G. D. Bader, M. P. Cary, and C. Sander, Pathguide: a Pathway Resource List, Nucleic Acids Research, vol.34, issue.90001, pp.505-505, 2006.
DOI : 10.1093/nar/gkj126

G. D. Bader and C. W. Hogue, BIND--a data specification for storing and describing biomolecular interactions, molecular complexes and pathways, Bioinformatics, vol.16, issue.5, pp.465-477, 2000.
DOI : 10.1093/bioinformatics/16.5.465

K. Ball, T. G. Kurtz, L. Popovic, and G. Rempala, Asymptotic analysis of multiscale approximations to reaction networks, The Annals of Applied Probability, vol.16, issue.4, pp.1925-1961, 2006.
DOI : 10.1214/105051606000000420

G. Batt, M. Page, I. Cantone, G. Goessler, P. Monteiro et al., Efficient parameter search for qualitative models of regulatory networks using symbolic model checking, Bioinformatics, vol.26, issue.18, pp.26603-610, 2010.
DOI : 10.1093/bioinformatics/btq387
URL : https://hal.archives-ouvertes.fr/inria-00482569

S. A. Becker, A. M. Feist, M. L. Mo, G. Hannum, B. O. Palson 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

S. Berthoumieux, M. Brilli, H. De-jong, D. Kahn, and E. Cinquemani, Identification of metabolic network models from incomplete high-throughput datasets, Bioinformatics, vol.27, issue.13, pp.17186-195, 2011.
DOI : 10.1093/bioinformatics/btr225
URL : https://hal.archives-ouvertes.fr/hal-00793039

J. Bruck and M. Gibson, Efficient exact stochastic simulation of chemical systems with many species and many channels, J. Phys. Chem, vol.105, pp.1876-1889, 2000.

Y. Cao, L. R. Petzold, M. Rathinam, and D. T. Gillespie, The numerical stability of leaping methods for stochastic simulation of chemically reacting systems, The Journal of Chemical Physics, vol.121, issue.24, pp.12169-12178, 2004.
DOI : 10.1063/1.1823412

M. P. Cary, G. D. Bader, and C. Sander, Pathway information for systems biology, FEBS Letters, vol.2004, issue.8, pp.1815-1820, 2005.
DOI : 10.1016/j.febslet.2005.02.005

R. Caspi, The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases, Nucleic Acids Research, vol.38, issue.Database, pp.473-479, 2010.
DOI : 10.1093/nar/gkp875

W. W. Chen, M. Niepel, and P. K. Sorger, Classic and contemporary approaches to modeling biochemical reactions, Genes & Development, vol.24, issue.17, pp.1861-1875, 2010.
DOI : 10.1101/gad.1945410

L. H. Chu and B. S. Chen, Construction of a cancer-perturbed protein-protein interaction network for discovery of apoptosis drug targets, BMC Systems Biology, vol.2, issue.56, pp.1-17, 2008.

H. Y. Chuang, E. Lee, Y. T. Liu, D. Lee, and T. Ideker, Network-based classification of breast cancer metastasis, Molecular Systems Biology, vol.5, p.140, 2007.
DOI : 10.1038/msb4100180

E. M. Clarke, J. R. Faeder, C. J. Langmead, L. A. Harris, S. K. Jha et al., Statistical Model Checking in BioLab: Applications to the Automated Analysis of T-Cell Receptor Signaling Pathway, CMSB, pp.231-250, 2008.
DOI : 10.1007/978-3-540-88562-7_18

V. Danos and C. Laneve, Formal molecular biology, Theoretical Computer Science, vol.325, issue.1, pp.69-110, 2004.
DOI : 10.1016/j.tcs.2004.03.065
URL : https://hal.archives-ouvertes.fr/hal-00164591

H. De and J. , Modeling and simulation of genetic regulatory systems: a literature review, J. Comput. Biol, vol.9, issue.1, pp.67-103, 2002.

