Autoregressive models for gene regulatory network inference: Sparsity, stability and causality issues

Mathematical Biosciences

Volumn 246, Issue 2, 2013, Pages 326-334

  Michailidis, George ^a   D'Alché Buc, Florence ^b,c  

^a UNIVERSITY OF MICHIGAN   (United States)

^b CNRS   (France)

^c UNIVERSITY OF EVRY   (France)

Author keywords

Autoregressive models; Causality; Dynamic Bayesian networks; Gene regulatory network inference; Sparsity; Stability

Indexed keywords

AUTO REGRESSIVE MODELS; CAUSALITY; DYNAMIC BAYESIAN NETWORKS; GENE REGULATORY NETWORKS; SPARSITY;

BAYESIAN NETWORKS; CONVERGENCE OF NUMERICAL METHODS; ORDINARY DIFFERENTIAL EQUATIONS;

GENES;

BAYESIAN ANALYSIS; CANONICAL ANALYSIS; CLUSTER ANALYSIS; GENE; GENOME; GENOMICS; NUMERICAL MODEL; REGRESSION ANALYSIS;

ALGORITHM; ARTICLE; BAYES THEOREM; CAUSAL NETWORK; DYNAMIC BAYESIAN NETWORK; ESCHERICHIA COLI; FIRST ORDER MARKOV MODEL; GENE REGULATORY NETWORK; INFORMATION PROCESSING; KERNEL METHOD; LINEAR AUTOREGRESSIVE MODEL; MARKOV CHAIN MONTE CARLO; MARKOV MODEL OF ORDER M; MATHEMATICAL MODEL; MATHEMATICAL PARAMETERS; MONTE CARLO METHOD; NONHUMAN; NONLINEAR AUTOREGRESSIVE MODEL; PROBABILITY; SACCHAROMYCES CEREVISIAE; SPARSE NETWORK; STABLE NETWORK; STAPHYLOCOCCUS AUREUS;

AUTOREGRESSIVE MODELS; CAUSALITY; DYNAMIC BAYESIAN NETWORKS; GENE REGULATORY NETWORK INFERENCE; SPARSITY; STABILITY;

BAYES THEOREM; CLUSTER ANALYSIS; GENE REGULATORY NETWORKS; GENOMICS; HUMANS; MODELS, GENETIC;

EID: 84888045025     PISSN: 00255564     EISSN: 18793134     Source Type: Journal    
DOI: 10.1016/j.mbs.2013.10.003     Document Type: Article

References (74)

