C. Baral, Knowledge Representation, Reasoning and Declarative Problem Solving, 2003.

S. Chevalier, C. Froidevaux, L. Paulevé, and A. Zinovyev, Synthesis of Boolean Networks from Biological Dynamical Constraints using Answer-Set Programming, 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (IC-TAI), pp.34-41, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02276921

M. A. Clarke and J. Fisher, Executable cancer models: successes and challenges. Nature Reviews Cancer, 2020.

D. P. Cohen, L. Martignetti, S. Robine, E. Barillot, A. Zinovyev et al., Mathematical modelling of molecular pathways enabling tumour cell invasion and migration, PLoS Comput Biol, vol.11, issue.11, p.1004571, 2015.
URL : https://hal.archives-ouvertes.fr/inserm-02141625

S. Collombet, C. Van-oevelen, J. L. Sardina-ortega, W. Abou-jaoudé, B. Di-stefano et al., Logical modeling of lymphoid and myeloid cell specification and transdifferentiation. Proceedings of the National Academy of, Sciences, vol.114, issue.23, pp.5792-5799, 2017.

F. Corblin, S. Tripodi, E. Fanchon, D. Ropers, and L. Trilling, A declarative constraint-based method for analyzing discrete genetic regulatory networks, Biosystems, vol.98, issue.2, pp.91-104, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00793012

,

J. Dorier, I. Crespo, A. Niknejad, R. Liechti, M. Ebeling et al., Boolean regulatory network reconstruction using literature based knowledge with a genetic algorithm optimization method, BMC Bioinformatics, vol.17, issue.1, p.410, 2016.

T. Eiter and G. Gottlob, On the computational cost of disjunctive logic programming: Propositional case, Annals of Mathematics and Artificial Intelligence, vol.15, issue.3, pp.289-323, 1995.

T. Eiter, G. Ianni, and T. Krennwallner, Answer Set Programming: A Primer, pp.40-110, 2009.

M. Gebser, R. Kaminski, B. Kaufmann, and T. Schaub, Answer Set Solving in Practice, Synthesis Lectures on Artificial Intelligence and Machine Learning, 2012.

M. Gebser, R. Kaminski, B. Kaufmann, and T. Schaub, Clingo = ASP + control: Preliminary report, 2014.

D. T. Gillespie, A general method for numerically simulating the stochastic time evolution of coupled chemical reactions, Journal of computational physics, vol.22, issue.4, pp.90041-90044, 1976.

J. Goldfeder and H. Kugler, BRE:IN -a backend for reasoning about interaction networks with temporal logic, Computational Methods in Systems Biology, pp.289-295, 2019.

S. Kauffman, A proposal for using the ensemble approach to understand genetic regulatory networks, Journal of Theoretical Biology, vol.230, issue.4, pp.581-590, 2004.

H. Klarner, A. Bockmayr, and H. Siebert, Computing maximal and minimal trap spaces of boolean networks, Natural Computing, vol.14, issue.4, pp.535-544, 2015.

P. Krawitz and I. Shmulevich, Basin entropy in boolean network ensembles, Physical Review Letters, vol.98, issue.15, 2007.

F. Lin and Y. Zhao, ASSAT: Computing answer sets of a logic program by SAT solvers, Artificial Intelligence, vol.157, issue.1, pp.115-137, 2004.

,

J. Lobo, J. Minker, and A. Rajasekar, Foundations of disjunctive logic programming, 1992.

L. Paulevé, J. Kol?ák, T. Chatain, and S. Haar, Reconciling qualitative, abstract, and scalable modeling of biological networks, 2020.

M. Razzaq, R. Kaminski, J. Romero, T. Schaub, J. Bourdon et al., Computing diverse boolean networks from phosphoproteomic time series data, Computational Methods in Systems Biology, pp.59-74, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01988432

R. Schwieger and H. Siebert, Graph representations of monotonic boolean model pools, pp.233-248, 2017.

G. Stoll, B. Caron, E. Viara, A. Dugourd, A. Zinovyev et al., MaBoSS 2.0: an environment for stochastic boolean modeling, Bioinformatics, vol.33, issue.14, pp.2226-2228, 2017.

G. Stoll, E. Viara, E. Barillot, and L. Calzone, Continuous time boolean modeling for biological signaling: application of gillespie algorithm, BMC systems biology, vol.6, issue.1, p.116, 2012.
URL : https://hal.archives-ouvertes.fr/inserm-00762304

C. Terfve, T. Cokelaer, D. Henriques, A. Macnamara, E. Goncalves et al., CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms, BMC Systems Biology, vol.6, issue.1, p.133, 2012.

N. G. Van-kampen, Stochastic processes in physics and chemistry, vol.1, 1992.

S. Wold, K. Esbensen, and P. Geladi, Principal component analysis. Chemometrics and intelligent laboratory systems, vol.2, pp.37-52, 1987.

, , vol.87, pp.80084-80093

B. Yordanov, S. J. Dunn, H. Kugler, A. Smith, G. Martello et al., A method to identify and analyze biological programs through automated reasoning, Systems Biology and Applications, vol.2, 2016.

,

J. G. Zañudo, S. N. Steinway, and R. Albert, Discrete dynamic network modeling of oncogenic signaling: Mechanistic insights for personalized treatment of cancer, Current Opinion in Systems Biology, vol.9, pp.1-10, 2018.

,