Abstract
Driven to discover the vast information and comprehend the fundamental mechanism of gene regulations, gene regulatory networks (GRNs) inference from gene expression data has gathered the interests of many researchers which is otherwise unfeasible in the past due to technology constraint. The dynamic Bayesian network (DBN) has been widely used to infer GRNs as it is capable of handling time-series gene expression data and feedback loops. However, the frequently occurred missing values in gene expression data, the incapability to deal with transcriptional time lag, and the excessive computation time triggered by the large search space, are attributed to restraint the effectiveness of DBN in inferring GRNs from gene expression data. This paper proposes a DBN-based model (IST-DBN) with missing values imputation, potential regulators selection, and time lag estimation to address these problems. To assess the performance of IST-DBN, we applied the model on the E. coli SOS response pathway time-series expression data. The experimental results showed IST-DBN has higher accuracy and faster computation time in recognising gene-gene relationships when compared with existing DBN-based model and conventional DBN. We also believe that the ensuing networks from IST-DBN are applicable as a common framework for prospective gene intervention study.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Lee, W.P., Tzou, W.S.: Computational methods for discovering gene networks from expression data. Brief Bioinform. 10(4), 408–423 (2009)
Jornsten, R., Wang, H.Y., Welsh, W.J., Ouyang, M.: DNA microarray data imputation and significance analysis of differential expression. Bioinformatics 21(22), 4155–4161 (2005)
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)
Muro, S., Takemasa, I., Oba, S., Matoba, R., Ueno, N., Maruyama, C., Yamashita, R., Sekimoto, M., Yamamoto, H., Nakamori, S., Monden, M., Ishii, S., Kato, K.: Identification of expressed genes linked to malignancy of human colorectal carcinoma by parametric clustering of quantitative expression data. Genome Biol. (4), 21 (2003)
Salgado, H., Gama-Castro, S., Peralta-Gil, M., Diaz-Peredo, E., Sanchez-Solano, F., Santos-Zavaleta, A., Martinez-Flores, I., Jimenez-Jacinto, V., Bonavides-Martinez, C., Segura-Salazar, J., Martinez-Antonio, A., Collado-Vides, J.: RegulonDB (version 5.0): Escherichia coli K-12 transcriptional regulatory network, operon organization, and growth conditions. Nucleic Acids Res. 34, 394–397 (2005)
Karlebach, G., Shamir, R.: Modelling and analysis of gene regulatory networks. Nat. Rev. Mol. Cell Bio. 9(10), 770–780 (2008)
Friedman, N., Linial, M., Nachman, I., Pe’er, D.: Using Bayesian networks to analyse expression data. J. Comp. Biol. 7, 601–620 (2000)
Murphy, K., Mian, S.: Modelling gene expression data using dynamic Bayesian networks. Technical report, Computer Science Division, University of California, Berkeley (1999)
Ouyang, M., Welsh, W.J., Geogopoulos, P.: Gaussian mixture clustering and imputation of microarray data. Bioinformatics 20(6), 917–923 (2004)
Jia, Y., Huan, J.: Constructing non-stationary dynamic Bayesian networks with a flexible lag choosing mechanism. BMC Bioinformatics (11), 27 (2010)
Chai, L.E., Mohamad, M.S., Deris, S., Chong, C.K., Choon, Y.W., Ibrahim, Z., Omatu, S.: Inferring gene regulatory networks from gene expression data by a dynamic bayesian network-based model. In: Omatu, S., De Paz Santana, J.F., González, S.R., Molina, J.M., Bernardos, A.M., Rodríguez, J.M.C. (eds.) Distributed Computing and Artificial Intelligence. AISC, vol. 151, pp. 379–386. Springer, Heidelberg (2012)
Kim, H., Golub, G., Park, H.: Missing value estimation for DNA microarray gene expression data: local least squares imputation. Bioinformatics 21(2), 187–198 (2005)
Yu, H., Luscombe, N.M., Qian, J., Gerstein, M.: Genomic analysis of gene expression relationships in transcriptional regulatory networks. Trends Genet. 19, 422–427 (2003)
Ronen, M., Rosenberg, R., Shraiman, B.I., Alon, U.: Assigning numbers to the arrows: parameterizing a gene regulation network by using accurate expression kinetics. Proc. Natl. Acad. Sci. 99, 10555–10560 (2002)
Radman, M.: Phenomenology of an inducible mutagenic DNA repair pathway in Escherichia coli. Basic Life Sci. 5A, 255–367 (1975)
Wilczynski, B., Dojer, N.: BNFinder: exact and efficient method for learning Bayesian networks. Bioinformatics 25(2), 286–287 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chai, L.E., Mohamad, M.S., Deris, S., Chong, C.K., Choon, Y.W. (2013). Inferring E. coli SOS Response Pathway from Gene Expression Data Using IST-DBN with Time Lag Estimation. In: Sidhu, A., Dhillon, S. (eds) Advances in Biomedical Infrastructure 2013. Studies in Computational Intelligence, vol 477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37137-0_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-37137-0_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37136-3
Online ISBN: 978-3-642-37137-0
eBook Packages: EngineeringEngineering (R0)