T. Akutsu, S. Kuhara, O. Maruyama, and S. Miyano, Identification of genetic networks by strategic gene disruptions and gene overexpressions under a boolean model, Theoretical Computer Science, vol.298, issue.1, pp.235-251, 2003.
DOI : 10.1016/S0304-3975(02)00425-5

T. Akutsu, T. Tamura, and K. Horimoto, Completing Networks Using Observed Data, Algorithmic Learning Theory, pp.126-140, 2009.
DOI : 10.1016/j.bbrc.2009.02.076

K. R. Apt and M. H. Van-emden, Contributions to the Theory of Logic Programming, Journal of the ACM, vol.29, issue.3, pp.841-862, 1982.
DOI : 10.1145/322326.322339

J. Banga, Optimization in computational systems biology, BMC Systems Biology, vol.2, issue.1, p.47, 2008.
DOI : 10.1186/1752-0509-2-47

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

C. Baral, K. Chancellor, N. Tran, N. Tran, A. Joy et al., A knowledge based approach for representing and reasoning about signaling networks, Proceedings of the Twelfth International Conference on Intelligent Systems for Molecular Biology/Third European Conference on Computational Biology, pp.15-22, 2004.
DOI : 10.1093/bioinformatics/bth918

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

B. Bollobás, Combinatorics: Set Systems, Hypergraphs, Families of Vectors, and Combinatorial Probability, 1986.

L. Calzone, N. Chabrier-rivier, F. Fages, and S. Soliman, Machine Learning Biochemical Networks from Temporal Logic Properties, Transactions on Computational Systems Biology VI. Lecture Notes in Computer Science, vol.4220, pp.68-94, 2006.
DOI : 10.1007/11880646_4

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

G. Collet, D. Eveillard, M. Gebser, S. Prigent, T. Schaub et al., Extending the Metabolic Network of Ectocarpus??Siliculosus Using Answer Set Programming, Proceedings of the Twelfth International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR'13 of Lecture Notes in Artificial Intelligence, pp.245-256, 2013.
DOI : 10.1007/978-3-642-40564-8_25

URL : https://hal.archives-ouvertes.fr/hal-00853752

F. Corblin, E. Fanchon, and L. Trilling, Applications of a formal approach to decipher discrete genetic networks, BMC Bioinformatics, vol.11, issue.1, p.385, 2010.
DOI : 10.1186/1471-2105-11-385

F. Corblin, E. Fanchon, L. Trilling, C. Chaouiya, and D. Thieffry, Automatic Inference of Regulatory and Dynamical Properties from Incomplete Gene Interaction and Expression Data, of Lecture Notes in Computer Science, pp.25-30, 2012.
DOI : 10.1007/978-3-642-28792-3_4

M. Durzinsky, W. Marwan, M. Ostrowski, T. Schaub, and A. Wagler, Abstract, Theory and Practice of Logic Programming, vol.3, issue.4-5, pp.749-766, 2011.
DOI : 10.1017/S1471068411000287

T. Fayruzov, M. De-cock, C. Cornelis, and D. Vermeir, Modeling Protein Interaction Networks with Answer Set Programming, 2009 IEEE International Conference on Bioinformatics and Biomedicine, pp.99-104, 2009.
DOI : 10.1109/BIBM.2009.9

T. Fayruzov, J. Janssen, D. Vermeir, C. Cornelis, and M. D. Cock, Modelling gene and protein regulatory networks with Answer Set Programming, International Journal of Data Mining and Bioinformatics, vol.5, issue.2, pp.209-229, 2011.
DOI : 10.1504/IJDMB.2011.039178

M. Folschette, L. Paulevé, K. Inoue, M. Magnin, and O. Roux, Concretizing the Process Hitting into Biological Regulatory Networks, Computational Methods in Systems Biology, pp.166-186, 2012.
DOI : 10.1007/978-3-642-33636-2_11

URL : https://hal.archives-ouvertes.fr/hal-00704569

A. A. Freitas, A critical review of multi-objective optimization in data mining, ACM SIGKDD Explorations Newsletter, vol.6, issue.2, p.77, 2004.
DOI : 10.1145/1046456.1046467

G. Gallo, G. Longo, S. Pallottino, and S. Nguyen, Directed hypergraphs and applications, Discrete Applied Mathematics, vol.42, issue.2-3, pp.177-201, 1993.
DOI : 10.1016/0166-218X(93)90045-P

M. Gebser, C. Guziolowski, M. Ivanchev, T. Schaub, A. Siegel et al., Repair and prediction (under inconsistency) in large biological networks with answer set programming, Proceedings of the Twelfth International Conference on Principles of Knowledge Representation and Reasoning (KR'10, pp.497-507, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00538137

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, R. Kaminski, M. Ostrowski, T. Schaub, and S. Thiele, On the Input Language of ASP Grounder Gringo, Proceedings of the Tenth International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR'09 of Lecture Notes in Artificial Intelligence, pp.502-508, 2009.
DOI : 10.1007/978-3-642-04238-6_49

