Statistical inference of the time-varying structure of gene-regulation networks

BMC Systems Biology

Volumn 4, Issue , 2010, Pages

  Lèbre, Sophie ^a,b   Becq, Jennifer ^c,d,e   Devaux, Frédéric ^f   Stumpf, Michael P H ^a   Lelandais, Gaëlle ^c,d,e  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b UNIVERSITÉ DE STRASBOURG   (France)

^c hôpital Robert Debré et Institut national de la transfusion sanguine   (France)

^d UNIVERSITÉ PARIS DESCARTES   (France)

^e INSTITUT NATIONAL DE LA TRANSFUSION SANGUINE   (France)

^f CNRS   (France)

Author keywords

[No Author keywords available]

Indexed keywords

DROSOPHILA MELANOGASTER; SACCHAROMYCES CEREVISIAE;

BENOMYL; TRANSCRIPTION FACTOR;

ALGORITHM; ANIMAL; ARTICLE; BIOLOGICAL MODEL; DROSOPHILA MELANOGASTER; DRUG EFFECT; GENE REGULATORY NETWORK; GENETICS; GROWTH, DEVELOPMENT AND AGING; METABOLISM; MONTE CARLO METHOD; PROBABILITY; REGRESSION ANALYSIS; REPRODUCIBILITY; SACCHAROMYCES CEREVISIAE;

ALGORITHMS; ANIMALS; BENOMYL; DROSOPHILA MELANOGASTER; GENE REGULATORY NETWORKS; MARKOV CHAINS; MODELS, GENETIC; MONTE CARLO METHOD; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; SACCHAROMYCES CEREVISIAE; TRANSCRIPTION FACTORS;

EID: 77957930628     PISSN: None     EISSN: 17520509     Source Type: Journal    
DOI: 10.1186/1752-0509-4-130     Document Type: Article

References (42)