H. De-jong, J. Geiselmann, C. Hernandez, and M. Page, Genetic Network Analyzer: qualitative simulation of genetic regulatory networks, Bioinformatics, vol.19, issue.3, pp.336-344, 1966.
DOI : 10.1093/bioinformatics/btf851
URL : https://hal.archives-ouvertes.fr/inria-00072325

H. De-jong and M. Page, Search for Steady States of Piecewise-Linear Differential Equation Models of Genetic Regulatory Networks, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.5, issue.2, pp.508-522, 2008.
DOI : 10.1109/TCBB.2007.70254
URL : https://hal.archives-ouvertes.fr/hal-01178204

H. De-jong and D. Ropers, Strategies for dealing with incomplete information in the modeling of molecular interaction networks, Briefings in Bioinformatics, vol.7, issue.4, pp.354-363, 2006.
DOI : 10.1093/bib/bbl034

E. Demir, PATIKA: an integrated visual environment for collaborative construction and analysis of cellular pathways, Bioinformatics, vol.18, issue.7, pp.996-1003, 2002.
DOI : 10.1093/bioinformatics/18.7.996

E. Demir, The BioPAX community standard for pathway data sharing, Nature Biotechnology, vol.35, issue.9, pp.935-942, 2010.
DOI : 10.1038/nbt.1666

F. Devun, L. Walter, J. Belliere, C. Cottet-rousselle, X. Leverve et al., Ubiquinone Analogs: A Mitochondrial Permeability Transition Pore-Dependent Pathway to Selective Cell Death, PLoS ONE, vol.382, issue.1, p.11792, 2010.
DOI : 10.1371/journal.pone.0011792.g005
URL : https://hal.archives-ouvertes.fr/inserm-00628487

N. C. Duarte, S. A. Becker, N. Jamshidi, I. Thiele, M. L. Mo et al., Global reconstruction of the human metabolic network based on genomic and bibliomic data, Proc. Natl. Acad. Sci. USA, pp.1777-1782, 2007.
DOI : 10.1073/pnas.0610772104

J. Elf and M. Ehrenberg, Fast Evaluation of Fluctuations in Biochemical Networks With the Linear Noise Approximation, Genome Research, vol.13, issue.11, pp.2475-2484, 2003.
DOI : 10.1101/gr.1196503

S. Elmore, Apoptosis: A Review of Programmed Cell Death, Toxicologic Pathology, vol.135, issue.2, pp.495-516, 2007.
DOI : 10.1016/S0092-8674(00)81382-3

S. N. Ethier and T. G. Kurtz, Markov processes: characterization and convergence. Wiley series in Probability and Statistics, 2005.
DOI : 10.1002/9780470316658

M. Feinberg, The existence and uniqueness of steady states for a class of chemical reaction networks, Archive for Rational Mechanics and Analysis, vol.49, issue.4, pp.311-370, 1995.
DOI : 10.1007/BF00375614

J. Fisher and T. Henzinger, Executable cell biology, Nature Biotechnology, vol.2034, issue.11, pp.1239-1249, 2007.
DOI : 10.1038/nbt1356

O. Folger, Predicting selective drug targets in cancer through metabolic networks, Molecular Systems Biol, vol.7, issue.501, pp.1-10, 2011.

C. Frezza, Haem oxygenase is synthetically lethal with the tumour suppressor fumarate hydratase, Nature, vol.1, issue.7363, pp.477225-228, 2011.
DOI : 10.1038/nature10363

C. W. Gardiner, Handbook of stochastic methods for physics, chemistry and the natural sciences, 2004.

E. Gasteiger, ExPASy: the proteomics server for in-depth protein knowledge and analysis, Nucleic Acids Research, vol.31, issue.13, pp.3784-3788, 2003.
DOI : 10.1093/nar/gkg563

I. M. Ghobrial, T. E. Witzig, and A. A. Adjei, Targeting Apoptosis Pathways in Cancer Therapy, CA: A Cancer Journal for Clinicians, vol.55, issue.3, pp.178-194, 2005.
DOI : 10.3322/canjclin.55.3.178

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.403-434, 1976.
DOI : 10.1016/0021-9991(76)90041-3

D. T. Gillespie, Exact stochastic simulation of coupled chemical reactions, The Journal of Physical Chemistry, vol.81, issue.25, pp.2340-2361, 1977.
DOI : 10.1021/j100540a008

D. T. Gillespie, A rigourous derivation of the chemical master equation, Physica A, vol.188, issue.1-3, pp.405-425, 1992.

