C. Ansótegui, M. Bonet, and J. Levy, SAT-based MaxSAT algorithms, Artificial Intelligence, vol.196, pp.77-105, 2013.
DOI : 10.1016/j.artint.2013.01.002

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

S. Becker, A. Feist, M. Mo, G. Hannum, B. Palsson et al., Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox, Nature Protocols, vol.14, issue.3, pp.727-738, 2007.
DOI : 10.1038/nprot.2007.99

G. Collet, D. Eveillard, M. Gebser, S. Prigent, T. Schaub et al., Extending the Metabolic Network of Ectocarpus??Siliculosus Using Answer Set Programming, Proceedings LPNMR, pp.245-256, 2013.
DOI : 10.1007/978-3-642-40564-8_25
URL : https://hal.archives-ouvertes.fr/hal-00853752

G. Dantzig, Linear Programming and Extensions, 1963.
DOI : 10.1515/9781400884179

A. Ebrahim, J. Lerman, B. Palsson, and D. Hyduke, COBRApy: COnstraints-Based Reconstruction and Analysis for Python, BMC Systems Biology, vol.7, issue.1, p.74, 2013.
DOI : 10.1093/bioinformatics/btr361
URL : http://doi.org/10.1186/1752-0509-7-74

M. Gebser, R. Kaminski, B. Kaufmann, M. Ostrowski, T. Schaub et al., Theory solving made easy with clingo 5, pp.1-2, 2016.

M. Gelfond and V. Lifschitz, Classical negation in logic programs and disjunctive databases, New Generation Computing, vol.38, issue.No. 3, pp.365-385, 1991.
DOI : 10.1145/116825.116838

T. Handorf, O. Ebenhöh, and R. Heinrich, Expanding Metabolic Networks: Scopes of Compounds, Robustness, and Evolution, Journal of Molecular Evolution, vol.268, issue.4, pp.498-512, 2005.
DOI : 10.1007/978-1-4613-1161-4

M. Latendresse, Efficiently gap-filling reaction networks, BMC Bioinformatics, vol.15, issue.1, p.225, 2014.
DOI : 10.1186/1471-2105-15-225
URL : http://doi.org/10.1186/1471-2105-15-225

C. Maranas and A. Zomorrodi, Optimiz. methods in metabolic networks, 2016.

J. Orth and B. Palsson, Systematizing the generation of missing metabolic knowledge, Biotechnology and Bioengineering, vol.98, issue.14, pp.403-415, 2010.
DOI : 10.1128/jb.179.23.7581-7586.1997

M. Ostrowski and T. Schaub, ASP modulo CSP: The clingcon system. Theory and Practice of Logic Programming, pp.485-503, 2012.

S. Prigent, G. Collet, S. Dittami, L. Delage, F. Ethis-de-corny et al., (EctoGEM): a resource to study brown algal physiology and beyond, The Plant Journal, vol.4, issue.Suppl 4, pp.367-381, 2014.
DOI : 10.1038/ncomms3833
URL : https://hal.archives-ouvertes.fr/hal-01057153

S. Prigent, C. Frioux, S. Dittami, S. Thiele, A. Larhlimi et al., Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks, PLOS Computational Biology, vol.7, issue.7, p.1005276, 2017.
DOI : 10.1371/journal.pcbi.1005276.s005
URL : https://hal.archives-ouvertes.fr/hal-01449100

J. Reed, T. Vo, C. Schilling, B. Palsson, and G. Gpr-), An expanded genome-scale model of Escherichia coli K-12, Genome Biology, vol.4, issue.9, p.54, 2003.
DOI : 10.1186/gb-2003-4-9-r54

V. S. Kumar, M. Dasika, and C. Maranas, Optimization based automated curation of metabolic reconstructions, BMC Bioinformatics, vol.8, issue.1, p.212, 2007.
DOI : 10.1186/1471-2105-8-212

T. Schaub and S. Thiele, Metabolic network expansion with ASP, Proceedings ICLP, pp.312-326, 2009.
DOI : 10.1007/978-3-642-02846-5_27
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.148.2771

P. Simons, I. Niemelä, and T. Soininen, Extending and implementing the stable model semantics, Artificial Intelligence, vol.138, issue.1-2, pp.181-234, 2002.
DOI : 10.1016/S0004-3702(02)00187-X
URL : http://doi.org/10.1016/s0004-3702(02)00187-x

I. Thiele, N. Vlassis, and R. Fleming, fastGapFill: efficient gap filling in metabolic networks, Bioinformatics, vol.30, issue.17, pp.2529-2531, 2014.
DOI : 10.1093/bioinformatics/btu321
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4147887

E. Vitkin and T. Shlomi, MIRAGE: a functional genomics-based approach for metabolic network model reconstruction and its application to cyanobacteria networks, Genome Biology, vol.13, issue.11, p.111, 2012.
DOI : 10.1529/biophysj.106.093138