1. Luscombe N, Babu M, Yu H, Snyder M, Teichmann S, Gerstein M. Genomic analysis of regulatory network dynamics reveals large topological change. Nature 2004, 431:308-312. 10.1038/nature02782, 15372033.
2. Seshasayee A, Bertone P, Fraser G, Luscombe N. Transcriptional regulatory networks in bacteria: from input signals to output responses. Curr Opin Microbiol 2006, 9(5):511-519. 10.1016/j.mib.2006.08.007, 16942903.
3. Zhu J, B Z, Smith E, Drees B, Brem R, Kruglyak L, Bumgarner R, Schadt E. Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nature Genetics 2008, 40(7):854-861. 10.1038/ng.167, 2573859, 18552845.
4. Barenco M, Tomescu D, Brewer D, Callard R, Stark J, Hubank M. Ranked prediction of p53 targets using hidden variable dynamic modeling. Genome Biology 2006, 7:R27. 10.1186/gb-2006-7-3-r25, 1557993, 16594986.
5. Bonneau R, Facciotti M, Reiss D, Schmid A, Pan M, Kaur A, Thorsson V, Shannon P, Johnson M, Bare J, Longabaugh W, Vuthoori M, Whitehead K, Madar A, Suzuki L, Mori T, Chang D, Diruggiero J, Johnson C, Hood L, Baliga N. A predictive model for transcriptional control of physiology in a free living cell. Cell 2007, 131(7):1354-1365. 10.1016/j.cell.2007.10.053, 18160043.
6. Friedman N. Inferring Cellular Networks Using Probabilistic Graphical Models. Science 2004, 303:799-805. 10.1126/science.1094068, 14764868.
7. Hartemink A. Reverse engineering gene regulatory networks. Nature Biotechnology 2005, 23(5):554-555. 10.1038/nbt0505-554, 15877071.
8. Werhli A, Grzegorczyk M, Husmeier D. Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical Gaussian models and Bayesian networks. Bioinformatics 2006, 22(20):2523-2531. 10.1093/bioinformatics/btl391, 16844710.
9. Yoshida R, Imoto S, Higuchi T. Estimating Time-Dependent Gene Networks from Time Series Microarray Data by Dynamic Linear Models with Markov Switching. CSB '05: Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference 2005, 289-298. Washington, DC, USA: IEEE Computer Society.
10. Talih M, Hengartner N. Structural learning with time-varying components: tracking the cross-section of financial time series. Journal of the Royal Statistical Society Series B 2005, 67(3).
11. Fujita A, Sato J, Garay-Malpartida H, Morettin P, Sogayar M, Ferreira C. Time-varying modeling of gene expression regulatory networks using the wavelet dynamic vector autoregressive method. Bioinformatics 2007, 23(13):1623-1630. 10.1093/bioinformatics/btm151, 17463021.
12. Xuan X, Murphy KP. Modeling changing dependency structure in multivariate time series. ICML 2007, 227:1055-1062. ACM International Conference Proceeding Series.
13. Robinson J, Hartemink A. Non-Stationary Dynamic Bayesian Networks. NIPS '08: Neural Information Processing Systems 2008 2008, 1369-1376.
14. Rao A, Hero AO, States DJ, Engel JD. Inferring Time-Varying Network Topologies from Gene Expression Data. EURASIP Journal on Bioinformatics and Systems Biology 2007,
15. Ahmed A, Xing EP. Recovering time-varying networks of dependencies in social and biological studies. Proceedings of the National Academy of Sciences 2009, 106(29):11878-11883.
16. Needham C, Bradford J, Bulpitt A, Westhead D. Inference in Bayesian networks. Nature Biotechnology 2006, 24:51-53. 10.1038/nbt0106-51, 16404397.
17. Alon U. Network motifs: theory and experimental approaches. Nature Reviews Genetics 2007, 8(6):450-461. 10.1038/nrg2102, 17510665.
18. Sachs K, Perez O, Pe'er D, Lauffenburger D, Nolan G. Causal protein-signaling networks derived from multiparameter single-cell data. Science 2005, 308(5721):523-529. 10.1126/science.1105809, 15845847.
19. Lebre S. Inferring dynamic genetic networks with low order independencies. Statistical Applications in Genetics and Molecular Biology 2009, 8. 10.2202/1544-6115.1294, 19222392.
20. de Silva E, Stumpf M. Complex networks and simple models in biology. J Roy Soc Interface 2005, 2:419-340.
21. Green P. Reversible jump Markoc chain Monte Carlo computation and Bayesian model determination. Biometrika 1995, 82:711-732.
22. Robert C, Ryden T, Titterington D. Bayesian inference in hidden Markov models through reversible jump Markov chain Monte Carlo. Journal of the Royal Statistical Society B 2000, 62:57-75.
23. Green PJ. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 1995, 82:711-732.
