Abstract
Identifying gene regulatory networks from high-throughput gene expression data is one of the most important goals of bioinformatics, but it remains difficult to define what makes a ‘good’ network. Here we introduce Expression Modeling Networks (EMN), in which we propose that a ‘good’ regulatory network must be a functioning tool that predicts biological behavior. Interaction strengths between a regulator and target gene are calculated by fitting observed expression data to the EMN. ‘Better’ EMNs should have superior ability to model previously observed expression data. In this study, we generate regulatory networks by three methods using Bayesian network approach from an oxidative stress gene expression time course experiments. We show that better networks, identified by percentage of interactions between genes sharing at least one GO-Slim Biological Process terms, do indeed generate more predictive EMN’s.
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References
Weaver, D., Workman, C., Stormo, G.: Modeling regulatory networks with weight matrices. In: Pacific Symp. Biocomp., vol. 99(4), pp. 112–123 (1999)
Butte, A.J., Tamayo, P., Slonim, D., Golub, T.R., Kohane, I.S.: Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proceedings of the National Academy of Sciences of the United States of America 97(22), 12182–12186 (2000)
Elo, L.L., Jarvenpaa, H., Oresic, M., Lahesmaa, R., Aittokallio, T.: Systematic construction of gene coexpression networks with applications to human T helper cell differentiation process. Bioinformatics 23(16), 2096–2103 (2007)
Huttenhower, C., Flamholz, A., Landis, J., Sahi, S., Myers, C., Olszewski, K., Hibbs, M., Siemers, N., Troyanskaya, O., Coller, H.: Nearest Neighbor Networks: clustering expression data based on gene neighborhoods. BMC Bioinformatics 8(1), 250 (2007)
Margolin, A., Nemenman, I., Basso, K., Wiggins, C., Stolovitzky, G., Favera, R., Califano, A.: ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context. BMC Bioinformatics 7(suppl. 1), S7 (2006)
Basso, K., Margolin, A.A., Stolovitzky, G., Klein, U., Dalla-Favera, R., Califano, A.: Reverse engineering of regulatory networks in human B cells. Nat. Genet. 37(4), 382 (2005)
Chen, G., Larsen, P., Almasri, E., Dai, Y.: Rank-based edge reconstruction for scale-free genetic regulatory networks. BMC Bioinformatics 9(1), 75 (2008)
Friedman, N., Linial, M., Nachman, I., Pe’er, D.: Using Bayesian networks to analyze expression data. J. Comput. Biol. 7, 601 (2000)
Almasri, E., Larsen, P., Chen, G., Dai, Y.: Incorporating literature knowledge in Bayesian network for inferring gene networks with gene expression data. In: Măndoiu, I., Sunderraman, R., Zelikovsky, A. (eds.) ISBRA 2008. LNCS (LNBI), vol. 4983, pp. 184–195. Springer, Heidelberg (2008)
Hartemink, A.J., Gifford, D.K., Jaakkola, T.S., Young, R.A.: Combining location and expression data for principled discovery of genetic regulatory network models. In: Pac. Symp. Biocomput., pp. 437–449 (2002)
Imoto, S., Higuchi, T., Goto, T., Tashiro, K., Kuhara, S., Miyano, S.: Combining Microarrays and Biological Knowledge for Estimating Gene Networks via Bayesian Networks. In: Proceedings of the IEEE Computer Society Conference on Bioinformatics. IEEE Computer Society, Los Alamitos (2003)
Le Phillip, P., Bahl, A., Unga, L.H.: Using prior knowledge to improve genetic network reconstruction from microarray data. Silico Biology 4, 335–353 (2004)
Kulkarnia, K., Larsen, P., Linninger, A.A.: Assessing chronic liver toxicity based on relative gene expression data. Journal of Theoretical Biology 254(2), 308–318 (2008)
R Development Core Team: R: A Language and Environment for Statistical Computing, http://www.R-project.org
Gasch, A.P., Spellman, P.T., Kao, C.M., Carmel-Harel, O., Eisen, M.B., Storz, G., Botstein, D., Brown, P.O.: Genomic expression programs in the response of yeast cells to environmental changes. Mol. Biol. Cell. 11, 4241–4257 (2000)
Larsen, P., Almasri, E., Chen, G., Dai, Y.: A statistical method to incorporate biological knowledge for generating testable novel gene regulatory interactions from microarray experiments. BMC Bioinformatics 8, 317 (2007)
GO Slim Mapper, http://db.yeastgenome.org/cgi-in/GO/goTermMapper
Herskovits, E., Cooper, G.: Algorithms for Bayesian belief-network precomputation. Methods Inf. Med. 30(2), 81–89 (1991)
Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., et al.: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000)
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Larsen, P., Dai, Y. (2009). Using Gene Expression Modeling to Determine Biological Relevance of Putative Regulatory Networks. In: Măndoiu, I., Narasimhan, G., Zhang, Y. (eds) Bioinformatics Research and Applications. ISBRA 2009. Lecture Notes in Computer Science(), vol 5542. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01551-9_5
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DOI: https://doi.org/10.1007/978-3-642-01551-9_5
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