D. T. Gillespie, Approximate accelerated stochastic simulation of chemically reacting systems, The Journal of Chemical Physics, vol.115, issue.4, pp.1716-1731, 2001.
DOI : 10.1063/1.1378322

D. T. Gillespie, Y. Cao, and L. Petzold, Multicale stochastic simulation algorithm with stochastic partial equilibrium assumption for chemically reacting systems, J. Comp. Phys, vol.206, issue.2, pp.395-411, 2005.

D. T. Gillespie, Y. Cao, and L. Petzold, The slow-scale stochastic simulation algorithm, J. Chem. Phys, vol.122, pp.403-434, 2005.

D. T. Gillespie, Y. Cao, K. R. Sanft, and L. R. Petzold, The subtle business of model reduction for stochastic chemical kinetics, The Journal of Chemical Physics, vol.130, issue.6, p.64103, 2009.
DOI : 10.1063/1.3072704

J. Goutsias, Quasiequilibrium approximation of fast reaction kinetics in stochastic biochemical systems, The Journal of Chemical Physics, vol.122, issue.18, p.184102, 2005.
DOI : 10.1063/1.1889434

J. Goutsias, Classical versus Stochastic Kinetics Modeling of Biochemical Reaction Systems, Biophysical Journal, vol.92, issue.7, pp.2350-2365, 2007.
DOI : 10.1529/biophysj.106.093781

J. Goutsias and N. H. Lee, Computational and Experimental Approaches for Modeling Gene Regulatory Networks, Current Pharmaceutical Design, vol.13, issue.14, pp.1415-1436, 2007.
DOI : 10.2174/138161207780765945

F. Grognard, H. De-jong, and J. L. Gouzé, Piecewise-Linear Models of Genetic Regulatory Networks: Theory and Example, LN in Control and Inf. Sci, pp.137-159, 2007.
DOI : 10.1007/978-3-540-71988-5_7
URL : https://hal.archives-ouvertes.fr/hal-01091662

J. Gunawardena, A linear elimination framework for nonlinear biochemical systems, 2011.

R. N. Gutenkunst, J. J. Waterfall, F. P. Casey, K. S. Brown, C. R. Myers et al., Universally sloppy parameter sensitivities in systems biology models, PLoS Comput. Biol, vol.3, pp.1871-1878, 2007.

E. L. Haseltine and J. B. Rawlings, Approximate simulation of coupled fast and slow reactions for stochastic chemical kinetics, The Journal of Chemical Physics, vol.117, issue.15, pp.6959-6969, 2002.
DOI : 10.1063/1.1505860

D. J. Higham, Modeling and Simulating Chemical Reactions, SIAM Review, vol.50, issue.2, pp.347-368, 2008.
DOI : 10.1137/060666457

W. Huang, B. T. Sherman, and R. A. Lampicki, Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists, Nucleic Acids Research, vol.37, issue.1, pp.1-17, 2009.
DOI : 10.1093/nar/gkn923

H. J. Huber, H. Duessmann, J. Wenus, S. M. Kilbride, and J. H. Prehn, Mathematical modelling of the mitochondrial apoptosis pathway, Biochimica et Biophysica Acta (BBA) - Molecular Cell Research, vol.1813, issue.4, pp.608-615, 2011.
DOI : 10.1016/j.bbamcr.2010.10.004

M. Hucka, Evolving a lingua franca and associated software infrastructure for computational systems biology: the Systems Biology Markup Language (SBML) project, Systems Biology, vol.1, issue.1, pp.41-53, 2004.
DOI : 10.1049/sb:20045008

K. Jaqaman and G. Danuser, Linking data to models: data regression, Nature Reviews Molecular Cell Biology, vol.91, issue.11, pp.1-10, 2006.
DOI : 10.1038/nrm2030

K. A. Johnson and R. S. Goody, The Original Michaelis Constant: Translation of the 1913 Michaelis???Menten Paper, Biochemistry, vol.50, issue.39, pp.8264-8269, 2011.
DOI : 10.1021/bi201284u

M. Kanehisha, S. Goto, S. Kawashima, Y. Okuno, and M. Hattori, The KEGG resource for deciphering the genome, Nucleic Acid Res, vol.32, pp.270-280, 2004.