24. Suchard M, Weiss R, Dorman K, Sinsheimer J. Inferring spatial phylogenetic variation along nucleotide sequences: a multiple change-point model. Journal of the American Statistical Assocation 2003, 98:427-437.
25. Andrieu C, Doucet A. Joint Bayesian model selection and estimation of noisy sinusoids via reversible jump MCMC. IEEE Trans. on Signal Processing 1999, 47(10):2667-2676.
26. Zellner A. On assessing prior distributions and Bayesian regression analysis with g-prior distribution. Bayesian Inference and Decision Techniques 1986, 233-243. New York: Elsevier, Goel PK, Zellner A.
27. Kass RE, Raftery AE. Bayes Factors. Journal of the American Statistical Association 1995, 90:773-795.
28. Arbeitman MN, Furlong F, Imam EE, Johnson E, Null BH, Baker BS, Krasnow MA, Scott MP, Davis RW, P WK. Gene expression during the life cycle of drosophila melanogaster. Science 2002, 297(5590):2270-2275. 10.1126/science.1072152, 12351791.
29. The Gene Ontology Consortium Gene ontology: tool for the unification of biology. Nature Genetics 2000, 25:25-29. 10.1038/75556, 10802651, The Gene Ontology Consortium., http://www.geneontology.org
30. Lucau-Danila A, Lelandais G, Kozovska Z, Tanty V, Delaveau T, Devaux F, Jacq C. Early Expression of Yeast Genes Affected by Chemical Stress. Mol Cell Biol 2005, 25(5):1860-1868. 10.1128/MCB.25.5.1860-1868.2005, 549374, 15713640.
31. Monteiro PT, Mendes ND, Teixeira MC, d'Orey S, Tenreiro S, Mira NP, Pais H, Francisco AP, Carvalho AM, Lourenco AB, Sa-Correia I, Oliveira AL, Freitas AT. YEASTRACT-DISCOVERER: new tools to improve the analysis of transcriptional regulatory associations in Saccharomyces cerevisiae. Nucl Acids Res 2008, 36(suppl_1):D132-136. 2238916, 18032429., http://nar.oxfordjournals.org/cgi/content/abstract/36/suppl_1/D132
32. Salin H, Fardeau V, Piccini E, Lelandais G, Tanty V, Lemoine S, Jacq C, Devaux F. Structure and properties of transcriptional networks driving selenite stress response in yeasts. BMC Genomics 2008, 9:333. 10.1186/1471-2164-9-333, 2515152, 18627600., http://www.biomedcentral.com/1471-2164/9/333
33. Ernst J, Nau GJ, Bar-Joseph Z. Clustering short time series gene expression data. Bioinformatics (Oxford, England) 2005, 21(Suppl 1):i159-168. [PMID: 15961453], 10.1093/bioinformatics/bti1022, 15961453.
34. Banerjee D, Lelandais G, Shukla S, Mukhopadhyay G, Jacq C, Devaux F, Prasad R. Responses of Pathogenic and Nonpathogenic Yeast Species to Steroids Reveal the Functioning and Evolution of Multidrug Resistance Transcriptional Networks. Eukaryotic Cell 2008, 7:68-77. 10.1128/EC.00256-07, 2224153, 17993571., http://ec.asm.org/cgi/content/abstract/7/1/68
35. Fardeau V, Lelandais G, Oldfield A, Salin H, Lemoine S, Garcia M, Tanty V, Crom SL, Jacq C, Devaux F. The Central Role of PDR1 in the Foundation of Yeast Drug Resistance. J Biol Chem 2007, 282(7):5063-5074. 10.1074/jbc.M610197200, 17158869., http://www.jbc.org/cgi/content/abstract/282/7/5063
36. Toni T, Welch D, Strelkowa N, Ipsen D, Stumpf M. Approximate Bayesian Computation scheme for parameter inference and model selection in dynamical systems. J Roy Soc Interface 2009, 6:187-202.
37. Gutenkunst RN, Waterfall JJ, Casey FP, Brown KS, Myers CR, Sethna JP. Universally sloppy parameter sensitivities in systems biology models. PLoS Comput Biol 2007, 3:1871-1878. 10.1371/journal.pcbi.0030189, 2000971,2000971, 17922568.
38. Gutenkunst RN, Casey FP, Waterfall JJ, Myers CR, Sethna JP. Extracting falsifiable predictions from sloppy models. Ann N Y Acad Sci 2007, 1115:203-11. 10.1196/annals.1407.003, 17925353.
39. Lauritzen SL. Graphical models 1996, Oxford University Press.
40. Werhli A, Husmeier D. Reconstructing Gene Regulatory Networks with Bayesian Networks by Combining Expression Data with Multiple Sources of Prior Knowledge. Statistical Applications in Genetics and Molecular Biology 2007, 6. 10.2202/1544-6115.1282, 17542777.
41. Mukherjee S, Speed TP. Network inference using informative priors. Proceedings of the National Academy of Sciences (PNAS) 2008, 105(38):14313-14318.
42. Grzegorczyk M, Husmeier D. Non-stationary continuous dynamic Bayesian networks. Advances in Neural Information Processing Systems (NIPS) 2009, 22:682-690. Bengio Y, Schuurmans D, Lafferty J, Williams CKI, Culotta A.