1. Licatalosi D., Darnell R.B. Rna processing and its regulation: global insights into biological networks. Nature Reviews Genetics 2010, 75.
2. Aebersold R., Mann M. Mass spectrometry-based proteomics. Nature 2003, 198.
3. Dettmer K., Aronov P., Hammock B. Mass spectrometry-based metabolomics. Mass Spectrom Reviews 2007, 26:51.
4. Michailidis G. Statistical challenges in biological networks. Journal of Computational and Graphical Statistics 2012, 21:840.
5. Voit E. Modelling metabolic networks using power-laws and s-systems. Essays Biochemistry 2008, 29.
6. Brouard C., d'Alché Buc F., Szafranski M. Semi-supervised penalized output kernel regression for link prediction. ICML 2011, 593. Omnipress. L. Getoor, T. Scheffer (Eds.).
7. Q.C. Zhang, D. Petrey, L. Deng, L. Qiang, Y. Shi, C. Thu, B. Bisikirska, C. Lefebvre, D. Accili, T. Hunter, et al., Structure-based prediction of protein-protein interactions on a genome-wide scale, Nature 490 (2012) 556.
8. Sima C., Hua J., Jung S. Inference of gene regulatory networks using time-series data: a survey. Current Genomics 2009, 416.
9. Chou I.C., Voit E.O. Recent developments in parameter estimation and structure identification of biochemical and genomic systems. Mathematical Biosciences 2009, 219:57.
10. Lawrence N., Girolami M., Rattray M., Sanguinetti G. Learning and Inference in Computational Systems Biology 2010, MIT Press.
11. Auliac C., Frouin V., Gidrol X., d'Alché Buc F. Evolutionary approaches for the reverse-engineering of gene regulatory networks: a study on a biologically realistic dataset. BMC Bioinformatics 2008, 9:91.
12. Mordelet F., Vert J.-P. Sirene: supervised inference of regulatory networks. Bioinformatics 2008, 24:i76.
13. C. Brouard, J. Dubois, C. Vrain, D. Castel, M.-A. Debily, F. d'Alché Buc, Learning a markov logic network for supervised inference of a gene regulatory network: application to the id2 regulatory network in human keratinocytes, BMC Bioinformatics, to appear, 2013.
14. Perrin B.-E., Ralaivola L., Mazurie A., Bottani S., Mallet J., d'Alché Buc F. Gene networks inference using dynamic bayesian networks. Bioinformatics 2003, 19:38.
15. Hartemink A. Reverse engineering gene regulatory networks. Nature Biotechnology 2005, 23:554.
16. Bansal M., Della Gatta G., di Bernardo D. Inference of gene regulatory networks and compound mode of action from time course gene expression profiles. Bioinformatics 2006, 22:815.
17. A. Fujita, J. Sato, H. Garay-Malpartida, R. Yamaguchi, S. Miyano, M. Sogayar, C.E. Ferreira, Modeling gene expression regulatory networks with the sparse vector autoregressive model, BMC Systems Biology 1 (2007), Article 39.
18. Shojaie A., Michailidis G. Penalized likelihood methods for estimation of sparse high dimensional directed acyclic graphs. Biometrika 2010, 97(3):519.
19. S. Basu, A. Shojaie, G. Michailidis, Network granger causality with inherent grouping structure, 2012, 1. ArXiv:1210.3711v3.
20. Lim N., Senbabaoglu Y., Michailidis G., d'Alché Buc F. Okvar-boost: a novel boosting algorithm to infer nonlinear dynamics and interactions in gene regulatory networks. Bioinformatics 2013, 29:1416.
21. Margolin A.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 2006, 7:S7.
22. Zoppoli P., Morganella S., Ceccarelli M. Timedelay-aracne: reverse engineering of gene networks from time-course data by an information theoretic approach. BMC Bioinformatics 2010, 11:154.
23. Meinshausen N., Bühlmann P. Stability selection (with discussion). Journal of the Royal Statistical Society:Series B 2010, 417.
24. V.A. Huynh-Thu, A. Irrthum, L. Wehenkel, P. Geurts, Inferring regulatory networks from expression data using tree-based methods, PLos ONE 5 (2010) e12776.
25. A.-C. Haury, F. Mordelet, P. Vera-Licona, J.-P. Vert, Tigress: Trustful inference of gene regulation using stability selection, BMC Systems Biology 6 (2012), Article 145.
26. Mukherjee S., Speed T. Network inference using informative priors. Proceedings of the National Academy of Sciences 2008, 105:14313.
27. Pearl J. Causality: Models, Reasoning, and Inference 2000, Cambridge Univ Press.
28. Gardner T.S., di Bernardo D., Lorenz D., Collins J.J. Inferring genetic networks and identifying compound mode of action via expression profiling. Science 2003, 301:102.
29. Tegner J., Yeung M.K., Hasty J., Collins J.J. Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling. Proceedings of the National Academy of Sciences USA 2003, 100:5944.
30. Granger C.W.J. Investigating causal relations by econometric models and cross-spectral methods. Econometrica 1969, 37:424.
31. Karlebach G., Shamir R. Modelling and analysis of gene regulatory networks. Nature Reviews Molecular Cell Biology 2008, 9:770.
32. Batt G., Ropers D., Jong H.D., Geiselmann J., Mateescu R., Schneider D. Validation of qualitative models of genetic regulatory networks by model checking: analysis of the nutritional stress response in escherichia coli. Bioinformatics 2005, 21:19.
33. Voit E., Savageau M. Power-law approach to modeling biological systems; iii. methods of analysis. Journal of Fermentation Technology 1982, 60:233.
34. Voit E. Computational Analysis of Biochemical Systems: A Practical Guide for Biochemists and Molecular Biologists 2000, Cambridge University Press, Cambridge; New York.
35. Vilela M., Chou I.-C., Vinga S., Vasconcelos A., Voit E., Almeida J. Parameter optimization in s-system models. BMC Systems Biology 2008, 2:35.
36. Shojaie A., Michailidis G. Discovering graphical granger causality using a truncating lasso penalty. Bioinformatics 2010, 26(18):i517.
37. Lozano A.C., Abe N., Liu Y., Rosset S. Grouped graphical granger modeling for gene expression regulatory networks discovery. Bioinformatics 2009, 25:i110.
38. Lütkepohl H. New Introduction to Multiple Time Series Analysis 2005, Springer.
39. Mukhopadhyay N., Chatterjee S. Causality and pathway search in microarray time series experiment. Bioinformatics 2007, 23:442.