G. Karlebach and R. Shamir, Modelling and analysis of gene regulatory networks, Nature Reviews Molecular Cell Biology, vol.18, issue.10, pp.770-780, 2008.
DOI : 10.1038/nrm2503

H. Kitano, Computational systems biology, Nature, vol.14, issue.6912, pp.206-210, 2002.
DOI : 10.1038/35002125

H. Kitano, A graphical notation for biochemical networks, BIOSILICO, vol.1, issue.5, pp.169-176, 2003.
DOI : 10.1016/S1478-5382(03)02380-1

P. E. Kloeden, E. Platen, and H. Schurz, Numerical solution of SDE through computer experiment, 1997.
DOI : 10.1007/978-3-642-57913-4

V. N. Kolokoltsov, Nonlinear Markov processes and kinetic Equations, volume 182 of Cambridge tracts in Mathematics, 2010.

M. Krull, TRANSPATH(R): an information resource for storing and visualizing signaling pathways and their pathological aberrations, Nucleic Acids Research, vol.34, issue.90001, pp.546-551, 2006.
DOI : 10.1093/nar/gkj107
URL : https://hal.archives-ouvertes.fr/hal-00314881

M. Krummenacker, S. Paley, L. Mueller, T. Yan, and P. D. Karp, Querying and computing with BioCyc databases, Bioinformatics, vol.21, issue.16, pp.3454-3455, 2005.
DOI : 10.1093/bioinformatics/bti546

S. Legewie, N. Blüthgen, and H. Herzel, Mathematical Modeling Identifies Inhibitors of Apoptosis as Mediators of Positive Feedback and Bistability, PLoS Computational Biology, vol.22, issue.9, p.120, 2006.
DOI : 10.1371/journal.pcbi.0020120.sd007

C. Li, M. Donizelli, N. Rodriguez, H. Dharuri, L. Endler et al., BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models, BMC Systems Biology, vol.4, issue.1, p.92, 2010.
DOI : 10.1186/1752-0509-4-92

H. Li, Y. Cao, L. Petzold, and D. Gillespie, Algorithms and Software for Stochastic Simulation of Biochemical Reacting Systems, Biotechnology Progress, vol.24, issue.1, pp.56-61, 2008.
DOI : 10.1021/bp070255h

J. Liang and H. Qian, Computational Cellular Dynamics Based on the Chemical Master Equation: A Challenge for Understanding Complexity, Journal of Computer Science and Technology, vol.11, issue.24, pp.154-168, 2010.
DOI : 10.1007/s11390-010-9312-6

W. E. Liu and E. Vanden-eijnden, Nested stochastic simulation algorithm for chemical kinetics systems with disparate rates, J. Chem. Phys, vol.123, issue.19, p.194107, 2002.

L. Lok and R. Brent, Automatic generation of cellular reaction networks with Moleculizer 1.0, Nature Biotechnology, vol.6, issue.1, pp.131-136, 2011.
DOI : 10.1093/bioinformatics/btg015

A. Mallavarapu, M. Thomson, B. Ullian, and J. Gunawardena, Programming with models: modularity and abstraction provide powerful capabilities for systems biology, Journal of The Royal Society Interface, vol.276, issue.34, pp.257-270, 2009.
DOI : 10.1074/jbc.M103369200

J. P. Massar, M. Travers, M. Elhai, and J. Shrager, BioLingua: a programmable knowledge environment for biologists, Bioinformatics, vol.21, issue.2, pp.199-207, 2005.
DOI : 10.1093/bioinformatics/bth465

L. Matthews, Reactome knowledgebase of human biological pathways and processes, Nucleic Acids Research, vol.37, issue.Database, pp.619-622, 2009.
DOI : 10.1093/nar/gkn863

J. M. Mccolluma, G. D. Peterson, C. D. Cox, M. L. Simpson, and N. F. Smatova, The sorting direct method for stochastic simulation of biochemical systems with varying reaction execution behavior, Computational Biology and Chemistry, vol.30, issue.1, pp.39-49, 2005.
DOI : 10.1016/j.compbiolchem.2005.10.007

S. Mcnamara, A. M. Bersani, K. Burrage, and R. B. Sidje, Stochastic chemical kinetics and the total quasi-steady-state assumption: Application to the stochastic simulation algorithm and chemical master equation, The Journal of Chemical Physics, vol.129, issue.9, p.95105, 2008.
DOI : 10.1063/1.2971036

D. A. Mcquarrie, Stochastic approach to chemical kinetics, Journal of Applied Probability, vol.42, issue.03, pp.413-478, 1967.
DOI : 10.1063/1.1733659