M. Gebser, R. Kaminski, and T. Schaub, Abstract, Theory and Practice of Logic Programming, vol.10, issue.4-5, pp.4-5, 2011.
DOI : 10.1017/S1471068411000329

URL : https://hal.archives-ouvertes.fr/hal-01186999

M. Gebser, B. Kaufmann, A. Neumann, and T. Schaub, clasp: A Conflict-Driven Answer Set Solver, Proceedings of the Ninth International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR'07 of Lecture Notes in Artificial Intelligence, pp.260-265, 2007.
DOI : 10.1007/978-3-540-72200-7_23

M. Gebser, B. Kaufmann, and T. Schaub, Abstract, Theory and Practice of Logic Programming, vol.6, issue.4-5, pp.4-5, 2012.
DOI : 10.1109/12.769433

URL : https://hal.archives-ouvertes.fr/hal-01186999

M. Gebser, T. Schaub, S. Thiele, and P. Veber, Abstract, Theory and Practice of Logic Programming, vol.1771, issue.2-3, pp.323-360, 2011.
DOI : 10.1017/S1471068410000554

URL : https://hal.archives-ouvertes.fr/hal-01186999

M. Gelfond and V. Lifschitz, The stable model semantics for logic programming, Proceedings of the Fifth International Conference and Symposium of Logic Programming (ICLP'88, pp.1070-1080, 1988.

C. Guziolowski, A. Kittas, F. Dittmann, and N. Grabe, Automatic generation of causal networks linking growth factor stimuli to functional cell state changes, FEBS Journal, vol.84, issue.Database issue, pp.3462-3474, 2012.
DOI : 10.1111/j.1742-4658.2012.08616.x

URL : https://hal.archives-ouvertes.fr/hal-00915016

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, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00853704

J. Handl, D. B. Kell, and J. Knowles, Multiobjective Optimization in Bioinformatics and Computational Biology, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.4, issue.2, pp.279-292, 2007.
DOI : 10.1109/TCBB.2007.070203

T. E. Ideker, V. Thorsson, and R. M. Karp, DISCOVERY OF REGULATORY INTERACTIONS THROUGH PERTURBATION: INFERENCE AND EXPERIMENTAL DESIGN, Biocomputing 2000, pp.305-316, 2000.
DOI : 10.1142/9789814447331_0029

K. Inoue, Logic programming for boolean networks, Proceedings of the Twenty-second International Joint Conference on Artificial Intelligence (IJCAI'11). IJCAI/AAAI, pp.924-930, 2011.

R. Kaminski, T. Schaub, A. Siegel, and S. Videla, Minimal intervention strategies in logical signaling networks with answer set programming, Theory and Practice of Logic Programming, vol.13, pp.4-5, 2013.

M. Kanehisa, S. Goto, M. Furumichi, M. Tanabe, and M. Hirakawa, KEGG for representation and analysis of molecular networks involving diseases and drugs, Nucleic Acids Research, vol.38, issue.Database, pp.355-60, 2010.
DOI : 10.1093/nar/gkp896

S. Kauffman and . Feb, 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

S. Klamt, U. Haus, and F. J. Theis, Hypergraphs and Cellular Networks, PLoS Computational Biology, vol.9, issue.5, p.1000385, 2009.
DOI : 10.1371/journal.pcbi.1000385.g002

S. Klamt, J. Saez-rodriguez, J. Lindquist, L. Simeoni, and E. Gilles, A methodology for the structural and functional analysis of signaling and regulatory networks, BMC Bioinformatics, vol.7, issue.1, p.56, 2006.
DOI : 10.1186/1471-2105-7-56

A. Macnamara, C. Terfve, D. Henriques, B. P. Bernabé, and J. Saez-rodriguez, State???time spectrum of signal transduction logic models, Physical Biology, vol.9, issue.4, p.45003, 2012.
DOI : 10.1088/1478-3975/9/4/045003

R. T. Marler, S. Arora, and J. , Survey of multi-objective optimization methods for engineering, Structural and Multidisciplinary Optimization, vol.26, issue.6, pp.369-395, 2004.
DOI : 10.1007/s00158-003-0368-6

J. Mccluskey and E. J. , Minimization of Boolean Functions*, Bell System Technical Journal, vol.35, issue.6, 1956.
DOI : 10.1002/j.1538-7305.1956.tb03835.x

A. Mitsos, I. Melas, P. Siminelakis, A. Chairakaki, J. Saez-rodriguez et al., Identifying Drug Effects via Pathway Alterations using an Integer Linear Programming Optimization Formulation on Phosphoproteomic Data, PLoS Computational Biology, vol.13, issue.12, p.1000591, 2009.
DOI : 10.1371/journal.pcbi.1000591.s004

