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Inferring Transcriptional Interactions and Regulator Activities from Experimental Data  

Wang, Rui-Sheng (Department of Electronics, Information and Communication Engineering, Osaka Sangyo University)
Zhang, Xiang-Sun (Academy of Mathematics and Systems Science, CAS)
Chen, Luonan (Department of Electronics, Information and Communication Engineering, Osaka Sangyo University)
Abstract
Gene regulation is a fundamental process in biological systems, where transcription factors (TFs) play crucial roles. Inferring transcriptional interactions between TFs and their target genes has utmost importance for understanding the complex regulatory mechanisms in cellular systems. On one hand, with the rapid progress of various high-throughput experiment techniques, more and more biological data become available, which makes it possible to quantitatively study gene regulation in a systematic manner. On the other hand, transcription regulation is a complex biological process mediated by many events such as post-translational modifications, degradation, and competitive binding of multiple TFs. In this review, with a particular emphasis on computational methods, we report the recent advances of the research topics related to transcriptional regulatory networks, including how to infer transcriptional interactions, reveal combinatorial regulation mechanisms, and reconstruct TF activity profiles.
Keywords
Combinatorial Regulation; Regulator Activity; Transcription Factor; Transcriptional Interactions;
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1 Bolouri, H. and Davidson, E. H. (2002) Modeling transcriptional regulatory networks. BioEssays 24, 1118−1129
2 Climescu-Haulica, A. and Quirk, M. D. (2007) A stochastic differential equation model for transcriptional regulatory networks. BMC Bioinformatics 8, S4
3 Elati, M., Neuvial, P., Bolotin-Fukuhara, M., Barillot, E., Radvanyi, F., et al. (2007) LICORN: learning cooperative regulation networks from gene expression data. Bioinformatics 23, 2407−2414
4 Hannenhalli, S. and Levy, S. (2002) Predicting transcription factor synergism. Nucleic Acids Res. 30, 4278−4284
5 Haverty, P. M., Hansen, U., and Weng, Z. (2004) Computational inference of transcriptional regulatory networks from expression profiling and transcription factor binding site identification. Nucleic Acids Res. 32, 179−188   DOI
6 He, F., Buer, J., Zeng, A. P., and Balling, R. (2007) Dynamic cumulative activity of transcription factors as a mechanism of quantitative gene regulation. Genome Biol. 8, R181   DOI
7 Heron, E. A., Finkenstädt, B., and Rand, D. A. (2007) Bayesian inference for dynamic transcriptional regulation: the Hes1 system as a case study. Bioinformatics 23, 2596−2603
8 Lawrence, N., Sanguinetti, G., and Rattray, M. (2006) Modelling transcriptional regulation using Gaussian processes. Neural Inf. Process. Sys. Conf., 2006
9 Meyer, P. E., Kontos, K., Lafitte, F., and Bontempi, G. (2007) Information-theoretic inference of large transcriptional regulatory networks. EURASIP J. Bioinform. Syst. Biol. 2007, 79879
10 Newman, J. R. S., Ghaemmaghami, S., Ihmels, J., Breslow, D. K., Noble, M., et al. (2006) Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature 441, 840−846
11 Ryu, T., Kim, Y., Kim, D. W., and Lee, D. (2007) Computational identification of combinatorial regulation and transcription factor binding sites. Biotechnol. Bioeng. 97, 1594− 1602
12 Salgado, H., Gama-Castro, S., Peralta-Gil, M., and Diaz-Peredo, E. (2006) RegulonDB (version 5.0): Escherichia coli K-12 transcriptional regulatory network, operon organization, and growth conditions. Nucleic Acids Res. 34, D394−D397
13 Spellman, P. T., Sherlock, G., Zhang, M. Q., Iyer, V. R., Anders, K., et al. (1998) Comprehensive identification of cell cycleregulated genes of the yeast Saccharomyces cererisiae by microarray hybridization. Mol. Biol. Cell 9, 3273−3297