L. Menten and M. I. Michaelis, Die Kinetic der Invertinwirkung, Biochem. Z, vol.49, pp.333-369, 1913.

H. Mi, The PANTHER database of protein families, subfamilies, functions and pathways, Nucleic Acids Research, vol.33, issue.Database issue, pp.284-288, 2005.
DOI : 10.1093/nar/gki078

H. Miao, X. Xia, A. S. Perelson, and H. Wu, On Identifiability of Nonlinear ODE Models and Applications in Viral Dynamics, SIAM Review, vol.53, issue.1, pp.3-39, 2011.
DOI : 10.1137/090757009

A. K. Miller, An overview of the CellML API and its implementation, BMC Bioinformatics, vol.11, issue.1, p.178, 2010.
DOI : 10.1186/1471-2105-11-178

P. T. Monteiro, E. Dumas, B. Besson, R. Mateescu, M. Page et al., A service-oriented architecture for integrating the modeling and formal verification of genetic regulatory networks, BMC Bioinformatics, vol.10, issue.1, p.450, 2009.
DOI : 10.1186/1471-2105-10-450
URL : https://hal.archives-ouvertes.fr/hal-00784446

E. C. O-'shaughnessy, S. Palani, J. J. Collins, and C. A. Sarkar, Tunable Signal Processing in Synthetic MAP Kinase Cascades, Cell, vol.144, issue.1, pp.119-131, 2011.
DOI : 10.1016/j.cell.2010.12.014

H. Qian, The mathematical theory of molecular motor movement and chemomechanical energy transduction, Journal of Mathematical Chemistry, vol.27, issue.3, pp.219-234, 2000.
DOI : 10.1023/A:1026428320489

H. Qian and E. L. Elson, Single-molecule enzymology: stochastic Michaelis???Menten kinetics, Biophysical Chemistry, vol.101, issue.102, pp.101-102565, 2002.
DOI : 10.1016/S0301-4622(02)00145-X

R. Ramaswamy, N. González-segredo, and I. F. Sbalzarini, A new class of highly efficient exact stochastic simulation algorithms for chemical reaction networks, The Journal of Chemical Physics, vol.130, issue.24, pp.1-13, 2009.
DOI : 10.1063/1.3154624

R. Ramaswamy and I. F. Sbalzarini, A partial-propensity variant of the compositionrejection stochastic simulation algorithm for chemical reaction networks, J. Chem. Phys, vol.132, issue.044102, pp.1-6, 2010.

R. Ramaswamy and I. F. Sbalzarini, A partial-propensity formulation of the stochastic simulation algorithm for chemical rection networks with delays, J. Chem. Phys, vol.134, pp.1-8, 2011.

R. Ramaswamy, I. F. Sbalzarini, and N. González-segredo, Noise-Induced Modulation of the Relaxation Kinetics around a Non-Equilibrium Steady State of Non-Linear Chemical Reaction Networks, PLoS ONE, vol.383, issue.1, p.16045, 2011.
DOI : 10.1371/journal.pone.0016045.t001

S. Ramsey, D. Orrel, and H. Bolouri, DIZZY: STOCHASTIC SIMULATION OF LARGE-SCALE GENETIC REGULATORY NETWORKS, Journal of Bioinformatics and Computational Biology, vol.03, issue.02, pp.415-436, 2005.
DOI : 10.1142/S0219720005001132

C. V. Rao and A. P. Arkin, Stochastic chemical kinetics and the quasi-steady-state assumption: Application to the Gillespie algorithm, The Journal of Chemical Physics, vol.118, issue.11, pp.4999-5010, 2003.
DOI : 10.1063/1.1545446

D. Ropers, V. Baldazzi, H. De, and J. , Model Reduction Using Piecewise-Linear Approximations Preserves Dynamic Properties of the Carbon Starvation Response in Escherichia coli, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.8, issue.1, pp.166-181, 1966.
DOI : 10.1109/TCBB.2009.49
URL : https://hal.archives-ouvertes.fr/hal-00793034

S. Ryu, S. C. Lin, N. Ugel, M. Antoniotti, and B. Mishra, Mathematical modeling of the formation of apoptosome in intrinsic pathway of apoptosis, Systems and Synthetic Biology, vol.274, issue.1-2, pp.49-66, 2009.
DOI : 10.1007/s11693-009-9022-y

