U. Alon, An Introduction to Systems Biology: Design Principles of Biological Circuits. London: Chapman and Hall/CRC, 2006.

L. Novere and N. , Quantitative and logic modelling of molecular and gene networks, Nat Rev Genet, vol.16, issue.3, p.146, 2015.

L. Calzone, E. Barillot, and A. Zinovyev, Logical versus kinetic modeling of biological networks: applications in cancer research, Curr Opin Cell Eng, vol.21, pp.22-31, 2018.

B. B. Aldridge, J. Saez-rodriguez, J. L. Muhlich, P. K. Sorger, and D. A. Lauffenburger, Fuzzy logic analysis of kinase pathway crosstalk in TNF/EGF/insulin-induced signaling, PLoS Comput Biol, vol.5, issue.4, p.1000340, 2009.

M. L. Wynn, N. Consul, S. D. Merajver, and S. Schnell, Logic-based models in systems biology: a predictive and parameter-free network analysis method, Integr Biol, vol.4, issue.11, pp.1323-1360, 2012.

M. K. Morris, J. Saez-rodriguez, P. K. Sorger, and D. A. Lauffenburger, Logic-based models for the analysis of cell signaling networks, Biochemistry, vol.49, issue.15, pp.3216-3240, 2010.

S. Kauffman, The large scale structure and dynamics of gene control circuits: an ensemble approach, J Theor Biol, vol.44, issue.1, pp.167-90, 1974.

S. A. Kauffman, Metabolic stability and epigenesis in randomly constructed genetic nets, J Theor Biol, vol.22, issue.3, pp.437-67, 1969.

S. Kauffman, Homeostasis and differentiation in random genetic control networks, Nature, vol.224, issue.5215, pp.177-185, 1969.

A. Naldi, C. Hernandez, W. Abou-jaoudé, P. T. Monteiro, C. Chaouiya et al., Logical modeling and analysis of cellular regulatory networks with ginsim 3.0, Front Physiol, vol.9, p.646, 2018.

A. G. Gonzalez, A. Naldi, L. Sanchez, D. Thieffry, and C. Chaouiya, GINsim: a software suite for the qualitative modelling, simulation and analysis of regulatory networks, Biosystems, vol.84, issue.2, pp.91-100, 2006.
URL : https://hal.archives-ouvertes.fr/hal-02263154

C. Müssel, M. Hopfensitz, and H. A. Kestler, BoolNet?an R package for generation, reconstruction and analysis of Boolean networks, Bioinformatics, vol.26, issue.10, pp.1378-80, 2010.

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

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.2226-2234, 2017.

D. T. Gillespie, Exact stochastic simulation of coupled chemical reactions, J Phys Chem, vol.81, issue.25, pp.2340-61, 1977.

C. V. Rao and A. P. Arkin, Stochastic chemical kinetics and the quasi-steady-state assumption: Application to the Gillespie algorithm, J Chem Phys, vol.118, issue.11, pp.4999-5010, 2003.

P. Érdi and J. Tóth, Mathematical Models of Chemical Reactions: Theory and Applications of Deterministic and Stochastic Models, 1989.

J. Béal, A. Montagud, P. Traynard, E. Barillot, and L. Calzone, Personalization of logical models with multi-omics data allows clinical stratification of patients, Front Physiol, vol.9, 1965.

Z. Zi, Sensitivity analysis approaches applied to systems biology models, IET Syst Biol, vol.5, issue.6, pp.336-382, 2011.

F. Fröhlich, B. Kaltenbacher, F. J. Theis, and J. Hasenauer, Scalable parameter estimation for genome-scale biochemical reaction networks, PLoS Comput Biol, vol.13, issue.1, p.1005331, 2017.

J. Gunawardena, A linear framework for time-scale separation in nonlinear biochemical systems, PloS ONE, vol.7, issue.5, p.36321, 2012.

I. Mirzaev and J. Gunawardena, Laplacian dynamics on general graphs, Bull Math Biol, vol.75, issue.11, pp.2118-2167, 2013.

M. Koltai and . Exastolog, , 2020.

W. Li, L. Cui, and M. K. Ng, On computation of the steady-state probability distribution of probabilistic Boolean networks with gene perturbation, J Comput Appl Math, vol.236, issue.16, pp.4067-81, 2012.

P. Trairatphisan, A. Mizera, J. Pang, A. A. Tantar, J. Schneider et al., Recent development and biomedical applications of probabilistic Boolean networks, Cell Commun Signal, vol.11, issue.1, p.46, 2013.

