C. G. Bowsher, M. Voliotis, and P. S. Swain, The Fidelity of Dynamic Signaling by Noisy Biomolecular Networks, PLoS Computational Biology, vol.102, issue.4, p.1002965, 2013.
DOI : 10.1371/journal.pcbi.1002965.s001

E. Cinquemani, Reconstruction of promoter activity statistics from reporter protein population snapshot data, 2015 54th IEEE Conference on Decision and Control (CDC), pp.1471-1476, 2015.
DOI : 10.1109/CDC.2015.7402418

URL : https://hal.archives-ouvertes.fr/hal-01218010

E. Cinquemani, Hybrid Systems Biology: Fourth International Workshop Revised Selected Papers, chap. Reconstructing Statistics of Promoter Switching from Reporter Protein Population Snapshot Data, pp.3-19, 2015.
DOI : 10.1007/978-3-319-47151-8

D. Nicolao, G. Sparacino, G. Cobelli, and C. , Nonparametric input estimation in physiological systems: Problems, methods, and case studies, Automatica, vol.33, issue.5, pp.851-870, 1997.
DOI : 10.1016/S0005-1098(96)00254-3

N. Friedman, L. Cai, and X. S. Xie, Linking Stochastic Dynamics to Population Distribution: An Analytical Framework of Gene Expression, Physical Review Letters, vol.97, issue.16, p.168302, 2006.
DOI : 10.1103/PhysRevLett.97.168302

J. Hasenauer, S. Waldherr, M. Doszczak, N. Radde, P. Scheurich et al., Identification of models of heterogeneous cell populations from population snapshot data, BMC Bioinformatics, vol.12, issue.1, p.125, 2011.
DOI : 10.1038/sj.cdd.4401189

J. Hasenauer, V. Wolf, A. Kazeroonian, and F. J. Theis, Method of conditional moments (MCM) for the Chemical Master Equation, Journal of Mathematical Biology, vol.109, issue.21, pp.687-735, 2014.
DOI : 10.1007/s00285-013-0711-5

J. Hespanha, Modelling and analysis of stochastic hybrid systems, IEE Proceedings - Control Theory and Applications, vol.153, issue.5, pp.520-535, 2006.
DOI : 10.1049/ip-cta:20050088

H. De-jong, C. Ranquet, D. Ropers, C. Pinel, and J. Geiselmann, Experimental and computational validation of models of fluorescent and luminescent reporter genes in bacteria, BMC Systems Biology, vol.4, issue.1, p.55, 2010.
DOI : 10.1186/1752-0509-4-55

URL : https://hal.archives-ouvertes.fr/hal-00784438

M. Kaern, T. C. Elston, W. J. Blake, and J. J. Collins, Stochasticity in gene expression: from theories to phenotypes, Nature Reviews Genetics, vol.8706, issue.6, pp.451-464, 2005.
DOI : 10.1073/pnas.0400673101

L. H. Koopmans, The Spectral Analysis of Time Series, Probability and Mathematical Statistics, 1995.

A. Lindquist and G. Picci, Linear stochastic systems ? A geometric approach to modeling, estimation and identification, 2015.

B. Munsky, B. Trinh, and M. Khammash, Listening to the noise: random fluctuations reveal gene network parameters, Molecular Systems Biology, vol.9, issue.318, 2009.
DOI : 10.1006/plas.2000.1477

URL : http://doi.org/10.1038/msb.2009.75

G. Neuert, B. Munsky, R. Tan, L. Teytelman, M. Khammash et al., Systematic Identification of Signal-Activated Stochastic Gene Regulation, Science, vol.339, issue.6119, pp.584-587, 2013.
DOI : 10.1126/science.1231456

A. Papoulis, Probability, random variables, and stochastic processes. McGraw-Hill series in electrical engineering, 1991.

J. Paulsson, Models of stochastic gene expression, Physics of Life Reviews, vol.2, issue.2, pp.157-175, 2005.
DOI : 10.1016/j.plrev.2005.03.003

K. R. Sanft, S. Wu, M. Roh, J. Fu, R. K. Lim et al., StochKit2: software for discrete stochastic simulation of biochemical systems with events, Bioinformatics, vol.27, issue.17, pp.2457-2458, 2011.
DOI : 10.1093/bioinformatics/btr401

D. Stefan, C. Pinel, S. Pinhal, E. Cinquemani, J. Geiselmann et al., Inference of Quantitative Models of Bacterial Promoters from Time-Series Reporter Gene Data, PLOS Computational Biology, vol.123, issue.1, p.1004028, 2015.
DOI : 10.1371/journal.pcbi.1004028.s010

URL : https://hal.archives-ouvertes.fr/hal-01097632

C. Zechner, J. Ruess, P. Krenn, S. Pelet, M. Peter et al., Moment-based inference predicts bimodality in transient gene expression, Proceedings of the National Academy of Sciences, vol.109, issue.21, pp.8340-8345, 2012.
DOI : 10.1073/pnas.1200161109

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3361437

C. Zechner, M. Unger, S. Pelet, M. Peter, and H. Koeppl, Scalable inference of heterogeneous reaction kinetics from pooled single-cell recordings, Nature Methods, vol.92, issue.2, pp.197-202, 2014.
DOI : 10.1109/78.978383

V. Zulkower, M. Page, D. Ropers, J. Geiselmann, and H. De-jong, Robust reconstruction of gene expression profiles from reporter gene data using linear inversion, Bioinformatics, vol.31, issue.12, pp.71-79, 2015.
DOI : 10.1093/bioinformatics/btv246

URL : https://hal.archives-ouvertes.fr/hal-01217800