R. Gutiérrez-ríos, D. Rosenblueth, J. Loza, A. Huerta, J. Glasner et al., Regulatory Network of Escherichia coli: Consistency Between Literature Knowledge and Microarray Profiles, Genome Research, vol.13, issue.11, pp.2435-2443, 2003.
DOI : 10.1101/gr.1387003

T. Lee, N. Rinaldi, F. Robert, D. Odom, Z. Bar-joseph et al., Transcriptional Regulatory Networks in Saccharomyces cerevisiae, Transcriptional regulatory networks in Saccharomyces cerevisiae, pp.799-804, 2002.
DOI : 10.1126/science.1075090

S. Kim, J. Lund, M. Kiraly, K. Duke, M. Jiang et al., A Gene Expression Map for Caenorhabditis elegans, Science, vol.293, issue.5537, pp.2087-2092, 2001.
DOI : 10.1126/science.1061603

S. Ma, Q. Gong, and H. Bohnert, An Arabidopsis gene network based on the graphical Gaussian model, Genome Research, vol.17, issue.11
DOI : 10.1101/gr.6911207

W. Mentzen and E. Wurtele, Regulon organization of Arabidopsis, BMC Plant Biology, vol.8, issue.1, p.99, 2008.
DOI : 10.1186/1471-2229-8-99

H. Lee, A. Hsu, J. Sajdak, J. Qin, and P. Pavlidis, Coexpression Analysis of Human Genes Across Many Microarray Data Sets, Genome Research, vol.14, issue.6, pp.1085-1094, 2004.
DOI : 10.1101/gr.1910904

M. Eisen, P. Spellman, P. Brown, and D. Botstein, Cluster analysis and display of genome-wide expression patterns, Proceedings of the National Academy of Sciences, vol.95, issue.25, pp.14863-14868, 1998.
DOI : 10.1073/pnas.95.25.14863

U. Alon, N. Barkai, D. Notterman, K. Gish, S. Ybarra et al., Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays, Proceedings of the National Academy of Sciences, vol.96, issue.12, pp.6745-6750, 1999.
DOI : 10.1073/pnas.96.12.6745

A. Ben-dor, R. Shamir, and Z. Yakhini, Clustering Gene Expression Patterns, Journal of Computational Biology, vol.6, issue.3-4, pp.281-297, 1999.
DOI : 10.1089/106652799318274

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.5341

P. Dhaeseleer, S. Liang, and R. Somogyi, Genetic network inference: from co-expression clustering to reverse engineering, Bioinformatics, vol.16, issue.8, pp.707-726, 2000.
DOI : 10.1093/bioinformatics/16.8.707

J. Ihmels, G. Friedlander, S. Bergmann, O. Sarig, Y. Ziv et al., Revealing modular organization in the yeast transcriptional network, Nature Genetics, vol.31, pp.370-377, 2002.
DOI : 10.1038/ng941

A. Butte and I. Kohane, MUTUAL INFORMATION RELEVANCE NETWORKS: FUNCTIONAL GENOMIC CLUSTERING USING PAIRWISE ENTROPY MEASUREMENTS, Biocomputing 2000, pp.418-429, 2000.
DOI : 10.1142/9789814447331_0040

K. Basso, A. Margolin, G. Stolovitzky, U. Klein, R. Dalla-favera et al., Reverse engineering of regulatory networks in human B cells, Nature Genetics, vol.14, issue.4, pp.382-390, 2005.
DOI : 10.1101/gad.906601

A. Margolin, I. Nemenman, K. Basso, C. Wiggins, G. Stolovitzky et al., ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context, BMC Bioinformatics, vol.7, issue.Suppl 1, p.7, 2006.
DOI : 10.1186/1471-2105-7-S1-S7

J. Faith, B. Hayete, J. Thaden, I. Mogno, J. Wierzbowski et al., Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles, PLoS Biology, vol.280, issue.1, p.8, 2007.
DOI : 10.1371/journal.pbio.0050008.sd001

P. Meyer, K. Kontos, F. Lafitte, and G. Bontempi, Information-Theoretic Inference of Large Transcriptional Regulatory Networks, EURASIP Journal on Bioinformatics and Systems Biology, vol.6, issue.1, p.79879, 2007.
DOI : 10.1162/089976698300017197

D. Husmeier, Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks, Bioinformatics, vol.19, issue.17, pp.2271-2282, 2003.
DOI : 10.1093/bioinformatics/btg313

J. Yu, V. Smith, P. Wang, A. Hartemink, and E. Jarvis, Advances to Bayesian network inference for generating causal networks from observational biological data, Bioinformatics, vol.20, issue.18, pp.3594-3603, 2004.
DOI : 10.1093/bioinformatics/bth448

A. Fujita, J. Sato, H. Garay-malpartida, R. Yamaguchi, S. Miyano et al., Modeling gene expression regulatory networks with the sparse vector autoregressive model, BMC Systems Biology, vol.1, issue.1, p.39, 2007.
DOI : 10.1186/1752-0509-1-39

F. Steinke, M. Seeger, and K. Tsuda, Experimental design for efficient identification of gene regulatory networks using sparse Bayesian models, BMC Systems Biology, vol.1, issue.1, p.51, 2007.
DOI : 10.1186/1752-0509-1-51

T. Gardner, D. Lorenz, D. Collins, and J. , Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling, Science, vol.301, issue.5629, pp.102-105, 2003.
DOI : 10.1126/science.1081900

