R. Albert and A. Barabási, Statistical mechanics of complex networks, Reviews of Modern Physics, vol.86, issue.1, pp.47-97, 2002.
DOI : 10.1103/PhysRevLett.86.5835

R. Albert, H. Jeong, and A. Barabási, Error and attack tolerance of complex networks, Nature, vol.1696, issue.6794, pp.378-382, 2000.
DOI : 10.1007/3-540-48155-9_27
URL : http://arxiv.org/pdf/cond-mat/0008064

U. Alon, Network motifs: theory and experimental approaches, Nature Reviews Genetics, vol.301, issue.6, pp.450-461, 2007.
DOI : 10.1091/mbc.9.12.3273

A. Barabási and R. Albert, Emergence of scaling in random networks, Science, vol.286, pp.509-512, 1999.

A. Bernard and A. Hartemink, INFORMATIVE STRUCTURE PRIORS: JOINT LEARNING OF DYNAMIC REGULATORY NETWORKS FROM MULTIPLE TYPES OF DATA, Biocomputing 2005, pp.459-470, 2005.
DOI : 10.1142/9789812702456_0044

H. Blalock, Causal models in the Social Sciences, 1971.
DOI : 10.4324/9781315081663

F. Bois, GNU MCSim: Bayesian statistical inference for SBML-coded systems biology models, Bioinformatics, vol.24, issue.17, pp.1453-1454, 2009.
DOI : 10.1093/bioinformatics/btn338
URL : https://hal.archives-ouvertes.fr/ineris-00961935

G. Casella and C. Robert, Monte Carlo Statistical Methods, 2004.

D. Jong and H. , Modeling and Simulation of Genetic Regulatory Systems: A Literature Review, Journal of Computational Biology, vol.9, issue.1, pp.67-103, 2002.
DOI : 10.1089/10665270252833208
URL : https://hal.archives-ouvertes.fr/inria-00072606

R. Dobrin, Q. Beg, A. Barabasi, and Z. Oltvai, Aggregation of topological motifs in the Escherichia coli transcriptional regulatory network, BMC Bioinformatics, vol.5, issue.1, p.10, 2004.
DOI : 10.1186/1471-2105-5-10

P. Erdös and A. Rényi, On random graphs, I. Publ. Math. Debrecen, vol.6, pp.290-297, 1959.

P. Erdös and A. Rényi, On the evolution of random graphs, Publ. Math. Inst. Hung. Acad. Sci, vol.5, pp.17-61, 1960.

P. Erdös and A. Rényi, On the strength of connectedness of a random graph, Acta Mathematica Academiae Scientiarum Hungaricae, vol.5, issue.1-2, pp.261-267, 1961.
DOI : 10.4153/CJM-1956-045-5

A. Gelman and D. Rubin, Inference from iterative simulation using multiple sequences (with discussion), 1992.
DOI : 10.1214/ss/1177011136
URL : https://doi.org/10.1214/ss/1177011136

, Stat. Sci, vol.7, pp.457-511

J. Gibbs, Elementary Principles in Statistical Mechanics Scribner's, 1902. Reprint, Wood-bridge, p.vi?viii, 1902.

, Helbing D (2013) Globally networked risks and how to respond, Nature, vol.497, pp.51-59

S. Janson, A. Rucinski, and T. Luczak, Random graphs, 2000.
DOI : 10.1002/9781118032718

H. Jeong, B. Tombor, R. Albert, Z. Oltvai, and A. Barabási, The large-scale organization of metabolic networks, Nature, vol.97, issue.6804, pp.651-654, 2000.
DOI : 10.1073/pnas.97.10.5528

N. Kashtan, S. Itzkovitz, R. Milo, and U. Alon, Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs, Bioinformatics, vol.20, issue.11, pp.1746-1758, 2004.
DOI : 10.1093/bioinformatics/bth163

J. Krumsiek, K. Suhre, T. Illig, J. Adamski, and F. Theis, Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data, BMC Systems Biology, vol.5, issue.1, p.21, 2011.
DOI : 10.1186/1752-0509-5-21
URL : https://bmcsystbiol.biomedcentral.com/track/pdf/10.1186/1752-0509-5-21?site=bmcsystbiol.biomedcentral.com

S. Lauritzen, Graphical models, 1996.

J. Leskovec, D. Chakrabarti, J. Kleinberg, C. Faloutsos, and Z. Ghahramani, Kronecker graphs: an approach to modeling networks, J. Mach. Learning Res, vol.11, pp.985-1042, 2010.

