H. Ge, A. Walhout, and M. Vidal, Integrating ???omic??? information: a bridge between genomics and systems biology, Trends in Genetics, vol.19, issue.10, pp.551-560, 2003.
DOI : 10.1016/j.tig.2003.08.009
URL : http://chagall.med.cornell.edu/BioinfoCourse/PDFs/Lecture13/Ge.pdf

C. Desert, M. Duclos, P. Blavy, F. Lecerf, F. Moreews et al., Transcriptome profiling of the feeding-to-fasting transition in chicken liver, BMC Genomics, vol.9, issue.1, p.611, 2008.
DOI : 10.1186/1471-2164-9-611
URL : https://hal.archives-ouvertes.fr/hal-00729919

Y. Jin, P. Dunlap, S. Mcbride, H. Al-refai, P. Bushel et al., Global Transcriptome and Deletome Profiles of Yeast Exposed to Transition Metals, PLoS Genetics, vol.30, issue.4, p.1000053, 2008.
DOI : 10.1371/journal.pgen.1000053.s008

J. Labaer, Mining the literature and large datasets, Nature Biotechnology, vol.21, issue.9, pp.976-977, 2003.
DOI : 10.1038/nbt0903-976b

S. Imoto, T. Higuchi, T. Goto, K. Tashiro, S. Kuhara et al., COMBINING MICROARRAYS AND BIOLOGICAL KNOWLEDGE FOR ESTIMATING GENE NETWORKS VIA BAYESIAN NETWORKS, Journal of Bioinformatics and Computational Biology, vol.02, issue.01, p.77, 2004.
DOI : 10.1142/S021972000400048X
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.108.2144

A. Rau, F. Jaffrézic, J. Foulley, and R. Doerge, An Empirical Bayesian Method for Estimating Biological Networks from Temporal Microarray Data, Statistical Applications in Genetics and Molecular Biology, vol.9, issue.1
DOI : 10.2202/1544-6115.1513
URL : https://hal.archives-ouvertes.fr/hal-01193424

R. Gutierrez-rios, 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, p.2435, 2003.
DOI : 10.1101/gr.1387003

C. Guziolowski, A. Bourde, F. Moreews, and A. Siegel, BioQuali Cytoscape plugin: analysing the global consistency of regulatory networks, BMC Genomics, vol.10, issue.1, p.244, 2009.
DOI : 10.1186/1471-2164-10-244
URL : https://hal.archives-ouvertes.fr/inria-00429804

M. Kanehisa and S. Goto, KEGG: Kyoto Encyclopedia of Genes and Genomes, Nucleic Acids Research, vol.28, issue.1, pp.27-30, 2000.
DOI : 10.1093/nar/28.1.27

R. Caspi, H. Foerster, C. Fulcher, P. Kaipa, M. Krummenacker et al., The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases, Nucleic Acids Research, vol.36, issue.Database, pp.623-631, 2008.
DOI : 10.1093/nar/gkm900

E. Cerami, B. Gross, E. Demir, I. Rodchenkov, O. Babur et al., Pathway Commons, a web resource for biological pathway data, Nucleic Acids Research, vol.39, issue.Database, pp.685-690, 2011.
DOI : 10.1093/nar/gkq1039

M. Krull, S. Pistor, N. Voss, A. Kel, I. Reuter et al., TRANSPATH(R): an information resource for storing and visualizing signaling pathways and their pathological aberrations, Nucleic Acids Research, vol.34, issue.90001, pp.546-551, 2006.
DOI : 10.1093/nar/gkj107
URL : https://hal.archives-ouvertes.fr/hal-00314881

C. Choi, M. Krull, A. Kel, O. Kel-margoulis, S. Pistor et al., ???A High Quality Database Focused on Signal Transduction, Comparative and Functional Genomics, vol.5, issue.2, pp.163-168, 2004.
DOI : 10.1002/cfg.386
URL : http://doi.org/10.1002/cfg.386

H. Jeong, B. Tombor, R. Albert, Z. Oltvai, and A. Barabasi, The large-scale organization of metabolic networks, Nature, vol.407, pp.651-654, 2000.

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

C. Jiang, Z. Xuan, F. Zhao, and M. Zhang, TRED: a transcriptional regulatory element database, new entries and other development, Nucleic Acids Research, vol.35, issue.Database, pp.137-140, 2007.
DOI : 10.1093/nar/gkl1041
URL : http://doi.org/10.1093/nar/gkl1041

J. Venter, M. Adams, E. Myers, P. Li, R. Mural et al., The Sequence of the Human Genome, Science, vol.291, issue.5507, pp.1304-1351, 2001.
DOI : 10.1126/science.1058040
URL : https://hal.archives-ouvertes.fr/hal-00465088

E. Shmelkov, Z. Tang, I. Aifantis, and A. Statnikov, Assessing quality and completeness of human transcriptional regulatory pathways on a genome-wide scale, Biology Direct, vol.6, issue.1, pp.6-15, 2011.
DOI : 10.1038/nri2304

M. Rakhshandehroo, B. Knoch, M. Müller, and S. Kersten, Peroxisome Proliferator-Activated Receptor Alpha Target Genes, PPAR Research, vol.22, issue.12, 2010.
DOI : 10.1016/0378-4274(96)03371-1
URL : http://doi.org/10.1155/2010/612089

S. Hummasti and P. Tontonoz, The Peroxisome Proliferator-Activated Receptor N-Terminal Domain Controls Isotype-Selective Gene Expression and Adipogenesis, Molecular Endocrinology, vol.20, issue.6, pp.1261-1275, 2006.
DOI : 10.1210/me.2006-0025

K. Schoonjans, J. Peinado-onsurbe, A. Lefebvre, R. Heyman, M. Briggs et al., PPARalpha and PPARgamma activators direct a distinct tissue-specific transcriptional response via a PPRE in the lipoprotein lipase gene, EMBO J, vol.15, pp.5336-5348, 1996.

