C. E. Antoniak, Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems. The annals of statistics, pp.1152-1174, 1974.

M. A. Beaumont, W. Zhang, and D. J. Balding, Approximate Bayesian computation in population genetics, Genetics, vol.162, issue.4, pp.2025-2035, 2002.

D. M. Blei and M. I. Jordan, Variational inference for Dirichlet process mixtures, Bayesian Analysis, vol.1, issue.1, pp.121-143, 2006.
DOI : 10.1214/06-BA104

O. Cappé, R. Douc, A. Guillin, J. Marin, R. et al., Adaptive importance sampling in general mixture classes, Statistics and Computing, vol.10, issue.2, pp.447-459, 2008.
DOI : 10.1007/978-1-4757-4321-0

J. Cornuet, J. Marin, A. Mira, R. , and C. P. , Adaptive Multiple Importance Sampling, Scandinavian Journal of Statistics, vol.62, issue.4, pp.798-812, 2012.
DOI : 10.1111/1467-9868.00254
URL : https://hal.archives-ouvertes.fr/hal-00403248

A. De, Wnt/Ca2+ signaling pathway: a brief overview, Acta Biochimica et Biophysica Sinica, vol.21, issue.10, pp.745-756, 2011.
DOI : 10.1016/S0165-6147(00)01468-1

D. Moral, P. Doucet, A. Jasra, and A. , Sequential Monte Carlo samplers, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.55, issue.3, pp.411-436, 2006.
DOI : 10.1007/978-1-4757-4145-2
URL : https://hal.archives-ouvertes.fr/hal-01593880

D. Moral, P. Doucet, A. Jasra, and A. , An adaptive sequential Monte Carlo method for approximate Bayesian computation, Statistics and Computing, vol.6, issue.5, pp.1009-1020, 2012.
DOI : 10.1098/rsif.2008.0172

M. D. Escobar, Estimating Normal Means with a Dirichlet Process Prior, Journal of the American Statistical Association, vol.2, issue.425, pp.268-277, 1994.
DOI : 10.2307/2347988

M. D. Escobar and M. West, Bayesian Density Estimation and Inference Using Mixtures, Journal of the American Statistical Association, vol.55, issue.430, pp.90-577, 1995.
DOI : 10.1007/978-1-4612-2714-4_16

S. Filippi, C. P. Barnes, J. Cornebise, and M. P. Stumpf, On optimality of kernels for approximate bayesian computation using sequential monte carlo. Statistical applications in genetics and molecular biology, pp.87-107, 2013.
DOI : 10.1515/sagmb-2012-0069
URL : http://arxiv.org/pdf/1106.6280

B. Fleiss, J. Van-steenwinckel, A. Schang, S. Sigaut, M. Krishnan et al., Microglial wnt signaling inhibition promotes microglia activation and oligodendrocyte maturation blockade, GLIA, 2015.

G. H. Givens and A. E. Raftery, Local Adaptive Importance Sampling for Multivariate Densities with Strong Nonlinear Relationships, Journal of the American Statistical Association, vol.55, issue.433, pp.91-132, 1996.
DOI : 10.1016/0304-4076(80)90030-5

D. Görür and C. E. Rasmussen, Dirichlet Process Gaussian Mixture Models: Choice of the Base Distribution, Journal of Computer Science and Technology, vol.25, issue.3, pp.653-664, 2010.
DOI : 10.1007/s11390-010-9355-8

P. B. Jensen, L. Pedersen, S. Krishna, and M. H. Jensen, A Wnt Oscillator Model for Somitogenesis, Biophysical Journal, vol.98, issue.6, pp.98-943950, 2010.
DOI : 10.1016/j.bpj.2009.11.039
URL : https://doi.org/10.1016/j.bpj.2009.11.039

M. Kühl and T. P. Rao, An updated overview on wnt signaling pathways: A prelude for more, Circulation Research, vol.106, pp.1798-1806, 2010.

E. Lee, A. Salic, R. Krüger, R. Heinrich, and M. W. Kirschner, The Roles of APC and Axin Derived from Experimental and Theoretical Analysis of the Wnt Pathway, PLoS Biology, vol.274, issue.1, pp.745-756, 2003.
DOI : 10.1371/journal.pbio.0000010.st002

J. Liepe, C. Barnes, E. Cule, K. Erguler, P. Kirk et al., ABC-SysBio--approximate Bayesian computation in Python with GPU support, Bioinformatics, vol.24, issue.1, pp.26-1797, 2010.
DOI : 10.1093/bioinformatics/btp619
URL : https://academic.oup.com/bioinformatics/article-pdf/26/14/1797/428759/btq278.pdf

S. N. Maceachern, Estimating normal means with a conjugate style dirichlet process prior, Communications in Statistics - Simulation and Computation, vol.34, issue.3, pp.727-741, 1994.
DOI : 10.1017/CBO9780511526237

A. Maclean, H. Harrington, M. Stumpf, and H. Byrne, Mathematical and Statistical Techniques for Systems Medicine: The Wnt Signaling Pathway as a Case Study, Methods Mol Biol, vol.1386, pp.405-439, 2016.
DOI : 10.1007/978-1-4939-3283-2_18