K. R. Sanft, D. T. Gillespie, and L. R. Petzold, Legitimacy of the stochastic Michaelis???Menten approximation, IET Systems Biology, vol.5, issue.1, pp.58-69, 2011.
DOI : 10.1049/iet-syb.2009.0057

C. F. Schaefer, PID: the Pathway Interaction Database, Nucleic Acids Research, vol.37, issue.Database, pp.674-679, 2009.
DOI : 10.1093/nar/gkn653

J. Schellenberger, J. O. Park, T. M. Conrad, and B. O. Palsson, BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions, BMC Bioinformatics, vol.11, issue.1, p.213, 2010.
DOI : 10.1186/1471-2105-11-213

M. Schulz, F. Krause, N. Le-novère, E. Klip, and W. Leibermeister, Retrieval, alignment, and clustering of computational models based on semantic annotations, Molecular Systems Biology, vol.11, issue.1, pp.1-10, 2011.
DOI : 10.1186/jbiol23

V. Shahrezaei and P. S. Swain, The stochastic nature of biochemical networks, Current Opinion in Biotechnology, vol.19, issue.4, pp.369-374, 2008.
DOI : 10.1016/j.copbio.2008.06.011

P. Shannon, Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks, Genome Research, vol.13, issue.11, pp.2498-2504, 2003.
DOI : 10.1101/gr.1239303

I. Shmulevitch and E. R. Dougherty, Genomic signal processing. Princeton series in Applied Mathematics, 2007.

C. K. Speirs, M. Hwang, S. Kim, W. Li, S. Chang et al., Harnessing the cell death pathway for targeted cancer treatment, Am. J. Cancer Res, vol.1, issue.1, pp.43-61, 2011.

M. Stiefenhofer, Quasi-steady-state approximation for chemical reaction networks, Journal of Mathematical Biology, vol.36, issue.6, pp.593-609, 1998.
DOI : 10.1007/s002850050116

I. W. Taylor, Dynamic modularity in protein interaction networks predicts breast cancer outcome, Nature Biotechnology, vol.129, issue.2, pp.199-204, 2009.
DOI : 10.1038/nbt.1522

M. Thomson and J. Gunawardena, The rational parameterisation theorem for multisite post-translational modification systems, Journal of Theoretical Biology, vol.261, issue.4, pp.626-636, 2009.
DOI : 10.1016/j.jtbi.2009.09.003

M. Thomson and J. Gunawardena, Unlimited multistability in multisite phosphorylation systems, Nature, vol.93, issue.7252, pp.274-277, 2009.
DOI : 10.1038/nature08102

D. J. Wilkinson, Stochastic modelling for systems biology, 2006.

S. Wu, J. Fu, Y. Cao, and L. R. Petzold, Michaelis???Menten speeds up tau-leaping under a wide range of conditions, The Journal of Chemical Physics, vol.134, issue.13, p.134112, 2011.
DOI : 10.1063/1.3576123

Y. Yamamoto, K. Inoue, and A. Doncescu, Integrating Abduction and Induction in Biological Inference Using CF-Induction, Elements of computational systems biology, pp.213-234, 2010.
DOI : 10.1002/9780470556757.ch9

N. Yosef, Toward accurate reconstruction of functional protein networks, Molecular Systems Biology, vol.19, issue.248, pp.1-19, 2009.
DOI : 10.1074/jbc.M706025200

G. U. Yule, A Mathematical Theory of Evolution, Based on the Conclusions of Dr. J. C. Willis, F.R.S., Philosophical Transactions of the Royal Society B: Biological Sciences, vol.213, issue.402-410, pp.21-87, 1925.
DOI : 10.1098/rstb.1925.0002

E. S. Zeron and M. Santillán, Numerical Solution of the Chemical Master Equation, Methods in Enzymology, vol.487, pp.147-169, 2011.
DOI : 10.1016/B978-0-12-381270-4.00006-8

W. Zhou, X. Peng, Z. Yang, and Y. Wang, Accelerated stochastic simulation algorithm for coupled chemical reactions with delays, Computational Biology and Chemistry, vol.32, issue.4, pp.240-242, 2008.
DOI : 10.1016/j.compbiolchem.2008.03.007