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2010 | 20 | 4 | 773-780
Tytuł artykułu

Planning identification experiments for cell signaling pathways: An NFκB case study

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Mathematical modeling of cell signaling pathways has become a very important and challenging problem in recent years. The importance comes from possible applications of obtained models. It may help us to understand phenomena appearing in single cells and cell populations on a molecular level. Furthermore, it may help us with the discovery of new drug therapies. Mathematical models of cell signaling pathways take different forms. The most popular way of mathematical modeling is to use a set of nonlinear ordinary differential equations (ODEs). It is very difficult to obtain a proper model. There are many hypotheses about the structure of the model (sets of variables and phenomena) that should be verified. The next step, fitting the parameters of the model, is also very complicated because of the nature of measurements. The blotting technique usually gives only semi-quantitative observations, which are very noisy and collected only at a limited number of time moments. The accuracy of parameter estimation may be significantly improved by a proper experiment design. Recently, we have proposed a gradient-based algorithm for the optimization of a sampling schedule. In this paper we use the algorithm in order to optimize a sampling schedule for the identification of the mathematical model of the NFκB regulatory module, known from the literature. We propose a two-stage optimization approach: a gradient-based procedure to find all stationary points and then pair-wise replacement for finding optimal numbers of replicates of measurements. Convergence properties of the presented algorithm are examined.
Rocznik
Tom
20
Numer
4
Strony
773-780
Opis fizyczny
Daty
wydano
2010
otrzymano
2010-01-18
poprawiono
2010-06-09
Twórcy
  • Institute of Automatic Control, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
Bibliografia
  • Box, G.E.P. and Lucas, H.L. (1959). Design of experiments in nonlinear situations, Biometrika 46(1/2): 77-90.
  • Box, M.J. (1968). The occurrence of replications in optimal designs of experiments to estimate parameters in non-linear models, Journal of the Royal Statistical Society. Series B 30(2): 290-302.
  • Chernoff, H. (1972). Sequential Analysis and Optimal Design, SIAM, Philadelphia, PA.
  • D'Argenio, D.Z. (1981). Optimal sampling times for pharmacokinetic experiments, Journal of Pharmacokinetics and Biopharmaceutics 9(6): 739-756.
  • de Jong, H. (2002). Modeling and simulation of genetic regulatory systems: A literature review, Journal of Computational Biology 9(1): 67-103.
  • DiStefano, J.J. (1981). Optimized blood sampling protocols and sequential design of kinetic experiments, American Journal of Physiology 9(240): R259-R265.
  • Fedorov, V.V. (1972). Theory of Optimal Experiments, Academic Press, New York, NY.
  • Fujarewicz, K. (2007). Planning identification experiments for cell signaling pathways using sensitivity analysis, Proceedings of the 23rd IFIP Conference on System Modelling and Optimization, Cracow, Poland, pp. 262-263.
  • Fujarewicz, K. (2008). Optimal scheduling for parameter estimation of cell signaling pathway models-A gradient approach, Proceedings of the 25th IASTED International Multi-Conference on Biomedical Engineering, Innsbruck, Austria, pp. 232-236.
  • Fujarewicz, K. (2009). Planning identification experiments for nfkb signaling pathway, Proceedings of the 15th National Conference on Application of Mathematics in Biology and Medicine, Szczyrk, Poland, pp. 64-53.
  • Fujarewicz, K. and Galuszka, A. (2004). Generalized backpropagation through time for continuous time neural networks and discrete time measurements, in L. Rutkowski, J. Siekmann, R. Tadeusiewicz and L. A. Zadeh (Eds.) Artificial Intelligence and Soft Computing-ICAISC 2004, Lecture Notes in Computer Science, Vol. 3070, Springer-Verlag, Berlin, pp. 190-196.
  • Fujarewicz, K., Kimmel, M., Lipniacki, T. and Swierniak, A. (2007). Adjoint systems for models of cell signalling pathways and their application to parameter fitting, IEEE/ACM Transactions on Computational Biology and Bioinformatics 4(3): 322-335.
  • Goodwin, G.C. and Payne, R.L. (1977). Dynamic System Identification: Experiment Design and Data Analysis, Academic Press, New York, NY.
  • Hoffman, A., Levchenko, A., Scott, M.L. and Baltimore, D. (2002). The iκb-nf-κb signaling module: Temporal control and selective gene activation, Science 298: 1241-1245.
  • Jacquez, J. (1998). Designs of experiments, Journal of Franklin Institute 335(2).
  • Jacquez, J. and Greif, P. (1985). Numerical parameter identifiability and estimability: Integrating identifiability, estimability, and optimal sampling design, Mathematical Biosciences 77: 201-227.
  • Kiefer, J. (1961). Optimum designs in regression problems, II. The Annals of Mathematical Statistics 32(1): 298-325.
  • Kutalik, Z., Cho, K. and Wolkenhauer, O. (2004). Optimal sampling time selection for parameter estimation in dynamic pathway modeling, BioSystems 75(1-3): 43-55.
  • Lee, E., Boone, D., Chai, S., Libby, S., Chien, M., Lodolce, J. and Ma, A. (2000). Failure to regulate tnf-induced nfκb and cell death responses in a20-deficient mice, Science 289(5488): 2350-2354.
  • Lipniacki, T., Paszek, P., Brasier, A.R., Luxon, B. and Kimmel, M. (2004). Mathematical model of nf-κb regulatory module, Journal of Theoretical Biology 228(2): 195-215.
  • Pronzato, L. and Walter, E. (1985). Robust experiment design via stochastic approximation, Mathematical Biosciences 75: 103-120.
  • Tod, M. and Rocchisani, J.M. (1997). Comparison of ed, eid and api criteria for the robust optimization of sampling times in pharmacokinetics, Journal of Pharmacokinetics and Biopharmaceutics 25(4): 515-537.
Typ dokumentu
Bibliografia
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Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv20i4p773bwm
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