during the course of an fMRI experiment using wavelet-based time-varying Granger causality. Neuroimage 31, 187–196 (2006)
Sato, J.R., Morettin, P.A., Arantes, P.R., Amaro Jr., E.: Wavelet based time-varying vector autoregressive modeling. Computational Statistics & Data Analysis 51, 5847–5866 (2007)
Shimamura, T., Imoto, S., Yamaguchi, R., Fujita, A., Nagasaki, M., Miyano, S.: Recursive regularization for inferring gene networks from time-course gene expression profiles. BMC Systems Biology 3, 41 (2009)
Zou, C., Feng, J.: Granger causality vs. dynamic Bayesian network inference: a comparative study. BMC Bioinformatics 10, 122 (2009)
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Fujita, A., Severino, P., Sato, J.R., Miyano, S. (2010). Granger Causality in Systems Biology: Modeling Gene Networks in Time Series Microarray Data Using Vector Autoregressive Models. In: Ferreira, C.E., Miyano, S., Stadler, P.F. (eds) Advances in Bioinformatics and Computational Biology. BSB 2010. Lecture Notes in Computer Science(), vol 6268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15060-9_2
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DOI: https://doi.org/10.1007/978-3-642-15060-9_2
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