40. Benjamini Y., Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B Methodological 1995, 57:289.
41. Barabasi A.-L., Albert R. Emergence of scaling in random networks. Science 1999, 286:11.
42. T. Shimamura, S. Imoto, R. Yamaguchi, A. Fujita, M. Nagasaki, S. Miyano, Recursive regularization for inferring gene networks from time-course gene expression profiles, BMC Systems Biology, 2009.
43. Opgen-Rhein R., Strimmer K. Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregressive process. BMC Bioinformatics 2007, 8:S3.
44. Buhlmann P., van de Geer S. Statistics for High-Dimensional Data: Methods, Theory and Applications 2011, Springer.
45. Kim S., Imoto S., Miyano S. Dynamic bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data. Biosystems 2004, 75:57.
46. Morrissey E.R., Jurez M.A., Denby K.J., Burroughs N.J. Inferring the time-invariant topology of a nonlinear sparse gene regulatory network using fully bayesian spline autoregression. Biostatistics 2011, 12:682.
47. A. Fouchet, J.-M. Delosme, F. d'Alché Buc, Gene regulatory network inference using ensemble of multiple local kernel models, Programme of Seventh International Workshop on Machine Learning in Systems Biology, satellite meeting of ISMB'2013, Uwe Owler and Jean-Philippe Vert, July 19-20, 2013.
48. Gonen M., Alpaydyn E. Multiple kernel learning algorithms. JMLR 2011, 12:2211.
49. Rakotomamonjy A., Bach F., Canu S., Grandvalet Y. Simplemkl. Journal of Machine Learning Research 2008, 9:2491.
50. Dojer N. Learning bayesian networks does not have to be np-hard. Proceedings of International Symposium on Mathematical Foundations of Computer Science 2006, 305.
51. Xuan N., Chetty M., Coppel R., Wangikar P. Gene regulatory network modeling via global optimization of high-order dynamic bayesian network. BMC Bioinformatics 2012, 13:131.
52. Tresch A., Markowetz F. Structure learning in nested effects models. Statistical Applications in Genetics and Molecular Biology 2008, 7:9.
53. Eaton D., Murphy K.P. Exact bayesian structure learning from uncertain interventions. Journal of Machine Learning Research - Proceedings Track 2007, 2:107.
54. S. Spencer, S. Hill, S. Mukherjee, Dynamic Bayesian networks for interventional data, Technical Report, Warwick University, UK, 2012.
55. Rajapakse J.C., Mundra P.A. Stability of building gene regulatory networks with sparse autoregressive models. BMC Bioinformatics 2011, 12:S17.
56. de Matos Simoes R., Emmert-Streib F. Bagging statistical network inference from large-scale gene expression data. PLoS One 2012, 7:e33624.
57. Marbach D., Costello J.C., Kuffner R., Vega N.M., Prill R.J., Camacho D.M., Allison K.R., Consortium T.D., Kellis M., Collins J.J., et al. Wisdom of crowds for robust gene network inference. Nature Methods 2012, 9:796.
58. Albert R. Scale-free networks in cell biology. Journal of Cell Science 2005, 118:4947.
59. Valouev A., Johnson D., Sundquist A., Medina C., Anton E., Batzoglou S., Myers R., Sidow A. Genome-wide analysis of transcription factor binding sites based on chip-seq data. Nature Methods 2008, 5:829.
60. Wingender E. The transfac project as an example of framework technology that supports the analysis of genomic regulation. Briefings in Bioinformatics 2008, 9:326.
61. Weber M., Henkel S., Vlaic S., Guthke R., van Zoelen E., Driesch D. Inference of dynamical gene-regulatory networks based on time-resolved multi-stimuli multi-experiment data applying netgenerator v2.0. BMC Systems Biology 2013, 7:1.
62. Zheng J., Chaturvedi I., Rajapakse J.C. Integration of epigenetic data in bayesian network modeling of gene regulatory network. Lecture Notes in Computer Science 2011, 7036:87. Springer. M. Loog, L.F.A. Wessels, M.J.T. Reinders, D. de Ridder (Eds.).
63. Pinna A., Soranzo N., de la Fuente A. From knockouts to networks: establishing direct cause-effect relationships through graph analysis. PLoS ONE 2010, 5:e12912.
64. Greenfield A., Hafemeister C., Bonneau R. Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks. Bioinformatics 2013, 29:1060.
65. Imoto S., Higuchi T., Goto T., Tashiro K., Kuhara S., Miyano S. Combining microarrays and biological knowledge for estimating gene networks via bayesian networks. Proceedings of the IEEE Computer Society Bioinformatics Conference (CSB 03) 2003, 104. IEEE.
66. A. Werhli, D. Husmeier, Reconstructing gene regulatory networks with bayesian networks by combining expression data with multiple sources of prior knowledge, Statistical Applications in Genetics and Molecular Biology 6 (2007), Article 15.
67. Bock M., Ogishima S., Tanaka H., Kramer S., Kaderali L. Hub-centered gene network reconstruction using automatic relevance determination. PLoS ONE 2012, 7:e35077.
68. Marbach D., Prill R.J., Schaffter T., Mattiussi C., Floreano D., Stolovitzky G. Revealing strengths and weaknesses of methods for gene network inference. Proceedings of the National Academy of Sciences USA 2010, 107(14):6286.
69. Prill R.J., Saez-Rodriguez J., Alexopoulos L.G., Sorger P.K., Stolovitzky G. Crowdsourcing network inference: the dream predictive signaling network challenge. Science Signaling 2011, 4:mr7.
70. Gupta R., Stincone A., Antczak P., Durant S., Bicknell R., Bikfalvi A., Falciani F. A computational framework for gene regulatory network inference that combines multiple methods and datasets. BMC Systems Biology 2011, 5:52.
71. Scholkopf B., Tsuda T., Vert J.-P. Kernel Methods In Computational Biology 2004, The MIT press.
72. Davidson E.H., Levine M.S. Properties of developmental gene regulatory networks. Proceedings of the National Academy of Sciences 2008, 105:20063.
73. Segal E., Shapira M., Regev A., Pe'er D., Botstein D., Koller D., Friedman N. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nature Genetics 2003, 34(2):166.
74. Steiert B., Raue A., Timmer J., Kreutz C. Experimental design for parameter estimation of gene regulatory networks. PLoS ONE 2012, 7:e40052.