H. Liu, F. Han, M. Yuan, J. Lafferty, and L. Wasserman, High-dimensional semiparametric Gaussian copula graphical models, The Annals of Statistics, vol.40, issue.4, pp.2293-2326, 2012.
DOI : 10.1214/12-AOS1037
URL : http://doi.org/10.1214/12-aos1037

S. Mangan and U. Alon, Structure and function of the feed-forward loop network motif, Proc. Natl Acad, 2003.
DOI : 10.1038/35014651

. Sci and . Usa, , pp.11980-11985

S. Mangan, A. Zaslaver, and U. Alon, The Coherent Feedforward Loop Serves as a Sign-sensitive Delay Element in Transcription Networks, Journal of Molecular Biology, vol.334, issue.2, pp.197-204, 2003.
DOI : 10.1016/j.jmb.2003.09.049

S. Mangan, A. Zaslaver, and U. Alon, The Incoherent Feed-forward Loop Accelerates the Response-time of the gal System of Escherichia coli, Journal of Molecular Biology, vol.356, issue.5, pp.1073-1081, 2006.
DOI : 10.1016/j.jmb.2005.12.003

F. Markowetz and R. Spang, Inferring cellular networks ??? a review, BMC Bioinformatics, vol.8, issue.Suppl 6, p.5, 2007.
DOI : 10.1186/1471-2105-8-S6-S5

A. Masoudi-nejad, F. Schreiber, and M. Razaghi, Building blocks of biological networks: a review on major network motif discovery algorithms, IET Systems Biology, vol.6, issue.5, pp.164-174, 2012.
DOI : 10.1049/iet-syb.2011.0011

R. Milo, S. Itzkovitz, N. Kashtan, R. Levitt, S. Shen-orr et al., Superfamilies of Evolved and Designed Networks, Science, vol.303, issue.5663, pp.1538-1542, 2004.
DOI : 10.1126/science.1089167

R. Milo, S. Shen-orr, S. Itzkovitz, N. Kashtan, D. Chklovskii et al., Network Motifs: Simple Building Blocks of Complex Networks, Science, vol.298, issue.5594, pp.824-827, 2002.
DOI : 10.1126/science.298.5594.824

S. Mukherjee and T. Speed, Network inference using informative priors, Proceedings of the National Academy of Sciences, vol.6, issue.5721, pp.14313-14318, 2008.
DOI : 10.1126/science.1105809
URL : http://www.pnas.org/content/105/38/14313.full.pdf

R. Neapolitan, Learning Bayesian Networks R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing, 2003.

C. Robert, The Bayesian Choice: from Decision-Theoretic Motivations to Computational Implementation, 2001.
DOI : 10.1007/978-1-4757-4314-2

H. Salgado, M. Peralta-gil, S. Gama-castro, A. Santos-zavaleta, L. Muniz-rascado et al., RegulonDB v8.0: omics data sets, evolutionary conservation, regulatory phrases, cross-validated gold standards and more, Nucleic Acids Research, vol.254, issue.D1, pp.203-213, 2013.
DOI : 10.1006/jmbi.1995.0638

S. Shen-orr, R. Milo, S. Mangan, and U. Alon, Network motifs in the transcriptional regulation network of Escherichia coli, Nature Genetics, vol.14, issue.1, pp.64-68, 2002.
DOI : 10.1002/(SICI)1098-2418(199907)14:4<293::AID-RSA1>3.0.CO;2-G

R. Silva and Z. Ghahramani, The hidden life of latent variables: Bayesian learning with mixed graph models, J. Machine Learning Res, vol.10, pp.1187-1238, 2009.

T. Van-den-bulcke, K. Van-leemput, B. Naudts, P. Van-remortel, M. H. Verschoren et al., SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms, BMC Bioinformatics, vol.7, issue.1, p.43, 2006.
DOI : 10.1186/1471-2105-7-43

D. Watts and S. Strogatz, Collective dynamics of 'small-world' networks, Nature, vol.393, pp.409-410, 1998.

J. Whittaker, Graphical models in applied multivariate statistics, Wiley series in probability and mathematical statistics: Probability and mathematical statistics, 1990.

R. Wilson, Introduction to Graph Theory, 2012.

H. Wold, Causality and Econometrics, Econometrica, vol.22, issue.2, pp.162-177, 1954.
DOI : 10.2307/1907540

S. Wright, Correlation and causation, Journal of Agricultural Research, vol.20, pp.557-585, 1921.