Cellular Decision Making and Biological Noise: From Microbes to Mammals, Cell, vol.144, issue.6, pp.910-925, 2011. ,
DOI : 10.1016/j.cell.2011.01.030
A Microfluidic Device for Temporally Controlled Gene Expression and Long-Term Fluorescent Imaging in Unperturbed Dividing Yeast Cells, PLoS ONE, vol.9, issue.12, p.1468, 2008. ,
DOI : 10.1371/journal.pone.0001468.s012
URL : https://hal.archives-ouvertes.fr/hal-00384163
Nonlinear models for repeated measurement data, 1995. ,
Modeling and simulation of genetic regulatory systems: a literature review, Journal of Computational Biology, vol.9, issue.1, pp.67-103, 2002. ,
Convergence of a Stochastic Approximation Version of the EM Algorithm. The Annals of Statistics, pp.94-128, 1999. ,
Mixed Models: Theory and Applications, 2004. ,
Mixed-effects and fMRI studies, NeuroImage, vol.24, issue.1, pp.244-52, 2005. ,
DOI : 10.1016/j.neuroimage.2004.08.055
Exact stochastic simulation of coupled chemical reactions, Journal of Phyical Chemistry, vol.81, pp.2340-2361, 1977. ,
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
Life cycle of the budding yeast Saccharomyces cerevisiae, Microbiological Reviews, vol.52, issue.4, pp.536-553, 1988. ,
Moment closure for biochemical networks, Proceedings of the Third International Symposium on Control, Communications and Signal Processing, 2008. ,
Osmotic Stress Signaling and Osmoadaptation in Yeasts, Microbiology and Molecular Biology Reviews, vol.66, issue.2, pp.300-372, 2002. ,
DOI : 10.1128/MMBR.66.2.300-372.2002
Nonparametric Statistical Methods, 1999. ,
DOI : 10.1002/9781119196037
Poisson-Gaussian noise parameter estimation in fluorescence microscopy imaging, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp.1663-1666, 2012. ,
DOI : 10.1109/ISBI.2012.6235897
URL : https://hal.archives-ouvertes.fr/hal-00646382
Accounting for extrinsic variability in the estimation of stochastic rate constants, International Journal of Robust and Nonlinear Control, vol.49, issue.4, pp.1103-1119, 2012. ,
DOI : 10.1002/rnc.2804
On Information and Sufficiency, The Annals of Mathematical Statistics, vol.22, issue.1, pp.79-86, 1951. ,
DOI : 10.1214/aoms/1177729694
Asymmetric segregation of protein aggregates is associated with cellular aging and rejuvenation, Proceedings of the National Academy of Sciences, pp.3076-3081, 2008. ,
DOI : 10.1073/pnas.0708931105
High-Dimensional ODEs Coupled With Mixed-Effects Modeling Techniques for Dynamic Gene Regulatory Network Identification, Journal of the American Statistical Association, vol.106, issue.496, pp.1242-1258, 2011. ,
DOI : 10.1198/jasa.2011.ap10194
Listening to the noise: random fluctuations reveal gene network parameters, Molecular Systems Biology, vol.9, 2009. ,
DOI : 10.1006/plas.2000.1477
A Systems-Level Analysis of Perfect Adaptation in Yeast Osmoregulation, Cell, vol.138, issue.1, pp.160-71, 2009. ,
DOI : 10.1016/j.cell.2009.04.047
Nonlinear Mixed-Effects Modeling: Individualization and Prediction, Aviation, Space, and Environmental Medicine, vol.75, pp.134-140, 2004. ,
Sources of Noise in Three-Dimensional Microscopical Data Sets, Three-Dimensional Confocal Microscopy: Volume Investigation of Biological Specimens, pp.47-94, 1994. ,
DOI : 10.1016/B978-0-12-668330-1.50007-7
Mixed-Effects Models in S and S-PLUS, 2000. ,
Nature, Nurture, or Chance: Stochastic Gene Expression and Its Consequences, Cell, vol.135, issue.2, pp.216-226, 2008. ,
DOI : 10.1016/j.cell.2008.09.050
URL : http://doi.org/10.1016/j.cell.2008.09.050
Stochastic modelling of gene regulatory networks, International Journal of Robust and Nonlinear Control, vol.122, issue.15, 2002. ,
DOI : 10.1002/rnc.1018
Origins of regulated cell-to-cell variability, Nature Reviews Molecular Cell Biology, vol.55, issue.2, pp.119-125, 2011. ,
DOI : 10.1038/nrm3044
Mammalian Genes Are Transcribed with Widely Different Bursting Kinetics, Science, vol.332, issue.6028, pp.472-474, 2011. ,
DOI : 10.1126/science.1198817
TOWARDS REAL-TIME CONTROL OF GENE EXPRESSION: CONTROLLING THE HOG SIGNALING CASCADE, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, pp.338-349, 2011. ,
DOI : 10.1142/9789814335058_0035
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 : 10.1073/pnas.1206810109
Stochastic Modelling for Systems Biology, 2006. ,
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