Abstract
A number of methods based on time-dependent state space models have been proposed for inferring time-varying gene regulatory networks. These methods are capable of detecting a relatively small number of topological changes in gene regulatory networks. However, they are insufficient since there is a greater number of changes in the gene regulatory mechanisms; the function of a regulatory protein frequently changes due to post-translational modification, such as protein phosphorylation and ATP-binding. We propose a self-organizing state space approach to inferring consecutive changes in causalities between regulatory proteins from gene expression data. Hidden regulatory proteins are identified using a test-based method from genome-wide protein-DNA binding data. Application of this approach to cell cycle data demonstrated its effectiveness.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Friedman, N., Linial, M., Nachman, I., Pe’er, D.: Using ayesian networks to analyze expression data. J. Comput. Biol. 7, 601–620 (2000)
Friedman, N.: Inferring cellular networks using probabilistic graphical models. Science 303, 799–805 (2004)
Imoto, S., Goto, T., Miyano, S.: Estimation of genetic networks and functional structures between genes by using bayesian networks and nonparametric regression. In: Proceedings of Pacific Symposium of Biocomputing, pp. 175–186 (2002)
Tamada, Y., Imoto, S., Tashiro, K., Kuhara, S., Miyano, S.: Identifying drug active pathways from gene networks estimated by gene expression data. Genome Inform. 16, 182–191 (2005)
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 (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 Syst. Biol. 3, 41 (2009)
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 Inform. 20, 37–51 (2008)
Beal, M.J., Falciani, F., Ghahramani, Z., Rangel, C., Wild, D.L.: A Bayesian approach to reconstructing genetic regulatory networks with hidden factors. Bioinformatics 21, 349–356 (2005)
Rangel, C., Angus, J., Ghahramani, Z., Lioumi, M., Sotheran, E., Gaiba, A., Wild, D.L., Falciani, F.: Modeling T-cell activation using gene expression profiling and state-space models. Bioinformatics 20, 1361–1372 (2004)
Wu, F.X., Zhang, W.J., Kusalik, A.J.: Modeling gene expression from microarray expression data with state-space equations. In: Proceedings of Pacific Symposium on Biocomputing, pp. 581–592 (2004)
Yamaguchi, R., Yoshida, R., Imoto, S., Higuchi, T., Miyano, S.: Finding module-based gene networks with state-space models. IEEE Signal Processing Magazine 24, 37–46 (2007)
Yoshida, R., Imoto, S., Higuchi, T.: Estimating time-dependent gene networks from time series microarray data by dynamic linear models with markov switching. In: Proceedings of Computational Systems Bioinformatics, pp. 289–298 (2005)
Li, Z., Shaw, S.M., Yedwabnick, M.J., Chan, C.: Using a state-space model with hidden variables to infer transcription factor activities. Bioinformatics 22, 747–754 (2006)
Rao, A., Hero, A.O., States, D.J., Engel, J.D.: Using directed information to build biologically relevant influence networks. In: Comput. Syst. Bioinformatics Conf., vol. 6, pp. 145–156 (2007)
Shumway, R.H., Stoffer, D.S.: An approach to time series smoothing and forecasting using the EM algorithm. Journal of Time Series Analysis 3, 253–264 (1982)
Kitagawa, G.: A self-organizing state-space model. Journal of the American Statistical Association 93, 1203–1215 (1998)
Higuchi, T., Kitagawa, G.: Knowledge discovery and self-organizing state space model. IEICE Transactions on Information and Systems E Series D 83, 36–43 (2000)
Yano, K.: A self-organizing state space model and simplex initial distribution search. Computational Statistics 23, 197–216 (2008)
Lee, T.I., Rinaldi, N.J., Robert, F., Odom, D.T., Bar-Joseph, Z., Gerber, G.K., Hannett, N.M., Harbison, C.T., Thompson, C.M., Simon, I., Zeitlinger, J., Jennings, E.G., Murray, H.L., Gordon, D.B., Ren, B., Wyrick, J.J., Tagne, J.B., Volkert, T.L., Fraenkel, E., Gifford, D.K., Young, R.A.: Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298, 799–804 (2002)
Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R., Anders, K., Eisen, M.B., Brown, P.O., Botstein, D., Futcher, B.: Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell. 9, 3273–3297 (1998)
Costanzo, M., Schub, O., Andrews, B.: G1 transcription factors are differentially regulated in Saccharomyces cerevisiae by the Swi6-binding protein Stb1. Mol. Cell. Biol. 23, 5064–5077 (2003)
de Bruin, R.A.M., Kalashnikova, T.I., Wittenberg, C.: Stb1 collaborates with other regulators to modulate the G1-specific transcriptional circuit. Mol. Cell. Biol. 28, 6919–6928 (2008)
Simon, I., Barnett, J., Hannett, N., Harbison, C.T., Rinaldi, N.J., Volkert, T.L., Wyrick, J.J., Zeitlinger, J., Gifford, D.K., Jaakkola, T.S., Young, R.A.: Serial regulation of transcriptional regulators in the yeast cell cycle. Cell 106, 697–708 (2001)
Koranda, M., Schleiffer, A., Endler, L., Ammerer, G.: Forkhead-like transcription factors recruit Ndd1 to the chromatin of G2/M-specific promoters. Nature 406, 94–98 (2000)
Amar, N., Messenguy, F., Bakkoury, M.E., Dubois, E.: ArgRII, a component of the argr-mcm1 complex involved in the control of arginine metabolism in saccharomyces cerevisiae, is the sensor of arginine. Mol. Cell. Biol. 20, 2087–2097 (2000)
Ho, Y., Gruhler, A., Heilbut, A., Bader, G.D., Moore, L., Adams, S.L., Millar, A., Taylor, P., Bennett, K., Boutilier, K., Yang, L., Wolting, C., Donaldson, I., Schandorff, S., Shewnarane, J., Vo, M., Taggart, J., Goudreault, M., Muskat, B., Alfarano, C., Dewar, D., Lin, Z., Michalickova, K., Willems, A.R., Sassi, H., Nielsen, P.A., Rasmussen, K.J., Andersen, J.R., Johansen, L.E., Hansen, L.H., Jespersen, H., Podtelejnikov, A., Nielsen, E., Crawford, J., Poulsen, V., Srensen, B.D., Matthiesen, J., Hendrickson, R.C., Gleeson, F., Pawson, T., Moran, M.F., Durocher, D., Mann, M., Hogue, C.W.V., Figeys, D., Tyers, M.: Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 415, 180–183 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hirose, O., Shimizu, K. (2010). A Self-organizing State Space Approach to Inferring Time-Varying Causalities between Regulatory Proteins. In: Khuri, S., Lhotská, L., Pisanti, N. (eds) Information Technology in Bio- and Medical Informatics, ITBAM 2010. ITBAM 2010. Lecture Notes in Computer Science, vol 6266. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15020-3_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-15020-3_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15019-7
Online ISBN: 978-3-642-15020-3
eBook Packages: Computer ScienceComputer Science (R0)