L. G. Alexopoulos, J. Saez-rodriguez, B. Cosgrove, D. A. Lauffenburger, and P. Sorger, Networks Inferred from Biochemical Data Reveal Profound Differences in Toll-like Receptor and Inflammatory Signaling between Normal and Transformed Hepatocytes, Molecular & Cellular Proteomics, vol.9, issue.9, pp.1849-1865, 2010.
DOI : 10.1074/mcp.M110.000406

S. Kauffman, Metabolic stability and epigenesis in randomly constructed genetic nets, Journal of Theoretical Biology, vol.22, issue.3, pp.437-467, 1969.
DOI : 10.1016/0022-5193(69)90015-0

R. Wang, A. Saadatpour, and R. Albert, Boolean modeling in systems biology: an overview of methodology and applications, Physical Biology, vol.9, issue.5
DOI : 10.1088/1478-3975/9/5/055001

N. Berestovsky and L. Nakhleh, An Evaluation of Methods for Inferring Boolean Networks from Time-Series Data, PLoS ONE, vol.22, issue.6, p.66031, 2013.
DOI : 10.1371/journal.pone.0066031.t002

A. Macnamara, C. Terfve, D. Henriques, B. Bernabe, and J. Saez-rodriguez, State???time spectrum of signal transduction logic models, Physical Biology, vol.9, issue.4
DOI : 10.1088/1478-3975/9/4/045003

M. Bansal, V. Belcastro, A. Ambesi-impiombato, and D. Di-bernardo, How to infer gene networks from expression profiles, Molecular Systems Biology, vol.3, p.78, 2007.

R. D. Smet and K. , Advantages and limitations of current network inference methods, Nature Reviews Microbiology, vol.35, issue.10, pp.717-729, 2010.
DOI : 10.1038/nrmicro2419

S. Maetschke, P. Madhamshettiwar, M. J. Davis, and M. Ragan, Supervised, semi-supervised and unsupervised inference of gene regulatory networks, Briefings in Bioinformatics, vol.15, issue.2, pp.195-211, 2014.
DOI : 10.1093/bib/bbt034

H. Busch, D. Camacho-trullio, Z. Rogon, K. Breuhahn, P. Angel et al., Gene network dynamics controlling keratinocyte migration, Molecular Systems Biology, vol.1115, issue.1, 0199.
DOI : 10.1210/en.2007-0361

R. Porreca, E. Cinquemani, J. Lygerosn, and G. Ferrari-trecate, Identification of genetic network dynamics with unate structure, Bioinformatics, vol.26, issue.9, pp.1239-1245, 2010.
DOI : 10.1093/bioinformatics/btq120
URL : https://hal.archives-ouvertes.fr/hal-00793025

S. M. Hill, L. M. Heiser, T. Cokelaer, M. Unger, N. K. Nesser et al., Inferring causal molecular networks: empirical assessment through a community-based effort, Nature Methods, vol.4, issue.4, pp.310-318, 2016.
DOI : 10.1038/nmeth.3773

M. Ostrowski, L. Paulevé, T. Schaub, A. Siegel, and C. Guziolowski, Boolean Network Identification from Multiplex Time Series Data, CMSB 2015 -13th conference on Computational Methods for Systems Biology, pp.170-181, 2015.
DOI : 10.1007/978-3-319-23401-4_15
URL : https://hal.archives-ouvertes.fr/hal-01164751

J. Aracena, E. Goles, A. Moreira, and L. Salinas, On the robustness of update schedules in Boolean networks, Biosystems, vol.97, issue.1, pp.1-8, 2009.
DOI : 10.1016/j.biosystems.2009.03.006

A. Cheng, J. Esparza, and J. Palsberg, Complexity results for 1-safe nets, Theoretical Computer Science, vol.147, issue.1-2, pp.117-136, 1995.
DOI : 10.1016/0304-3975(94)00231-7

C. Baral, Knowledge Representation, Reasoning and Declarative Problem Solving, 2003.
DOI : 10.1017/CBO9780511543357

M. Gebser, R. Kaminski, B. Kaufmann, and T. Schaub, Answer Set Solving in Practice, Synthesis Lectures on Artificial Intelligence and Machine Learning, vol.6, issue.3, 2012.
DOI : 10.2200/S00457ED1V01Y201211AIM019

M. Gebser, B. Kaufmann, and T. Schaub, Abstract, Theory and Practice of Logic Programming, vol.6, issue.4-5, pp.4-5
DOI : 10.1109/12.769433
URL : https://hal.archives-ouvertes.fr/hal-01186999

M. Gebser, B. Kaufmann, R. Otero, J. Romero, T. Schaub et al., Domain-specific heuristics in answer set programming, Proceedings of the 27th National Conference on Artificial Intelligence (AAAI'13), pp.350-356, 2013.

E. M. Clarke and E. A. Emerson, Design and synthesis of synchronization skeletons using branching-time temporal logic, in: Logic of Programs, pp.52-71, 1981.

J. Krumsiek, S. Polsterl, D. M. Wittmann, and F. J. Theis, Odefy -- From discrete to continuous models, BMC Bioinformatics, vol.11, issue.1, p.11, 2010.
DOI : 10.1186/1471-2105-11-233

S. Klamt, J. Saez-rodriguez, J. A. Lindquist, L. Simeoni, and E. Gilles, A methodology for the structural and functional analysis of signaling and regulatory networks, BMC Bioinformatics, vol.7, issue.56, 2006.

O. Sahin, H. Frohlich, C. Lobke, U. Korf, S. Burmester et al., Arlt, Modeling ERBB receptor-regulated G1/S transition to find novel targets for de novo trastuzumab resistance, BMC Systems Biology, vol.3, issue.1, 2009.

A. Cimatti, E. Clarke, E. Giunchiglia, F. Giunchiglia, M. Pistore et al., NuSMV 2: An OpenSource Tool for Symbolic Model Checking, Computer Aided Verification, pp.241-268, 2002.
DOI : 10.1007/3-540-45657-0_29

L. Paulevé, Goal-Oriented Reduction of Automata Networks, Computational Methods for Systems Biology Lecture Notes in Bioinformatics, vol.5, issue.8, 2016.
DOI : 10.1007/978-3-319-45177-0_16

C. Guziolowski, S. Videla, F. Eduati, S. Thiele, T. Cokelaer et al., Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming, Bioinformatics, vol.29, issue.18, pp.2320-2326, 2013.
DOI : 10.1093/bioinformatics/btt393
URL : https://hal.archives-ouvertes.fr/hal-00853704

J. Saez-rodriguez, L. G. Alexopoulos, J. Epperlein, R. Samaga, D. Lauffenburger et al., Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction, Molecular Systems Biology, vol.41, issue.331
DOI : 10.1038/msb.2009.87

H. Klarner, A. Streck, D. Safránek, J. Kol?ák, and H. Siebert, Parameter Identification and Model Ranking of Thomas Networks, Computational Methods in Systems Biology, pp.207-226
DOI : 10.1007/978-3-642-33636-2_13

E. Gallet, M. Manceny, P. L. Gall, and P. Ballarini, An LTL Model Checking Approach for Biological Parameter Inference, Formal Methods and Software Engineering, pp.155-170, 2014.
DOI : 10.1007/978-3-319-11737-9_11
URL : https://hal.archives-ouvertes.fr/hal-01110746