P. Marjoram, J. Molitor, V. Plagnol, and S. Tavaré, Markov chain Monte Carlo without likelihoods, Proceedings of the National Academy of Sciences, pp.15324-15328, 2003.
DOI : 10.1214/aoms/1177700372
URL : http://www.pnas.org/content/100/26/15324.full.pdf

O. Mazemondet, M. John, S. Leye, A. Rolfs, and A. Uhrmacher, Elucidating the Sources of ??-Catenin Dynamics in Human Neural Progenitor Cells, PLoS ONE, vol.7, issue.8, 2012.
DOI : 10.1371/journal.pone.0042792.s002
URL : https://hal.archives-ouvertes.fr/hal-00656231

G. Mclachlan and D. Peel, Finite mixture models, 2004.
DOI : 10.1002/0471721182

A. Milias-argeitis, S. Summers, J. Stewart-ornstein, I. Zuleta, D. Pincus et al., In silico feedback for in vivo regulation of a gene expression circuit, Nature Biotechnology, vol.5, issue.12, pp.29-1114, 2011.
DOI : 10.1186/1754-1611-3-15

P. Müller, A. Erkanli, and M. West, Bayesian curve fitting using multivariate normal mixtures, Biometrika, vol.83, issue.1, pp.67-79, 1996.
DOI : 10.1093/biomet/83.1.67

R. M. Neal, Markov chain sampling methods for Dirichlet process mixture models, Journal of computational and graphical statistics, vol.9, issue.2, pp.249-265, 2000.
DOI : 10.1080/10618600.2000.10474879
URL : http://www.ics.uci.edu/~ihler/classes/CS295/papers/neal00.pdf

J. K. Pritchard, M. T. Seielstad, A. Perez-lezaun, and M. W. Feldman, Population growth of human Y chromosomes: a study of Y chromosome microsatellites, Molecular Biology and Evolution, vol.16, issue.12, pp.16-1791, 1999.
DOI : 10.1093/oxfordjournals.molbev.a026091

C. E. Rasmussen, The Infinite Gaussian Mixture Model, NIPS, pp.554-560, 1999.

Y. Schmitz, K. Rateitschak, and O. Wolkenhauer, Analysing the impact of nucleo-cytoplasmic shuttling of ??-catenin and its antagonists APC, Axin and GSK3 on Wnt/??-catenin signalling, Cellular Signalling, vol.25, issue.11, p.22102221, 2013.
DOI : 10.1016/j.cellsig.2013.07.005

S. A. Sisson, Y. Fan, and M. M. Tanaka, Sequential Monte Carlo without likelihoods, Proceedings of the National Academy of Sciences, pp.1760-1765, 2007.
DOI : 10.1111/j.1467-9868.2005.00497.x
URL : http://www.pnas.org/content/104/6/1760.full.pdf

E. B. Sudderth, Graphical models for visual object recognition and tracking, 2006.

C. Tan, B. Gardiner, Y. Hirokawa, D. Smith, and A. Burgess, Analysis of Wnt signaling ??-catenin spatial dynamics in HEK293T cells, BMC Systems Biology, vol.8, issue.1, pp.1-18, 2014.
DOI : 10.1137/S1064827594276424
URL : https://bmcsystbiol.biomedcentral.com/track/pdf/10.1186/1752-0509-8-44?site=bmcsystbiol.biomedcentral.com

C. W. Tan, B. S. Gardiner, Y. Hirokawa, M. J. Layton, D. W. Smith et al., Wnt Signalling Pathway Parameters for Mammalian Cells, PLoS ONE, vol.18, issue.2, p.31882, 2012.
DOI : 10.1371/journal.pone.0031882.s018
URL : https://doi.org/10.1371/journal.pone.0031882

S. Tavaré, D. J. Balding, R. C. Griffiths, and P. Donnelly, Inferring coalescence times from DNA sequence data, Genetics, vol.145, issue.2, pp.505-518, 1997.

T. Toni, D. Welch, N. Strelkowa, A. Ipsen, and M. P. Stumpf, Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems, Journal of The Royal Society Interface, vol.24, issue.6, pp.187-202, 2009.
DOI : 10.1093/bioinformatics/btm607
URL : http://rsif.royalsocietypublishing.org/content/royinterface/6/31/187.full.pdf

J. Uhlendorf, A. Miermont, T. Delaveau, G. Charvin, F. Fages et al., Long-term model predictive control of gene expression at the population and single-cell levels, Proceedings of the National Academy of Sciences, pp.14271-14276, 2012.
DOI : 10.1016/0031-3203(81)90009-1
URL : https://hal.archives-ouvertes.fr/hal-01528440

L. Wang and D. B. Dunson, Fast Bayesian Inference in Dirichlet Process Mixture Models, Journal of Computational and Graphical Statistics, vol.20, issue.1, pp.196-216, 2011.
DOI : 10.1198/jcgs.2010.07081
URL : http://europepmc.org/articles/pmc3812957?pdf=render

D. Wraith, M. Kilbinger, K. Benabed, O. Cappé, J. Cardoso et al., Estimation of cosmological parameters using adaptive importance sampling, Physical Review D, vol.39, issue.2, p.23507, 2009.
DOI : 10.1007/s00180-007-0022-7
URL : https://hal.archives-ouvertes.fr/hal-00365944