D. Di-bernardo, M. Thompson, T. Gardner, S. Chobot, E. Eastwood et al., Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks, Nature Biotechnology, vol.176, issue.3, pp.377-383, 2005.
DOI : 10.1126/science.1088697

R. Bonneau, D. Reiss, P. Shannon, M. Facciotti, L. Hood et al., The inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology datasets de novo, Genome Biology, vol.7, issue.5, p.36, 2006.
DOI : 10.1186/gb-2006-7-5-r36

J. Carrera, R. G. Jaramillo, and A. , Model-based redesign of global transcription regulation, Nucleic Acids Research, vol.37, issue.5, p.38, 2009.
DOI : 10.1093/nar/gkp022

URL : https://hal.archives-ouvertes.fr/hal-00766032

R. Bonneau, A Predictive Model for Transcriptional Control of Physiology in a Free Living Cell, Cell, vol.131, issue.7, pp.1354-1365, 2007.
DOI : 10.1016/j.cell.2007.10.053

R. Irizarray, B. Hobbs, C. F. Beazer-barclay, Y. Antonellis, K. Scherf et al., Exploration, normalization, and summaries of high density oligonucleotide array probe level data, Biostatistics, vol.4, issue.2, pp.249-264, 2003.
DOI : 10.1093/biostatistics/4.2.249

P. Shannon, A. Markiel, O. Ozier, N. Baliga, J. Wang et al., Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks, Genome Research, vol.13, issue.11
DOI : 10.1101/gr.1239303

R. Albert and A. Barabási, Statistical mechanics of complex networks, Reviews of Modern Physics, vol.74, issue.1, pp.47-97, 2002.
DOI : 10.1103/RevModPhys.74.47

R. Albert, Scale-free networks in cell biology, Journal of Cell Science, vol.118, issue.21, pp.4947-4957, 2005.
DOI : 10.1242/jcs.02714

A. Barabási and Z. Oltvai, Network biology: understanding the cell's functional organization, Nature Reviews Genetics, vol.5, issue.2, pp.101-113, 2004.
DOI : 10.1038/nrg1272

R. Khanin and E. Wit, How Scale-Free Are Biological Networks, Journal of Computational Biology, vol.13, issue.3, pp.810-818, 2006.
DOI : 10.1089/cmb.2006.13.810

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.104.5347

E. Ravasz and A. Barabási, Hierarchical organization in complex networks, Physical Review E, vol.67, issue.2, p.26112, 2003.
DOI : 10.1103/PhysRevE.67.026112

E. Ravasz, A. Somera, D. Mongru, Z. Oltvai, and A. Barabási, Hierarchical Organization of Modularity in Metabolic Networks, Science, vol.297, issue.5586, pp.1551-1555, 2002.
DOI : 10.1126/science.1073374

Z. Oltvai and A. Barabási, SYSTEMS BIOLOGY: Life's Complexity Pyramid, Science, vol.298, issue.5594, pp.763-764, 2002.
DOI : 10.1126/science.1078563

N. Kashtan, S. Itzkovitz, R. Milo, and U. Alon, Topological generalizations of network motifs, Physical Review E, vol.70, issue.3, p.31909, 2004.
DOI : 10.1103/PhysRevE.70.031909

S. Mangan and U. Alon, Structure and function of the feed-forward loop network motif, Proceedings of the National Academy of Sciences, vol.100, issue.21, pp.11980-11985, 2003.
DOI : 10.1073/pnas.2133841100

S. Mangan, A. Zalsaver, 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

F. Hayot and C. Jayaprakash, A feedforward loop motif in transcriptional regulation: induction and repression, Journal of Theoretical Biology, vol.234, issue.1, pp.133-143, 2005.
DOI : 10.1016/j.jtbi.2004.11.010

U. Alon, Network motifs: theory and experimental approaches, Nature Reviews Genetics, vol.301, issue.6, pp.450-461, 2007.
DOI : 10.1038/nrg2102

R. Sanjuán and S. Elena, Epistasis correlates to genomic complexity, Proceedings of the National Academy of Sciences, vol.103, issue.39, pp.14402-14405, 2006.
DOI : 10.1073/pnas.0604543103

E. Dekel and U. Alon, Optimality and evolutionary tuning of the expression level of a protein, Nature, vol.115, issue.7050, pp.588-592, 2005.
DOI : 10.1146/annurev.mi.03.100149.002103

Z. Bar-joseph, Analyzing time series gene expression data, Bioinformatics, vol.20, issue.16, pp.2493-2503, 2004.
DOI : 10.1093/bioinformatics/bth283

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.304.9977

R. Tibshirani, Regression shrinkage and selection via de Lasso, J R Statist, vol.58, pp.267-288, 1996.
DOI : 10.1111/j.1467-9868.2011.00771.x

S. Shevade and S. Keerthi, A simple and efficient algorithm for gene selection using sparse logistic regression, Bioinformatics, vol.19, issue.17, pp.2246-2253, 2003.
DOI : 10.1093/bioinformatics/btg308

M. Hucka, H. Bolouri, A. Finney, H. Sauro, J. Doyle et al., The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models, Bioinformatics, vol.19, issue.4, pp.524-531, 2003.
DOI : 10.1093/bioinformatics/btg015

S. Wernicke and F. Rasche, FANMOD: a tool for fast network motif detection, Bioinformatics, vol.22, issue.9, pp.1152-1153, 2006.
DOI : 10.1093/bioinformatics/btl038

URL : http://bioinformatics.oxfordjournals.org/cgi/content/short/22/9/1152