Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks

Bioinformatics

Volumn 29, Issue 8, 2013, Pages 1060-1067

  Greenfield, Alex ^a   Hafemeister, Christoph ^b   Bonneau, Richard ^a,b,c  

^a NEW YORK UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b NEW YORK UNIVERSITY   (United States)

^c POLYTECHNIC UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BACTERIA (MICROORGANISMS);

TRANSCRIPTION FACTOR;

ALGORITHM; ARTICLE; BACILLUS SUBTILIS; BAYES THEOREM; BIOLOGICAL MODEL; ESCHERICHIA COLI; GENE EXPRESSION; GENE REGULATORY NETWORK; GENETICS; METABOLISM; METHODOLOGY; REGRESSION ANALYSIS; SYSTEMS BIOLOGY;

ALGORITHMS; BACILLUS SUBTILIS; BAYES THEOREM; ESCHERICHIA COLI; GENE EXPRESSION; GENE REGULATORY NETWORKS; MODELS, GENETIC; REGRESSION ANALYSIS; SYSTEMS BIOLOGY; TRANSCRIPTION FACTORS;

EID: 84876207916     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btt099     Document Type: Article

References (36)

1. Bar-Joseph, Z. et al. (2012) Studying and modelling dynamic biological processes using time-series gene expression data. Nat. Rev. Genet., 13, 552-564.
2. Bonneau, R. et al. (2006) The Inferelator: an algorithm for learning parsimonious regulatory networksfromsystems-biology datasetsdenovo. GenomeBiol., 7, R36.
3. Bonneau, R. et al. (2007) A predictive model for transcriptional control of physiology in a free living cell. Cell, 131, 1354-1365.
4. Carro, M.S. et al. (2010) The transcriptional network for mesenchymal transform- ation of brain tumours. Nature, 463, 318-325.
5. Ciofani, M. et al. (2012) A validated regulatory network for Th17 cell specification. Cell, 151, 289-303.
6. ENCODE Project Consortium. (2012) An integrated encyclopedia of DNA elements in the human genome. Nature, 489, 57-74.
7. Ernst, J. et al. (2008) A semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli. PLoS Comput. Biol., 4, e1000044.
8. Florez, L.A. et al. (2009) A community-curated consensual annotation that is continuously updated: the Bacillus Subtilis centred wiki subtiwiki. Database, 2009, bap012.
9. Fu, Y. et al. (2011) Reconstructing genome-wide regulatory network of E. coli using transcriptome data and predicted transcription factor activities. BMC Bioinformatics, 12, 233.
10. Gallo, S.M. et al. (2010) Redfly v3.0: toward a comprehensive database oftranscrip- tional regulatory elements in Drosophila. Nucleic Acids Res., 39, D118-D123.
11. Gama-Castro, S. et al. (2011) Regulondb version 7.0: transcriptional regulation of Escherichia Coli k-12 integrated within genetic sensory response units (gensor units). Nucleic Acids Res., 39, D98-D105.
12. Greenfield, A. et al. (2010) Dream4: combining genetic and dynamic information to identify biological networks and dynamical models. PLoS One, 5, e13397.
13. Gustafsson, M. and Hornquist, M. (2010) Gene expression prediction by soft integration and the elastic netbest performance of the dream3 gene expression challenge. PLoS One, 5, e9134.
14. Hambraeus, G. et al. (2003) Genome-wide survey of mRNA half-lives in Bacillus Subtilis identifies extremely stable mRNAs. Mol. Genet. Genomics, 269, 706-714.
15. Hecker, M. et al. (2009) Gene regulatory network inference: data integration in dynamic models-a review. Biosystems, 96, 86-103.
16. Husmeier, D. and Werhli, A.V. (2007) Bayesian integration of biological prior knowledge into the reconstruction of gene regulatory networks with Bayesian networks. Comput. Syst. Bioinformatics Conf., 6, 85-95.
17. Imoto, S. et al. (2003) Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks. Proc. IEEE Comput. Soc. Bioinform. Conf., 2, 104-13.
18. Lammers, C.R. et al. (2010) Connecting parts with processes: Subtiwiki and Subtipathways integrate gene and pathway annotation for Bacillus subtilis. Microbiology, 156, 849-859.
19. Li, C. and Li, H. (2008) Network-constrained regularization and variable selection for analysis ofgenomic data. Bioinformatics, 24, 1175-1182.
20. Luscombe, N.M. et al. (2004) Genomic analysis of regulatory network dynamics reveals large topological changes. Nature, 431, 308-312.
21. Madar, A. et al. (2010) DREAM3: network inference using dynamic context likeli- hood ofrelatedness and the inferelator. PloS One, 5, e9803.
22. Marbach, D. et al. (2010) Revealing strengths and weaknesses ofmethods for gene network inference. Proc. Natl Acad. Sci. USA, 107, 6286-6291.
23. Marbach, D. et al. (2012) Wisdom of crowds for robust gene network inference. Nat. Methods, 9, 796-804.
24. Nicolas, P. et al. (2012) Condition-dependent transcriptome reveals high-level regulatory architecture in Bacillus subtilis. Science, 335, 1103-1106.
25. Prill, RJ. et al. (2010) Towards a rigorous assessment ofsystems biology models: the DREAM3 challenges. PLoS ONE, 5, e9202.
26. Schulz, M.H. et al. (2012) DREM 2.0: improved reconstruction of dynamic regulatory networks from time-series expression data. BMCSyst. Biol., 6, 104.
27. Selinger, D. et al. (2003) Global RNA half-life analysis in Escherichia coli reveals positional patterns of transcript degradation. Genome Res., 13, 216-223.
28. Seok, J. et al. (2009) A dynamic network of transcription in LPS-treated human subjects. BMC Syst. Biol., 3, 78.
29. Stolovitzky, G. et al. (2007) Dialogue on reverse-engineering assessment and methods: the DREAM of high-throughput pathway inference. Ann. NY Acad. Sci., 1115, 1-22.
30. Stolovitzky, G. et al. (2009) Lessons from the DREAM2 challenges. Ann. NYAcad. Sci., 1158, 159-195.
31. Tamada, Y. et al. (2003) Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection. Bioinformatics, 19 (Suppl. 2), ii227-ii236.
32. Werhli, A.V. and Husmeier, D. (2007) Reconstructing gene regulatory networks with bayesian networks by combining expression data with multiple sources of prior knowledge. Stat. Appl. Genet. Mol. Biol., 6. Article15.
33. Yong-A-Poi, J. et al. (2008) Adaptive least absolute regression network analysis improves genetic network reconstruction by employing prior knowledge. PhD Thesis, Delft University ofTechnology.
34. Zellner, A (1983) Applications of Bayesian analysis in econometrics. Statistician, 32, 23.
35. Zou, H. and Hastie, T. (2005) Regularization and variable selection via the elastic net. J. R. Stat. Soc. B Stat. Methodol., 67, 301-320.
36. Zou, H. and Zhang, H.H. (2009) On the adaptive elasticnet with a diverging number ofparameters. Ann. Stat., 37, 1733-1751.