of biological models from single-cell data: a comparison between mixed-eects and moment-based inference

[1]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[2]  D. Gillespie Exact Stochastic Simulation of Coupled Chemical Reactions , 1977 .

[3]  I. Herskowitz,et al.  Life cycle of the budding yeast Saccharomyces cerevisiae. , 1988, Microbiological reviews.

[4]  James B. Pawley,et al.  CHAPTER 3 – Sources of Noise in Three-Dimensional Microscopical Data Sets , 1994 .

[5]  L. Skovgaard NONLINEAR MODELS FOR REPEATED MEASUREMENT DATA. , 1996 .

[6]  É. Moulines,et al.  Convergence of a stochastic approximation version of the EM algorithm , 1999 .

[7]  D. Bates,et al.  Mixed-Effects Models in S and S-PLUS , 2001 .

[8]  S. Hohmann Osmotic Stress Signaling and Osmoadaptation in Yeasts , 2002, Microbiology and Molecular Biology Reviews.

[9]  Hidde de Jong,et al.  Modeling and Simulation of Genetic Regulatory Systems: A Literature Review , 2002, J. Comput. Biol..

[10]  Erik Olofsen,et al.  Nonlinear mixed-effects modeling: individualization and prediction. , 2004, Aviation, space, and environmental medicine.

[11]  E. Demidenko,et al.  Mixed Models: Theory and Applications (Wiley Series in Probability and Statistics) , 2004 .

[12]  Nick T. Thomopoulos,et al.  Some Measures on the Standard Bivariate Lognormal Distribution , 2004 .

[13]  Karl J. Friston,et al.  Mixed-effects and fMRI studies , 2005, NeuroImage.

[14]  Linda R. Petzold,et al.  Stochastic modelling of gene regulatory networks , 2005 .

[15]  Darren J. Wilkinson Stochastic Modelling for Systems Biology , 2006 .

[16]  Gilles Charvin,et al.  A Microfluidic Device for Temporally Controlled Gene Expression and Long-Term Fluorescent Imaging in Unperturbed Dividing Yeast Cells , 2008, PloS one.

[17]  A. Oudenaarden,et al.  Nature, Nurture, or Chance: Stochastic Gene Expression and Its Consequences , 2008, Cell.

[18]  François Taddei,et al.  Asymmetric segregation of protein aggregates is associated with cellular aging and rejuvenation , 2008, Proceedings of the National Academy of Sciences.

[19]  J. Hespanha Moment closure for biochemical networks , 2008, 2008 3rd International Symposium on Communications, Control and Signal Processing.

[20]  A. Oudenaarden,et al.  A Systems-Level Analysis of Perfect Adaptation in Yeast Osmoregulation , 2009, Cell.

[21]  Brian Munsky,et al.  Listening to the noise: random fluctuations reveal gene network parameters , 2009, Molecular systems biology.

[22]  Frank Allgöwer,et al.  Identification of models of heterogeneous cell populations from population snapshot data , 2011, BMC Bioinformatics.

[23]  B. Snijder,et al.  Origins of regulated cell-to-cell variability , 2011, Nature Reviews Molecular Cell Biology.

[24]  Nacho Molina,et al.  Mammalian Genes Are Transcribed with Widely Different Bursting Kinetics , 2011, Science.

[25]  Hongzhe Li,et al.  High-Dimensional ODEs Coupled With Mixed-Effects Modeling Techniques for Dynamic Gene Regulatory Network Identification , 2011, Journal of the American Statistical Association.

[26]  François Fages,et al.  Towards Real-Time Control of Gene Expression: Controlling the Hog Signaling Cascade , 2011, Pacific Symposium on Biocomputing.

[27]  J. Lygeros,et al.  Moment-based inference predicts bimodality in transient gene expression , 2012, Proceedings of the National Academy of Sciences.

[28]  Heinz Koeppl,et al.  Accounting for extrinsic variability in the estimation of stochastic rate constants , 2012 .

[29]  Hugues Talbot,et al.  Poisson-Gaussian noise parameter estimation in fluorescence microscopy imaging , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[30]  F. Fages,et al.  Long-term model predictive control of gene expression at the population and single-cell levels , 2012, Proceedings of the National Academy of Sciences.