F. Fages and S. Soliman, From reaction models to influence graphs and back: a theorem, International Workshop on Formal Methods in Systems Biology, pp.90-102, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00419921

D. Bérenguier, C. Chaouiya, P. T. Monteiro, A. Naldi, R. E. Thieffry et al., Dynamical modeling and analysis of large cellular regulatory networks, Chaos: An Interdiscip J Nonlinear Sci, vol.23, issue.2, p.25114, 2013.

G. Stoll, J. Rougemont, and F. Naef, Few crucial links assure checkpoint efficiency in the yeast cell-cycle network, Bioinformatics, vol.22, issue.20, pp.2539-2585, 2006.

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.261-74, 2002.

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

O. Radulescu, A. N. Gorban, A. Zinovyev, and A. Lilienbaum, Robust simplifications of multiscale biochemical networks, BMC Syst Biol, vol.2, issue.1, p.86, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00331212

M. Brun, E. R. Dougherty, and I. Shmulevich, Steady-state probabilities for attractors in probabilistic Boolean networks. Signal Process, vol.85, pp.1993-2013, 2005.

S. Zhang, C. Ng, M. K. Akutsu, and T. , Simulation study in probabilistic Boolean network models for genetic regulatory networks, Int J Data Min Bioinforma, vol.1, issue.3, pp.217-257, 2007.

W. Ching, S. Zhang, M. K. Ng, and T. Akutsu, An approximation method for solving the steady-state probability distribution of probabilistic Boolean networks, Bioinformatics, vol.23, issue.12, pp.1511-1519, 2007.

M. W. Hirsch, R. L. Devaney, and S. Smale, Differential equations, dynamical systems, and linear algebra, vol.60, 1974.

A. B. Kahn, Topological sorting of large networks, Commun ACM, vol.5, issue.11, pp.558-62, 1962.

S. Dasgupta, C. H. Papadimitriou, and U. V. Vazirani, Algorithms, 2008.

P. Traynard, A. Fauré, F. Fages, and D. Thieffry, Logical model specification aided by model-checking techniques: application to the mammalian cell cycle regulation, Bioinformatics, vol.32, issue.17, pp.772-80, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01378465

J. Zañudo, M. Scaltriti, and R. Albert, A network modeling approach to elucidate drug resistance mechanisms and predict combinatorial drug treatments in breast cancer, Cancer Converg, vol.1, issue.1, p.5, 2017.

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

Ö. Sahin, H. Fröhlich, C. Löbke, U. Korf, S. Burmester et al., Modeling ERBB receptor-regulated G1/S transition to find novel targets for de novo trastuzumab resistance, BMC Syst Biol, vol.3, issue.1, p.1, 2009.

V. Noel and . Maboss-sampling, , 2020.

N. M. Santio, S. Landor, L. Vahtera, J. Ylä-pelto, E. Paloniemi et al., Phosphorylation of Notch1 by Pim kinases promotes oncogenic signaling in breast and prostate cancer cells, Oncotarget, vol.7, issue.28, p.43220, 2016.

P. G. Constantine and P. Diaz, Global sensitivity metrics from active subspaces, Reliab Eng Syst Saf, vol.162, pp.1-13, 2017.

M. Dorel, B. Klinger, T. Gross, A. Sieber, A. Prahallad et al., Modelling signalling networks from perturbation data, Bioinformatics, vol.34, issue.23, pp.4079-86, 2018.

B. Klinger, A. Sieber, R. Fritsche-guenther, F. Witzel, L. Berry et al., Network quantification of EGFR signaling unveils potential for targeted combination therapy, Mol Syst Biol, vol.9, issue.1, 2013.

M. Kwiatkowska, G. Norman, and D. Parker, PRISM: probabilistic model checking for performance and reliability analysis, ACM SIGMETRICS Perform Eval Rev, vol.36, issue.4, pp.40-45, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00457906

M. Kwiatkowska, G. Norman, and D. Parker, Symmetry reduction for probabilistic model checking, International Conference on Computer Aided Verification, pp.234-282, 2006.

A. N. Gorban and O. Radulescu, Dynamic and static limitation in multiscale reaction networks, revisited, Adv Chem Eng, vol.34, pp.103-73, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00184976

A. N. Gorban, O. Radulescu, and A. Y. Zinovyev, Asymptotology of chemical reaction networks, Chem Eng Sci, vol.65, issue.7, pp.2310-2334, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00431225

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