Density-based modeling and identification of biochemical networks in cell populations

IFAC Proceedings Volumes (IFAC-PapersOnline)

Volumn 9, Issue PART 1, 2010, Pages 320-325

  Hasenauer, J ^a   Waldherr, S ^a   Doszczak, M ^a   Scheurich, P ^a   Allgower F ^a  

^a UNIVERSITY OF STUTTGART   (Germany)

Author keywords

Apoptotic signaling; Density estimation; Parameter estimation; Population models

Indexed keywords

APOPTOTIC SIGNALING; BIOCHEMICAL NETWORK; BIOLOGICAL PROCESS; CASPASE ACTIVATION; CELL POPULATIONS; CONVEX OPTIMIZATION TECHNIQUES; DENSITY ESTIMATION; DENSITY-BASED; EXPERIMENTAL DATA; FLOW-CYTOMETRIC ANALYSIS; INDIVIDUAL CELLS; INITIAL CONDITIONS; MODEL SPECIFICATIONS; NOISE PROPERTIES; PARAMETER DISTRIBUTIONS; PARAMETER VALUES; PARTIAL DIFFERENTIAL; POPULATION MODEL; SINGLE CELLS; SINGLE-CELL MODELS; STATISTICAL DATA MODEL; SYSTEM OF ORDINARY DIFFERENTIAL EQUATIONS; UNKNOWN PARAMETERS;

CELL CULTURE; CELL PROLIFERATION; CELLS; CONVEX OPTIMIZATION; DATA FLOW ANALYSIS; DISTRIBUTION FUNCTIONS; ESTIMATION; ORDINARY DIFFERENTIAL EQUATIONS; PARAMETER ESTIMATION; PARTIAL DIFFERENTIAL EQUATIONS; PROCESS CONTROL;

POPULATION STATISTICS;

EID: 80051708722     PISSN: 14746670     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3182/20100705-3-BE-2011.0038     Document Type: Conference Paper

References (14)

1. S.V. Avery. Microbial cell individuality and the underlying sources of heterogeneity. Nat. Rev. Microbiol., 4:577-587, 2006. (Pubitemid 44317020)
2. T. Eissing, H. Conzelmann, E.D. Gilles, F. Allgöwer, E. Bullinger, and P. Scheurich. Bistability analyses of a caspase activation model for receptor-induced apoptosis. J. of Biol. Chem., 279 (35): 36892-36897, 2004. (Pubitemid 39129038)
3. T. Eissing, S. Waldherr, E. Bullinger, C. Gondro, O. Sawodny, F. Allgöwer, P. Scheurich, and T. Sauter. Sensitivity analysis of programmed cell death and implications for crosstalk phenomena during Tumor Necrosis Factor stimulation. In Proc. IEEE Conf. Contr. Appl. (CCA), pages 1746-52, 2006. (Pubitemid 351635079)
4. L.C. Evans. Partial Differential Equations. American Mathematical Society, 1998.
5. M.O. Hengartner. The biochemistry of apoptosis. Nature, 407(6805): 770-776, 2000.
6. T. Luzyanina, D. Roose, T. Schenkel, M. Sester, S. Ehl, A. Meyerhans, and G. Bocharov. Numerical modelling of label-structured cell population growth using CFSE distribution data. Theo. Biol. and Med. Mod., 4(26):1-14, 2007.
7. T. Luzyanina, D. Roose, and G. Bocharov. Distributed parameter identification for label-structured cell population dynamics model using CFSE histogram time-series data. J. Math. Biol., 59:581-603, 2009.
8. N.V. Mantzaris. From single-cell genetic architecture to cell population dynamics: Quantitatively decomposing the effects of different population heterogeneity sources for a genetic network with positive feedback architecture. Biophys. J., 92:4271-4288, 2007. (Pubitemid 46910543)
9. B. Munsky, B. Trinh, and M. Khammash. Listening to the noise: random fluctuations reveal gene network parameters. Mol. Syst. Biol., 5, 2009.
10. J.B. Rawlings and B.R. Bakshi. Particle filtering and moving horizon estimation. Comp. and Chem. Eng., 30:1529-1541, 2006. (Pubitemid 44292278)
11. B.W. Silverman. Density Estimation for Statistics and Data Analysis. Monographs on Statistics and Applied Probability. London: Chapman and Hall, 1986.
12. C.J. Stone. An asymptotically optimal window selection rule for kernel density estimation. Annual Statistics, 12:1285-1297, 1984.
13. H.M. Tsuchiya, A.G. Fredrickson, and R. Aris. Dynamics of microbial cell populations. Adv. Chem. Eng., 6:125-206, 1966.
14. S. Waldherr, J. Hasenauer, and F. Allgöwer. Estimation of biochemical network parameter distributions in cell populations. In Proc. of the 15th IFAC Symp. on Syst. Ident., pages 1265-1270, 2009.