O. Ziouzenkova and J. Plutzky, Retinoid metabolism and nuclear receptor responses: New insights into coordinated regulation of the PPAR-RXR complex, FEBS Letters, vol.277, issue.1, pp.32-38, 2008.
DOI : 10.1016/j.febslet.2007.11.081

P. Sertznig, M. Seifert, W. Tilgen, and J. Reichrath, in melanoma cell lines and other skin-derived cell lines, Dermato-Endocrinology, vol.63, issue.4, pp.232-238, 2009.
DOI : 10.1158/1078-0432.CCR-05-2556

B. Forman, J. Chen, and R. Evans, Hypolipidemic drugs, polyunsaturated fatty acids, and eicosanoids are ligands for peroxisome proliferator-activated receptors ?? and ??, Proceedings of the National Academy of Sciences, vol.94, issue.9, p.4312, 1997.
DOI : 10.1073/pnas.94.9.4312
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC20719

A. Sessler and J. Ntambi, Polyunsaturated fatty acid regulation of gene expression, J Nutr, vol.128, pp.923-926, 1998.

A. Yessoufou, J. Atègbo, E. Attakpa, A. Hichami, K. Moutairou et al., Peroxisome proliferator-activated receptor-?? modulates insulin gene transcription factors and inflammation in adipose tissues in mice, Molecular and Cellular Biochemistry, vol.170, issue.2, pp.101-111, 2009.
DOI : 10.1007/s11010-008-9968-1

X. Jiao, B. Sherman, D. Huang, R. Stephens, M. Baseler et al., DAVID-WS: a stateful web service to facilitate gene/protein list analysis, Bioinformatics, vol.28, issue.13, pp.1805-1806, 2012.
DOI : 10.1093/bioinformatics/bts251
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3381967

K. Volcik, J. Nettleton, C. Ballantyne, and E. Boerwinkle, Peroxisome proliferator?activated receptor ? genetic variation interacts with n?6 and long-chain n?3 fatty acid intake to affect total cholesterol and LDL-cholesterol concentrations in the atherosclerosis risk in communities study, Am J Clin Nutr, vol.87, pp.1926-1931, 2008.

R. Kanaan and L. Kanaan, Transforming growth factor beta1, bone connection, Med Sci Monit, vol.12, pp.164-169, 2006.

G. Leonarduzzi, A. Sevanian, B. Sottero, M. Arkan, F. Biasi et al., Up-regulation of the fibrogenic cytokine TGF-beta1 by oxysterols: a mechanistic link between cholesterol and atherosclerosis, FASEB J, vol.15, pp.1619-1621, 2001.

A. Scheepers, H. Joost, and A. Schürmann, The glucose transporter families SGLT and GLUT: molecular basis of normal and aberrant function, Journal of Parenteral and Enteral Nutrition, vol.28, issue.5
DOI : 10.1177/0148607104028005364

M. Sanna, C. Da-silva, O. Ducrey, J. Lee, K. Nomoto et al., IAP Suppression of Apoptosis Involves Distinct Mechanisms: the TAK1/JNK1 Signaling Cascade and Caspase Inhibition, Molecular and Cellular Biology, vol.22, issue.6, pp.1754-1766, 2002.
DOI : 10.1128/MCB.22.6.1754-1766.2002

B. Belgardt, J. Mauer, and J. Brüning, Novel roles for JNK1 in metabolism, Aging, vol.2, issue.9, pp.621-626, 2010.
DOI : 10.18632/aging.100192

D. Scheuner, B. Song, E. Mcewen, C. Liu, R. Laybutt et al., Translational Control Is Required for the Unfolded Protein Response and In Vivo Glucose Homeostasis, Molecular Cell, vol.7, issue.6, pp.1165-1176, 2001.
DOI : 10.1016/S1097-2765(01)00265-9

G. Raciti, C. Iadicicco, L. Ulianich, B. Vind, M. Gaster et al., Glucosamine-induced endoplasmic reticulum stress affects GLUT4 expression via activating transcription factor 6 in rat and human skeletal muscle cells, Diabetologia, vol.283, issue.5, pp.955-965, 2010.
DOI : 10.1007/s00125-010-1676-1

J. Small and D. Fell, Metabolic control analysis. Sensitivity of control coefficients to elasticities, European Journal of Biochemistry, vol.191, issue.2, pp.413-420, 1990.
DOI : 10.1016/0025-5564(87)90008-3

. Cornish-bowden, A: Fundamentals of enzyme kinetics, p.3, 1995.