14 Wang, W., Cherry, J. M., Nochomovitz, Y., Jolly, E., Botstein, D., et al. (2005) Inference of combinatorial regulation in yeast transcriptional networks: A case study of sporulation. Proc. Natl. Acad. Sci. USA 102, 1998-2003
15 Yeung, M. K. S., Tegner, J., and Collins, J. (2002) Reverse engineering gene networks using singular value decomposition and robust regression. Proc. Natl. Acad. Sci. USA 99, 6163− 6168
16 Chen, K. C., Wang, T. Y., Tseng, H. H., Huang, C. Y., and Kao, C. Y. (2005) A stochastic differential equation model for quantifying transcriptional regulatory network in Saccharomyces cerevisiae. Bioinformatics 21, 2883−2890
17 Hackney, J. A., Ehrenkaufer, G. M., and Singh, U. (2007) Identification of putative transcriptional regulatory networks in Entamoeba histolytica using Bayesian inference. Nucleic Acids Res. 35, 2141−2152
18 Li, Z., Shaw, S. M., Yedwabnick, M. J., and Chan, C. (2006b) Using a state-space model with hidden variables to infer transcription factor activities. Bioinformatics 22, 747−754
19 Tsai, H. K., Lu, H. H. S., and Li, W. H. (2005) Statistical methods for identifying yeast cell cycle transcription factors. Proc. Natl. Acad. Sci. USA 102, 13532−13537
20 Boulesteix, A. L. and Strimmer, K. (2005) Predicting transcription factor activities from combined analysis of microarray and ChIP data: a partial least squares approach. Theor. Biol. Med. Model. 2, 23   DOI   ScienceOn
21 Kato, M., Hata, N., Banerjee, N., Futcher, B., and Zhang, M.Q. (2004) Identifying combinatorial regulation of transcription factors and binding motifs. Genome Biol. 5, R56   DOI
22 Lawley, D. N. and Maxwell, A. E. (1971) Factor Analysis as a Statistical Method, 2nd ed., Butterworths
23 Gao, F., Foat, B. C., and Bussemaker, H. J. (2004) Defining transcriptional networks through integrative modeling of mRNA expression and transcription factor binding data. BMC Bioinformatics 5, 31   DOI
24 Utsugi, A. and Kumagai, T. (2001) Bayesian analysis of mixtures of factor analyzers. Neural Comput. 13, 993−1002
25 Gibson, M. and Mjolsness, E. (2004) Modeling the activity of single genes, in Computational Modeling of Genetic and Biochemical Networks, J. M. Bower and H. Bolouri (eds.), pp.1- 48, MIT Press, London
26 Nguyen, D. H. and D'haeseleer, P. (2006) Deciphering principles of transcription regulation in eukaryotic genomes. Mol. Syst. Bio., msb4100054
27 Wang, Y., Joshi, T., Zhang, X. S., Xu, D., and Chen, L. (2006a) Inferring gene regulatory networks from multiple microarray datasets. Bioinformatics 22, 2413−2420
28 Remenyi, A., Scholer, H. R., and Wilmanns, M. (2004) Combinatorial control of gene expression. Nat. Struc. Mol. Biol. 11, 812−815
29 Sinha, S. and Tompa, M. (2002) Discovery of novel transcription factor binding sites by statistical overrepresentation. Nucleic Acids Res. 24, 5549−5560
30 Barrett, C. L. and Palsson, B. O. (2006) Iterative reconstruction of transcriptional regulatory networks: an algorithmic approach. PLoS Comput. Biol. 2, e52   DOI
31 Harbison, C. T., Gordon, D. B., Lee, T. I., Rinaldi, N. J., Macisaac, K. D. et al. (2004) Transcriptional regulatory code of a eukaryotic genome. Nature 431, 99−104   DOI   ScienceOn
32 Nachman, I., Regev, A., and Friedman, N. (2004) Inferring quantitative models of regulatory networks from expression data. Bioinformatics 20, i248−i256
33 Sabatti, C. and James, G. M. (2006) Bayesian sparse hidden components analysis for transcription regulation networks. Bioinformatics 15, 739−746
34 Shimoni, Y., Friedlander, G., Hetzroni, G., Niv, G., Altuvia, S., et al. (2007) Regulation of gene expression by small noncoding RNAs: a quantitative view. Mol. Syst. Biol. 3, 138
35 Wu, W. S., Li, W. H., and Chen, B. S. (2007) Identifying regulatory targets of cell cycle transcription factors using gene expression and ChIP-chip data. BMC Bioinformatics 8, 188