M. Morris, J. Saez-rodriguez, P. Sorger, and D. A. Lauffenburger, Logic-Based Models for the Analysis of Cell Signaling Networks, Biochemistry, vol.49, issue.15, pp.3216-3224, 2010.
DOI : 10.1021/bi902202q

I. Papatheodorou, M. Ziehm, D. Wieser, N. Alic, L. Partridge et al., Using Answer Set Programming to Integrate RNA Expression with Signalling Pathway Information to Infer How Mutations Affect Ageing, PLoS ONE, vol.5, issue.12, p.50881, 2012.
DOI : 10.1371/journal.pone.0050881.s005

L. Paulevé and A. Richard, Static Analysis of Boolean Networks Based on Interaction Graphs: A Survey, Electronic Notes in Theoretical Computer Science, vol.284, pp.93-104, 2012.
DOI : 10.1016/j.entcs.2012.05.017

R. J. Prill, J. Saez-rodriguez, L. G. Alexopoulos, P. K. Sorger, and G. Stolovitzky, Crowdsourcing Network Inference: The DREAM Predictive Signaling Network Challenge, Science Signaling, vol.4, issue.189, p.7, 2011.
DOI : 10.1126/scisignal.2002212

O. Ray and T. Soh, Analyzing Pathways Using ASP-Based Approaches, Algebraic and Numeric Biology, 2012.
DOI : 10.1007/978-3-642-28067-2_10

O. Ray, K. Whelan, and R. King, Logic-Based Steady-State Analysis and Revision of Metabolic Networks with Inhibition, 2010 International Conference on Complex, Intelligent and Software Intensive Systems, pp.661-666, 2010.
DOI : 10.1109/CISIS.2010.184

E. Remy, P. Ruet, and D. Thieffry, Graphic requirements for multistability and attractive cycles in a Boolean dynamical framework, Advances in Applied Mathematics, vol.41, issue.3, pp.335-350, 2008.
DOI : 10.1016/j.aam.2007.11.003

URL : https://hal.archives-ouvertes.fr/hal-00692086

S. Liang, S. F. Somogyi, and R. , REVEAL, A General Reverse Engineering Algorithm for Inference of GeneticNetwork Architectures, Pacific Symposium on Biocomputing 3, pp.19-29, 1998.

A. Saadatpour and R. Albert, Boolean modeling of biological regulatory networks: A methodology tutorial, Methods, vol.62, issue.1, 2012.
DOI : 10.1016/j.ymeth.2012.10.012

J. Saez-rodriguez, L. G. Alexopoulos, J. Epperlein, R. Samaga, D. A. 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, 2009.
DOI : 10.1038/msb.2009.87

C. F. Schaefer, K. Anthony, S. Krupa, J. Buchoff, M. Day et al., PID: the Pathway Interaction Database, Nucleic Acids Research, vol.37, issue.Database, pp.674-679, 2009.
DOI : 10.1093/nar/gkn653

T. Schaub and S. Thiele, Metabolic Network Expansion with Answer Set Programming, Proceedings of the Twenty-fifth International Conference on Logic Programming (ICLP'09, pp.312-326, 2009.
DOI : 10.1007/s10601-007-9031-y

R. Sharan and R. M. Karp, Reconstructing Boolean Models of Signaling, Research in Computational Molecular Biology, pp.261-271, 2012.

I. Shmulevich, E. R. Dougherty, S. Kim, and W. Zhang, Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks, Bioinformatics, vol.18, issue.2, pp.261-274, 2002.
DOI : 10.1093/bioinformatics/18.2.261

I. Shmulevich, A. Saarinen, O. Yli-harja, and J. Astola, Inference of Genetic Regulatory Networks Via Best-Fit Extensions, Computational and Statistical Approaches to Genomics, pp.197-210, 2003.
DOI : 10.1007/0-306-47825-0_11

C. D. Terfve, T. Cokelaer, D. Henriques, A. Macnamara, E. Gonçalves et al., CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms, BMC Systems Biology, vol.6, issue.1, pp.133-133, 2012.
DOI : 10.1186/1752-0509-6-133

R. Thomas, Regulatory networks seen as asynchronous automata: A logical description, Journal of Theoretical Biology, vol.153, issue.1, pp.1-23, 1991.
DOI : 10.1016/S0022-5193(05)80350-9

R. R. Thomas and . Nov, Boolean formalization of genetic control circuits, Journal of Theoretical Biology, vol.42, issue.3, pp.563-585, 1973.
DOI : 10.1016/0022-5193(73)90247-6

S. Videla, C. Guziolowski, F. Eduati, S. Thiele, N. Grabe et al., Revisiting the Training of Logic Models of Protein Signaling Networks with ASP, Computational Methods in Systems Biology, pp.342-361, 2012.
DOI : 10.1007/978-3-642-33636-2_20

R. R. Wang, A. A. Saadatpour, and R. R. Albert, Boolean modeling in systems biology: an overview of methodology and applications, Physical Biology, vol.9, issue.5, 2012.
DOI : 10.1088/1478-3975/9/5/055001