Identification of biological models from single-cell data: A comparison between mixed-effects and moment-based inference

2013 European Control Conference, ECC 2013

Volumn , Issue , 2013, Pages 3652-3657

  Gonzalez, Andres M ^a   Uhlendorf, Jannis ^b   Schaul, Joe ^b   Cinquemani, Eugenio ^c   Batt, Gregory ^b   Ferrari Trecate, Giancarlo ^a  

^a UNIVERSITY OF PAVIA   (Italy)

^b INRIA ROCQUENCOURT   (France)

^c INRIA RHÔNE ALPES   (France)

Author keywords

[No Author keywords available]

Indexed keywords

CELL CULTURE; CELL PROLIFERATION; GENE EXPRESSION; PROBABILITY DISTRIBUTIONS;

BIOLOGICAL NETWORKS; CELL-TO-CELL VARIABILITY; CHEMICAL MASTER EQUATION; EXPERIMENTAL TECHNIQUES; IDENTIFICATION APPROACH; MICRO-FLUIDIC DEVICES; NETWORK PARAMETERS; TIME-LAPSE MICROSCOPY;

POPULATION STATISTICS;

EID: 84893293657     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.23919/ecc.2013.6669366     Document Type: Conference Paper

References (23)

1. B. Snijder and L. Pelkmans, Origins of regulated cell-to-cell variability, Nature Reviews, Molecular Cell Biology, vol. 12, pp. 119-125, Feb. 2011.
2. G. Balazsi, A. van Oudenaarden, and J. J. Collins, Cellular Decision Making and Biological Noise: From Microbes to Mammals, Cell, vol. 144, pp. 910-925, Mar. 2011.
3. B. Munsky, B. Trinh, and M. Khammash, Listening to the noise: random fluctuations reveal gene network parameters, Molecular Systems Biology, vol. 5, Oct. 2009.
4. J. P. Hespanha, Moment closure for biochemical networks, in Proceedings of the Third International Symposium on Control, Communications and Signal Processing, March 2008.
5. C. Zechner, J. Ruess, P. Krenn, S. Pelet, M. Peter, J. Lygeros, and H. Koeppl, Moment-based inference predicts bimodality in transient gene expression, Proceedings of the National Academy of Sciences, vol. 109, pp. 8340-8345, May 2012.
6. J. C. Pinheiro and D. M. Bates, Mixed-Effects Models in S and SPLUS. Springer Verlag, New York, first ed., 2000.
7. M. Davidian and D. M. Giltinan, Nonlinear models for repeated measurement data. Chapman & Hall, 1995.
8. J. Uhlendorf, A. Miermont, T. Delaveau, G. Charvin, F. Fages, S. Bottani, G. Batt, and P. Hersen, Long-term model predictive control of gene expression at the population and single-cell levels, Proceedings of the National Academy of Sciences, vol. 109, pp. 14271-14276, Aug. 2012.
9. H. E. Samad, M. Khammash, L. Petzold, and D. Gillespie, Stochastic modelling of gene regulatory networks, International Journal of Robust and Nonlinear Control, 2002.
10. D. Wilkinson, Stochastic Modelling for Systems Biology. London, UK: Chapman & Hall/CRC, 2006.
11. J. Hasenauer, S. Waldherr, M. Doszczak, N. Radde, P. Scheurich, and F. Allgower, Identification of models of heterogeneous cell populations from population snapshot data, BMC Bioinformatics, vol. 12, p. 125, Apr. 2011.
12. S. Hohmann, Osmotic Stress Signaling and Osmoadaptation in Yeasts, Microbiology and Molecular Biology Reviews, vol. 66, pp. 300-372, June 2002.
13. H. de Jong, Modeling and simulation of genetic regulatory systems: A literature review., Journal of Computational Biology, vol. 9, pp. 67- 103, Jan. 2002.
14. A. M. Gonzalez, J. Uhlendorf, J. Schaul, E. Cinquemani, G. Batt, and G. Ferrari-Trecate, Identification of biological models from singlecell data: A comparison between mixed-effects and moment-based inference, Tech. Rep. RR-8288, INRIA, April 2013.
15. A. Jezierska, H. Talbot, C. Chaux, J.-C. Pesquet, and G. Engler, Poisson-Gaussian noise parameter estimation in fluorescence microscopy imaging., in 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1663-1666, IEEE, May 2012.
16. B. Delyon, M. Lavielle, and E. Moulines, Convergence of a Stochastic Approximation Version of the EM Algorithm, The Annals of Statistics, vol. 27, no. 1, pp. 94-128, 1999.
17. I. Herskowitz, Life cycle of the budding yeast Saccharomyces cerevisiae., Microbiological Reviews, vol. 52, pp. 536-553, Dec. 1988.
18. D. T. Gillespie, Exact stochastic simulation of coupled chemical reactions, Journal of Phyical Chemistry, vol. 81, pp. 2340-2361, December 1977.
19. M. Hollander and D. A. Wolfe, Nonparametric Statistical Methods, 2nd Edition. Wiley-Interscience, 2 ed., Jan. 1999.
20. The MathWorks, Matlab statistics toolbox, R2011a.
21. The MathWorks, Matlab optimization toolbox, R2011a.
22. A. B. Lindner, R. Madden, A. Demarez, E. J. Stewart, and F. Taddei, Asymmetric segregation of protein aggregates is associated with cellular aging and rejuvenation, Proceedings of the National Academy of Sciences, vol. 105, pp. 3076-3081, Feb. 2008.
23. G. Charvin, F. R. Cross, and E. D. Siggia, A microfluidic device for temporally controlled gene expression and long-term fluorescent imaging in unperturbed dividing yeast cells, PLoS One, vol. 3, p. e1468, January 2008.