36 Chen, L., Wang, R., Kobayashi, T., and Aihara, K. (2004b) Dynamics of gene regulatory networks with cell division cycle. Phys. Rev. E. Stat. Nonlin, Soft Matter Phys. 70, 011909
37 Smith, A. D., Sumazin, P., Das, D., and Zhang, M. Q. (2005) Mining ChIP-chip data for transcription factor and cofactor binding sites. Bioinformatics 21, i403−i412
38 Wang, R. S., Wang, Y., Zhang, X. S., and Chen, L. (2007) Inferring transcriptional regulatory networks from high-throughtput data. Bioinformatics, doi:10.1093/bioinformatics/btm465
39 Yu, X., Lin, J., Zack, D. J., and Qian, J. (2006) Computational analysis of tissue-specific combinatorial gene regulation: predicting interaction between transcription factors in human tissues. Nucleic Acids Res. 34, 4925−4936
40 Wang, Y., Joshi, T., Xu, D., Zhang, X. S., and Chen, L. (2006b) Supervised inference of gene regulatory networks by linear programming. Lecture Notes in Bioinformatics 4115, 551-561
41 Wu, W. S., Li, W. H., and Chen, B. S. (2006) Computational reconstruction of transcriptional regulatory modules of the yeast cell cycle. BMC Bioinformatics 7, 421   DOI
42 Sun, N., Carroll, R. J., and Zhao, H. (2006) Bayesian error analysis model for reconstructing transcriptional regulatory networks, Proc. Natl. Acad. Sci. USA 103, 7988−7993
43 Liao, J. C., Boscolo, R., Yang, Y. L., Tran, L. M., Sabatti, C., et al. (2003) Network component analysis: reconstruction of regulatory signals in biological systems. Proc. Natl. Acad. Sci. USA 100, 15522−15527
44 Sharma, M., George, A. A., Singh, B. N., Sahoo, N. C., and Rao, K. V. S. (2007) Regulation of transcript elongation through cooperative and ordered recruitment of cofactors. J. Biol. Chem. 282, 20887−20896
45 Chen, L., Wang, R. S., and Zhang, X. S. (2008) Biomolecular Networks: Computational Methods and Appliations in Bioinformatics and Systems Biology. Wiley Interscience, NJ, in press
46 Kao, K., Yang, Y., Boscolo, R., Sabatti, C., Roychowdhury, V., et al. (2004). Transcriptome-based determination of multiple transcription regulator activities in Escherichia coli by using network component analysis. Proc. Natl. Acad. Sci. USA 101, 641−646
47 Chen, H. C., Lee, H. C., Lin, T. Y., Li, W. H., and Chen, B. S. (2004a) Quantitative characterization of the transcriptional regulatory network in the yeast cell cycle. Bioinformatics 20, 1914−1927
48 Hart, C. E., Mjolsness, E., and Wold, B. J. (2006) Connectivity in the yeast cell cycle transcription network: inferences from neural networks. PLoS Comput. Biol. 2, e169   DOI
49 Nagamine, N., Kawada, Y., and Sakakibara, Y. (2005) Identifying cooperative transcriptional regulations using proteinprotein interactions. Nucleic Acids Res. 33, 4828−4837
50 Pilpel, Y., Sudarsanam, P., and Church, G. (2001) Identifying regulatory networks by combinatorial analysis of promoter elements. Nature Genet. 29, 153−159
51 Tootle, T. L. and Rebay, I. (2005) Post-translational modifications influence transcription factor activity: a view from the ETS superfamily. BioEssays 27, 285−298
52 Banerjee, N. and Zhang, M. Q. (2003) Identifying cooperativity among transcription factors controlling the cell cycle in yeast. Nucleic Acids Res. 31, 7024−7031
53 Barenco, M., Tomescu, D., Brewer, D., Callard, R., Stark, J., et al. (2006) Ranked prediction of p53 targets using hidden variable dynamic modeling. Genome Biol. 7, R25   DOI
54 Beal, M. J., Falciani, F., Ghahramani, Z., Rangel, C., and Wild, D. L. (2005) A Bayesian approach to reconstructing genetic regulatory networks with hidden factors. Bioinformatics 21, 349−356
55 Balaji, S., Babu, M. M., Iyer, L. M., Luscombe, N. M., and Aravind, L. (2003) Comprehensive analysis of combinatorial regulation using the transcriptional regulatory network of yeast. J. Mol. Biol. 360, 213−227
56 Khanin, R., Vinciotti,V., Mersinias, V., Smith, P., and Wit, E. (2007) Statistical reconstruction of transcription factor activity using Michaelis-Menten kinetics. Biometrics 63, 816−823
