Large-scale learning of combinatorial transcriptional dynamics from gene expression

Bioinformatics

Volumn 27, Issue 9, 2011, Pages 1277-1283

  Asif, H M Shahzad ^a,b   Sanguinetti, Guido ^a  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

^b UNIVERSITY OF ENGINEERING AND TECHNOLOGY   (Pakistan)

Author keywords

[No Author keywords available]

Indexed keywords

TRANSCRIPTION FACTOR;

ARTICLE; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; BIOLOGICAL MODEL; BIOLOGY; COMPUTER PROGRAM; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORK; GENETICS; METABOLISM; METHODOLOGY; PROBABILITY; STATISTICAL MODEL;

ARTIFICIAL INTELLIGENCE; BAYES THEOREM; COMPUTATIONAL BIOLOGY; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORKS; LIKELIHOOD FUNCTIONS; MARKOV CHAINS; MODELS, GENETIC; SOFTWARE; TRANSCRIPTION FACTORS;

EID: 79954522725     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btr113     Document Type: Article

References (29)

1. Barenco,M. et al. (2006) Ranked prediction of p53 targets using hidden variable dynamical modelling. Genome Biol., 7, R25.
2. Beal,M.J. (2003) Variational Algorithms for Approximate Bayesian Inference. UK PhD Thesis, Gatsby Computational Neuroscience Unit University College, London.
3. Bhoite,L.T. et al. (2002) Mutations in the Pho2 (Bas2) transcription factor that differentially affect activation with its partner proteins Bas1, Pho4, and Swi5. J. Biol. Chem., 277, 37612-37618.
4. Bishop,C. (2006) Pattern Recognition and Machine Learning. Springer, New York.
5. Coffman,J. et al. (1996) Gat1p, a GATAfamily protein whose production is sensitive to nitrogen catabolite repression, participates in transcriptional activation of nitrogencatabolic genes in Saccharomyces cerevisiae. Mol. Cell. Biol., 16, 847.
6. Daignan-Fornier,B. and Fink,G. (1992) Coregulation of purine and histidine biosynthesis by the transcriptional activators BAS1 and BAS2. Proc. Natl Acad. Sci. USA, 89, 6746.
7. Davidge,K. et al. (2009) Carbon monoxide-releasing antibacterial molecules target respiration and global transcriptional regulators. J. Biol. Chem., 284, 4516.
8. Ghahramani,Z. and Jordan,M. (1997) Factorial hidden Markov models. Mach. Learn., 29, 245-273.
9. Hahn,S. et al. (1988) The HAP3 regulatory locus of Saccharomyces cerevisiae encodes divergent overlapping transcripts. Mol. Cell. Biol., 8, 655.
10. Harbison,C. et al. (2004) Transcriptional regulatory code of a eukaryotic genome. Nature, 431, 99-104.
11. Jordan,M. et al. (1999) An introduction to variational methods for graphical models. Mach. Learn., 37, 183-233.
12. Lawrence,N.D. et al. (2006) Modelling transcriptional regulation using Gaussian processes. In Advances in Neural Information Processing Systems 19. MIT Press, p. 785.
13. Lee,T.I. et al. (2002) Transcriptional regulatory networks in Saccharomyces cerevisiae. Science, 298, 799-804.
14. Liao,J. et al. (2003) Network component analysis: reconstruction of regulatory signals in biological systems. Proc. Natl Acad. Sci. USA, 100, 15522-15527.
15. Opper,M. and Sanguinetti,G. (2010) Learning combinatorial transcriptional dynamics from gene expression data. Bioinformatics, 26, 1623.
16. Partridge,J. et al. (2007) Transition of Escherichia coli from aerobic to micro-aerobic conditions involves fast and slow reacting regulatory components. J. Biol. Chem., 282, 11230.
17. Ptashne,M. and Gann,A. (2002) Genes & Signals. Cold Spring Harbor, New York.
18. Rogers,S. et al. (2007) Bayesian model-based inference of transcription factor activity. BMC Bioinformatics, 8, S2.
19. Sabatti,C. and James,G.M. (2006) Bayesian sparse hidden components analysis for transcription regulation networks. Bioinformatics, 22, 739-746.
20. Sanguinetti,G. et al. (2006) Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities. Bioinformatics, 22, 2775-2781.
21. Sanguinetti,G. et al. (2009) Switching regulatory models of cellular stress response. Bioinformatics, 25, 1280-1286.
22. Scott,S. et al. (2000) Roles of the Dal82p domains in allophanate/oxalurate-dependent gene expression in Saccharomyces cerevisiae. J. Biol. Chem., 275, 30886.
23. Asif,H.M.S. et al. (2010) TFInfer: a tool for probabilistic inference of transcription factor activities. Bioinformatics, 26, 2635.
24. Shi,Y. et al. (2009)Acombined expression-interaction model for inferring the temporal activity of transcription factors. J. Comput. Biol., 16, 1035-1049.
25. Spellman,P.T. et al. (1998) Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell, 9, 3273-3297.
26. Tu,B. et al. (2005) Logic of the yeast metabolic cycle: temporal compartmentalization of cellular processes. Science, 310, 1152.
27. Tuna,S. and Niranjan,M. (2010) Reducing the algorithmic variability in transcriptomebased inference. Bioinformatics, 26, 1185.
28. Xing,Y. et al. (1993) Mutations in yeast HAP2/HAP3 define a hybrid CCAAT box binding domain. EMBO J., 12, 4647.
29. Xu,Z. and Tsurugi,K. (2007) Role of Gts1p in regulation of energy-metabolism oscillation in continuous cultures of the yeast Saccharomyces cerevisiae. Yeast, 24, 161-170.