Reverse engineering gene regulatory networks using approximate Bayesian computation

Statistics and Computing

Volumn 22, Issue 6, 2012, Pages 1257-1271

  Rau, Andrea ^a,b   Jaffrézic, Florence ^b   Foulley, Jean Louis ^b   Doerge, R W ^a,c  

^a PURDUE UNIVERSITY   (United States)

^b UMR1313 GÉNÉTIQUE ANIMALE ET BIOLOGIE INTÉGRATIVE   (France)

^c PURDUE UNIVERSITY   (United States)

Author keywords

Approximate Bayesian computation; Gene regulatory networks; Longitudinal gene expression; Markov chain Monte Carlo

Indexed keywords

EID: 84867979927     PISSN: 09603174     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11222-011-9309-1     Document Type: Article

References (44)

1. Alon, U.: Network motifs: theory and experimental approaches. Nat. Genet. Rev. 8, 450-461 (2007).
2. Beal, M. J., Falciani, F., Ghahramani, Z., Rangel, C., Wild, D. L.: A Bayesian approach to reconstructing genetic regulatory networks with hidden factors. Bioinformatics 21(3), 349-356 (2005).
3. Beaumont, M. A., Zhang, W., Balding, D. J.: Approximate Bayesian computation in population genetics. Genetics 162, 2025-2035 (2002).
4. Beaumont, M. A., Cornuet, J. M., Marin, J. M., Robert, C. P.: Adaptivity for ABC algorithms: the ABC-PMC. Biometrika 96(4), 983-990 (2009).
5. Bortot, P., Coles, S. G., Sisson, S. A.: Inference for stereological extremes. J. Am. Stat. Assoc. 102, 84-92 (2007).
6. Charbonnier, C., Chiquet, J., Ambroise, C.: Weighted-LASSO for structured network inference from time course data. Stat. Appl. Genet. Mol. Biol. 9(15) (2010).
7. Cleveland, W. S.: Robust locally weighted regression and smoothing scatterplots. J. Am. Stat. Assoc. 74, 829-836 (1979).
8. Damien, P., Wakefield, J., Walker, S.: Gibbs sampling for Bayesian non-conjugate and hierarchical models using auxiliary variables. J. R. Stat. Soc. B 61(2), 331-344 (1999).
9. Del Moral, P., Doucet, A., Jasra, A.: Sequential Monte Carlo samplers. J. R. Stat. Soc. B 68, 411-436 (2006).
10. Del Moral, P., Doucet, A., Jasra, A.: An adaptive sequential Monte Carlo method for approximate Bayesian computation. Stat. Comput. (2011). doi: 10. 1007/s11222-011-9271-y.
11. Drovandi, C. C., Pettitt, A. N.: Estimation of parameters for macroparasite population evolution using approximate Bayesian computation. Biometrics 67, 225-233 (2011).
12. Friedman, N.: Using Bayesian networks to analyze expression data. J. Comput. Biol. 7(3/4), 601-620 (2000).
13. Friedman, N.: Inferring cellular networks using probabilistic graphical models. Science 303(799), 799-805 (2004).
14. Gelman, A., Rubin, D. B.: Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457-511 (1992).
15. Geyer, C. J.: Practical Markov chain Monte Carlo. Stat. Sci. 7, 473-511 (1992).
16. Gilks, W. R., Richardson, S., Spiegelhalter, D. J. (eds.): Markov Chain Monte Carlo in Practice: Interdisciplinary Statistics. Chapman and Hall/CRC, Boca Raton (1996).
17. Gottardo, R., Raftery, A. E.: Markov chain Monte Carlo with mixtures of singular distributions. Tech. Rep. 470, University of Washington, Department of Statistics (2004).
18. Hastings, W. K.: Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57(1), 97-109 (1970).
19. Husmeier, D.: Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics 19(17), 2271-2282 (2003).
20. Husmeier, D., Dybowski, R., Roberts, S. (eds.): Probabilistic Modeling in Bioinformatics and Medical Informatics. Springer, Berlin (2005).
21. Leclerc, R. D.: Survival of the sparsest: robust gene networks are parsimonious. Mol. Syst. Biol. 4(213) (2008).
22. Leuenberger, C., Wegmann, D.: Bayesian computation and model selection without likelihoods. Genetics 183, 1-10 (2009).