57 Pournara, I. and Wernisch, L. (2007) Factor analysis for gene regulatory networks and transcription factor activity profiles. BMC Bioinformatics 8, 61   DOI
58 Rogers, S., Khanin, R., and Girolami, M. (2007) Bayesian modelbased inference of transcription factor activity. BMC Bioinformatics 8, S2   DOI
59 Wang, J. (2007) A new framework for identifying combinatorial regulation of transcription factors: A case study of the yeast cell cycle. J. Biomed. Inform., in press
60 Ellrott, K., Yang, C., Sladek, F. M., and Jiang, T. (2002) Identifying transcription factor binding sites through Markov chain optimization. Bioinformatics 18, S100−S109   DOI
61 Chen, C. C., Zhu, X., and Zhong, S. (2007) Selection of thermodynamic models for combinatorial control of multiple transcription factors in early differentiation of embryonic stem cells. BMC Genomics (in press)
62 Teixeira, M. C., Monteiro, P., and Jain, P. (2006) The YEASTRACT database: a tool for the analysis of transcription regulatory associations in S. cerevisiae. Nucleic Acids Res. 34, D446−D451   DOI   ScienceOn
63 Bluthgen, N., Kielbasa, S. M., and Herzel, H. (2005) Inferring combinatorial regulation of transcription in silico. Nucleic Acids Res. 33, 272−279   DOI
64 Levine, E., Zhang, Z., Kuhlman, T., and Hwa, T. (2007) Quantitative characteristics of gene regulation by small RNA. PLoS Biol. 5, e229   DOI
65 Sanguinetti, G., Lawrence, N. D., and Rattray, M. (2006b) Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities. Bioinformatics 22, 2775−2781
66 Chen, L. and Aihara, K. (2002) Stability of genetic regulatory networks with time delay. IEEE Trans. Circuits Sys. 49, 602− 608
67 Das, D., Banerjee, N., and Zhang, M. Q. (2004) Interacting models of cooperative gene regulation. Proc. Natl. Acad. Sci. USA 101, 16234-16239
68 Sanguinetti, G., Lawrence, N. D., and Rattray, M. (2006a) A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcription. Bioinformatics 22, 1753−1759
69 Chang, Y. H., Wang, Y. C., and Chen, B. S. (2006) Identification of transcription factor cooperativity via stochastic system model. Bioinformatics 22, 2276−2282
70 Wagner, A. (1999) Genes regulated cooperatively by one or more transcription factors and their identification in whole eukaryotic genomes. Bioinformatics 15, 776−784
71 de Jong, H. (2002) Modeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol. 9, 67−103
72 Vu, T. T. and Vohradsky, J. (2007) Nonlinear differential equation model for quantification of transcriptional regulation applied to microarray data of Saccharomyces cerevisiae. Nucleic Acids Res. 35, 279−287   DOI   ScienceOn
73 Yu, T. and Li, K. C. (2005) Inference of transcriptional regulatory network by two-stage constrained space factor analysis. Bioinformatics 21, 4033−4038
74 Husmeier, D. (2003) Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics 19, 2271− 2282
75 Lee, T. I., Rinaldi, N. J., Robert, F., Odom, D. T., Bar-Joseph, Z., et al. (2002) Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298, 799−804
76 Khanin, R., Vinciotti,V., and Wit, E. (2006) Reconstructing repressor protein levels from expression of gene targets in E. coli. Proc. Natl. Acad. Sci. USA 103, 18592−18596
77 Qian, J., Lin, J., Luscombe, N. M., Yu, H., and Gerstein, M. (2003) Prediction of regulatory networks: genome-wide identification of transcription factor targets from gene expression data. Bioinformatics 19, 1917−1926
78 Veitia, R. A. (2003) A sigmoidal transcriptional response: cooperativity, synergy and dosage effects. Biol. Rev. 78, 149−170
79 Li, H., Sun, Y., and Zhan, M. (2006a) The discovery of transcriptional modules by a two-stage matrix decomposition approach. Bioinformatics 23, 473−479
80 Hermsen, R., Tans, S., and Wolde, P.R. (2006) Transcriptional regulation by competing transcription factor modules. PLoS Comput. Biol. 2, e164   DOI