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Sensitivity analysis of deterministic signaling pathways models

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper is focused on application of sensitivity methods to analysis of signaling pathway models. Two basic methods are compared: local, based on standard sensitivity functions, and global, based on Sobol indices. Firstly, a general outline of modeling of signaling pathways by means of ordinary differential equations is briefly described. Afterwards, the methods of sensitivity analysis, known from literature, are introduced and illustrated with a simple example of a dynamical system of the second order. Subsequently, the analysis of the p53/Mdm2 regulatory module, which is a key element of any pathway involving p53 protein, is presented. The results of this analysis suggest that no single method should be chosen for investigation of any signaling pathway model but several of them should be applied to answer important questions about sources of heterogeneity in cells behavior, robustness of signaling pathways and possible molecular drug targets.
Słowa kluczowe
Rocznik
Strony
471--479
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
autor
autor
  • Institute of Automatic Control, Silesian University of Technology, 16 Akademicka St., 44-100 Gliwice, Poland
Bibliografia
  • [1] J.J. Tyson, R. Albert, A. Goldbeter, P. Ruoff, and J. Sible, “Biological switches and clocks”, J. Royal Society Interface 5 (1), S1-S8 (2008).
  • [2] P. Iglesias and B. Ingalls, Control Theory and Systems Biology, MIT Press, Cambridge, 2010.
  • [3] J. Smieja, M. Jamalludin, A. Brasier, and M. Kimmel, “Modelbased analysis of interferon-induced signaling pathway”, Bioinformatics 24 (20), 2363-2369 (2008).
  • [4] J. Tyson, K. Chen, and B. Novak, “Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell”, Current Opinion in Cell Biology 15, 221--231 (2003).
  • [5] K. Richter, M. Nessling, and P. Lichter, “Macromolecular crowding and its potential impact on nuclear function”, Biochimica et Biophysica Acta 1783 (11), 2100-2107 (2008).
  • [6] H.X. Zhou, G. Rivas, and A.P. Minton, “Macromolecular crowding and confinement: biochemical, biophysical, and potential physiological consequences”, Annual Review of Biophysics 37, 375-397 (2008).
  • [7] E. Conrad and J. Tyson, “Modeling molecular interaction networks with nonlinear ordinary diferential equations”, System Modeling in Cell Biology. From Concepts to Nuts and Bolts, eds. Z. Szallasi, J. Stelling, and V. Periwal, pp. 97-123, The MIT Press, Cambridge, 2006.
  • [8] H. Lodish, A. Berk, S. Zipursky, P. Matsudaira, D. Baltimore, and J. Darnell, Molecular Cell Biology, 4th edition, W.H. Freeman, New York, 2000.
  • [9] D.A. Rand, “Mapping the global sensitivity of cellular network dynamics”, J. Royal Society Interface 5, S59-S69 (2008).
  • [10] K.A. Kim, S.L. Spencer, J.G. Albeck, J.M. Burke, P.K. Sorger, S. Gaudet, and H. Kim, “Systematic calibration of a cell signaling network model”, BMC Bioinformatics 11, 202 (2010).
  • [11] A. Marin-Sanguino, S.K. Gupta, E.O. Voit, and J. Vera, “Biochemical pathway modeling tools for drug target detection in cancer and other complex diseases”, Methods in Enzymology 487, 319-369 (2011).
  • [12] N.A.W. van Riel, “Dynamic modelling and analysis of biochemical networks: mechanism-based models and model-based experiments”, Briefings in Bioinformatics 7 (4), 364-374 (2006).
  • [13] J.J. Cruz, Feedback Systems, McGraw-Hill, New York, 1972.
  • [14] A. Saltelli, Sensitivity Analysis in Practice: a Guide to Assessing Scientific Models, John Wiley & Sons, London, 2004.
  • [15] J. Leis and M. Kramer, “Sensitivity analysis of systems of differential and algebraic equations”, Computers & Chemical Engineering 9, 93--96 (1985).
  • [16] I.Gy. Zsely, J. Zador, and T. Turanyi, “Similarity of sensitivity functions of reaction kinetic models”, J. Physical Chemistry A 107, 2216-2238 (2003).
  • [17] H. Yue, M. Brown, J. Knowles, H. Wang, D.S. Broomhead, and D.B. Kell, “Insights into the behaviour of systems biology models from dynamic sensitivity and identifiability analysis: a case study of an nf-kappab signalling pathway”, Molecular BioSystems 2 (12), 640-649 (2006).
  • [18] F. Campolongo, J. Cariboni, and A. Saltelli, “An effective screening design for sensitivity analysis of large models”, Environmental Modelling & Software 22, 1509-1518 (2007).
  • [19] M.D. Morris, “Factorial sampling plans for preliminary computational experiments”, Technometrics 33 (2), 161-174 (1991).
  • [20] A. Saltelli, Global Sensitivity Analysis: the primer, John Wiley & Sons, London, 2008.
  • [21] Z. Zi, K.-H. Chob, M.-H. Sung, X. Xia, J. Zheng, and Z. Sun, “In silico identification of the key components and steps in IFN- induced JAK-STAT signaling pathway”, FEBS Letters 579, 1101-1108 (2005).
  • [22] M. Bentele, I. Lavrik, M. Ulrich, S. Stosser, D.W. Heermann, H. Kalthoff, P.H. Krammer, and R. Eils. “Mathematical modeling reveals threshold mechanism in cd95-induced apoptosis”, J. Cell Biology 166 (6), 839-851 (2004).
  • [23] M. Rathinam, P.W. Sheppard, and M. Khammash, “Efficient computation of parameter sensitivities of discrete stochastic chemical reaction networks”, J. Chemical Physics 132 (3), 034103 (2010).
  • [24] I. Sobol, “Global sensitivity indices for nonlinear mathematical models and their monte carlo estimates”, Mathematics and Computers in Simulation 55, 271-280 (2001).
  • [25] B. Hat, K. Puszyński and T. Lipniacki, “Exploring mechanisms of oscillations in p53 and nuclear factor-kB systems”, IET Systems Biology 3 (5), 342-355 (2009).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPG8-0096-0010
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