23. Lund, R., Li, B.: Revisiting climate region definitions via clustering. Am. Meteorol. Soc. 22, 1787-1800 (2009).
24. Marjoram, P., Molitor, J., Plagnol, V., Tavaré, S.: Markov chain Monte Carlo without likelihoods. Proc. Natl. Acad. Sci. 100(26), 15324-15328 (2003).
25. 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 Bioinf. 8(Suppl 2) (2007).
26. Perrin, B. E., Ralaivola, L., Mazurie, A., Bottani, S., Mallet, J., d'Alché Buc, F.: Gene networks inference using dynamic Bayesian networks. Bioinformatics 19(Suppl. 2), ii138-ii148 (2003).
27. Pritchard, J. K., Seielstad, M. T., Perez-Lezann, A., Feldman, M. W.: Population growth of human Y chromosomes: a study of Y chromosome microsatellites. Mol. Biol. Evol. 16, 1791-1798 (1999).
28. Rangel, C., Angus, J., Ghahramani, Z., Lioumi, M., Southeran, E., Gaiba, A., Wild, D. L., Falciani, F.: Modeling T-cell activation using gene expression profiling and state-space model. Bioinformatics 20(9), 1361-1372 (2004).
29. Ratmann, O., Jorgensen, O., Hinkley, T., Stumpf, M., Richardson, S., Wiuf, C.: Using likelihood-free inference to compare evolutionary dynamics of the protein networks of H. pylori and P. falciparum. PLoS Comput. Biol. 3(11), 2266-2278 (2007). doi: 10. 1371/journal. pcbi. 0030230.
30. Ratmann, O., Andrieu, C., Wiuf, C., Richardson, S.: Model criticism based on likelihood-free inference, with an application to protein network evolution. Proc. Natl. Acad. Sci. 106(26), 10576-10581 (2009).
31. Ratmann, O., Pudlo, P., Richardson, S., Robert, C.: Monte Carlo algorithms for model assessment via conflicting summaries. ArXiv e-prints 1106. 5919v1 (2011).
32. Rau, A.: Reverse engineering gene networks using genomic time-course data. Ph. D. dissertation, Purdue University, West Lafayette, IN, USA (2010).
33. Rau, A., Jaffrézic, F., Foulley, J. L., Doerge, R. W.: An empirical Bayesian method for estimating biological networks from temporal microarray data. Stat. Appl. Genet. Mol. Biol. 9(9), 1-28 (2010).
34. Robert, C.: Bayesian computational methods. http://arxiv. org/abs/1002. 2702 (2010).
35. Robert, C., Casella, G.: Monte Carlo Statistical Methods. Springer, Berlin (2004).
36. Ronen, M., Rosenberg, R., Shraiman, B. I., Alon, U.: Assigning numbers to the arrows: parameterizing a gene regulation network by using accurate expression kinetics. Proc. Natl. Acad. Sci. 99(16), 10555-10560 (2002).
37. Sachs, K., Perez, O., Pe'er, D., Lauffenburger, D. A., Nolan, G. P.: Causal protein-signaling networks derived from multiparameter single-cell data. Science 308(5721), 523-529 (2005).
38. Schlitt, T., Brazma, A.: Current approaches to gene regulatory network modelling. BMC Bioinform. 8(Suppl 6(S9)), 1-22 (2007).
39. Sisson, S. A., Fan, Y., Tanaka, M. M.: Sequential Monte Carlo without likelihoods. Proc. Natl. Acad. Sci. 104, 1760-1765 (2007).
40. Toni, T., Stumpf, M. P. H.: Simulation-based model selection for dynamical systems in systems and population biology. Bioinformatics 26(1), 104-110 (2010).
41. Toni, T., Welch, D., Strelkowa, N., Ipsen, A., Stumpf, M. P. H.: Approximate Bayesian computation scheme for parameter inference and model selection in dynamic systems. J. R. Soc. Interface 6(31), 187-202 (2009).
42. Wegmann, D., Leuenberger, C., Excoffier, L.: Efficient approximate Bayesian computation coupled with Markov chain Monte Carlo without likelihood. Genetics 182, 1207-1218 (2009).
43. Werhli, A. V., Husmeier, D.: Reconstructing gene regulatory networks with Bayesian networks by combining expression data with multiple sources of prior knowledge. Stat. Appl. Genet. Mol. Biol. 6(1), 15 (2007).
44. Wilkinson, D. J.: Stochastic modelling for quantitative description of heterogeneous biological systems. Nat. Rev. Genet. 10, 122-133 (2009).