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Physicochemical modelling of cell signalling pathways

Abstract

Physicochemical modelling of signal transduction links fundamental chemical and physical principles, prior knowledge about regulatory pathways, and experimental data of various types to create powerful tools for formalizing and extending traditional molecular and cellular biology.

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Figure 1: The modify–measure–mine–model paradigm in systems biology.
Figure 2: Physicochemical modelling involves a trade off between increasing scope and falling detail.
Figure 3: Steps in physicochemical modelling.
Figure 4: Sensitivity analysis and parameter estimation are context specific.

References

  1. Nurse, P. A long twentieth century of the cell cycle and beyond. Cell 100, 71–78 (2000).

    CAS  Article  Google Scholar 

  2. Papin, J. A., Price, N. D., Wiback, S. J., Fell, D. A. & Palsson, B. O. Metabolic pathways in the post-genome era. Trends Biochem. Sci. 28, 250–258 (2003).

    CAS  Article  Google Scholar 

  3. Reed, J. L., Vo, T. D., Schilling, C. H. & Palsson, B. O. An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol. 4, R54 (2003).

    Article  Google Scholar 

  4. Bhalla, U. S., Ram, P. T. & Iyengar, R. MAP kinase phosphatase as a locus of flexibility in a mitogen-activated protein kinase signaling network. Science 297, 1018–1023 (2002).

    CAS  Article  Google Scholar 

  5. Hoffmann, A., Levchenko, A., Scott, M. L. & Baltimore, D. The IκB–NF-κB signaling module: temporal control and selective gene activation. Science 298, 1241–1245 (2002).

    CAS  Article  Google Scholar 

  6. Huang, C. Y. & Ferrell, J. E., Jr. Ultrasensitivity in the mitogen-activated protein kinase cascade. Proc. Natl Acad. Sci. USA 93, 10078–10083 (1996).

    CAS  Article  Google Scholar 

  7. Markevich, N. I., Hoek, J. B. & Kholodenko, B. N. Signaling switches and bistability arising from multisite phosphorylation in protein kinase cascades. J. Cell Biol. 164, 353–359 (2004).

    CAS  Article  Google Scholar 

  8. Schoeberl, B., Eichler-Jonsson, C., Gilles, E. D. & Muller, G. Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors. Nature Biotechnol. 20, 370–375 (2002).

    Article  Google Scholar 

  9. Oda, K., Matsuoka, Y., Funahashi, A. & Kitano, H. A comprehensive pathway map of epidermal growth factor receptor signaling. Mol. Syst. Biol. 1, 0010 (2005).

    Article  Google Scholar 

  10. Gardiner, C. W. Handbook of Stochastic Processes (Springer, New York, 2005).

    Google Scholar 

  11. Danuser, G. & Waterman-Storer, C. M. Quantitative fluorescent speckle microscopy of cytoskeleton dynamics. Annu. Rev. Biophys. Biomol. Struct. 35, 361–387 (2006).

    CAS  Article  Google Scholar 

  12. Mallavarapu, A. & Mitchison, T. Regulated actin cytoskeleton assembly at filopodium tips controls their extension and retraction. J. Cell Biol. 146, 1097–1106 (1999).

    CAS  Article  Google Scholar 

  13. Odde, D. J. & Buettner, H. M. Time series characterization of simulated microtubule dynamics in the nerve growth cone. Ann. Biomed. Eng. 23, 268–286 (1995).

    CAS  Article  Google Scholar 

  14. Ponti, A., Machacek, M., Gupton, S. L., Waterman-Storer, C. M. & Danuser, G. Two distinct actin networks drive the protrusion of migrating cells. Science 305, 1782–1786 (2004).

    CAS  Article  Google Scholar 

  15. McAdams, H. H. & Arkin, A. Stochastic mechanisms in gene expression. Proc. Natl Acad. Sci. USA 94, 814–819 (1997).

    CAS  Article  Google Scholar 

  16. Paulsson, J. Summing up the noise in gene networks. Nature 427, 415–418 (2004).

    CAS  Article  Google Scholar 

  17. Conzelmann, H., Saez-Rodriguez, J., Sauter, T., Kholodenko, B. N. & Gilles, E. D. A domain-oriented approach to the reduction of combinatorial complexity in signal transduction networks. BMC Bioinformatics 7, 34 (2006).

    Article  Google Scholar 

  18. Blinov, M. L., Faeder, J. R., Goldstein, B. & Hlavacek, W. S. A network model of early events in epidermal growth factor receptor signaling that accounts for combinatorial complexity. Biosystems 83, 136–151 (2006).

    CAS  Article  Google Scholar 

  19. Hlavacek, W. S. et al. Rules for modeling signal-transduction systems. Sci. STKE re6 (2006).

  20. Tolle, D. P. & Le Novere, N. Particle-Based Stochastic Simulation in Systems Biology. Current Bioinformatics 1, 1–6 (2006).

  21. Gardner, T. S., Cantor, C. R. & Collins, J. J. Construction of a genetic toggle switch in Escherichia coli. Nature 403, 339–342 (2000).

    CAS  Article  Google Scholar 

  22. Tyson, J. J., Chen, K. C. & Novak, B. Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell. Curr. Opin. Cell Biol. 15, 221–231 (2003).

    CAS  Article  Google Scholar 

  23. von Dassow, G., Meir, E., Munro, E. M. & Odell, G. M. The segment polarity network is a robust developmental module. Nature 406, 188–192 (2000).

    CAS  Article  Google Scholar 

  24. Conrad, E. D. & Tyson, J. J. in System Modeling in Cellular Biology (eds. Szallasi, Z., Stelling, J. & Periwal, V.) 97–123 (MIT Press, Cambridge, 2006).

    Book  Google Scholar 

  25. Farrow, L. A. & Edelson, D. The steady-state assumption: fact or fiction? Int. J. Chem. Kin. 1, 309–322 (1974).

    Google Scholar 

  26. Flach, E. H. & Schnell, S. Use and abuse of the quasi-steady-state approximation. IEE Proc. Syst. Biol. 153, 187–191 (2006).

    CAS  Article  Google Scholar 

  27. Segel, L. A. On the validity of the steady state assumption of enzyme kinetics. Bull. Math. Biol. 50, 579–593 (1988).

    CAS  Article  Google Scholar 

  28. Balci, O. in Proceedings of the 29th conference on Winter simulation 135–141 (ACH Press, Atlanta, 1997).

    Book  Google Scholar 

  29. Sargent, R. G. in 2005 Proceedings of the Winter Simulation Conference 14 (ACH Press, New York, 2005).

    Google Scholar 

  30. van Riel, N. A. W. & Sontag, E. D. Parametric estimation in models combining signal transduction and metabolic pathways: the dependent input approach. IEE Proc.Syst. Biol. 153, 263–274 (2006).

    CAS  Article  Google Scholar 

  31. Geva-Zatorsky, N. et al. Oscillations and variability in the p53 system. Mol. Syst. Biol. 2, 0033 (2006).

    Article  Google Scholar 

  32. Aldridge, B. B., Haller, G., Sorger, P. K. & Lauffenburger, D. A. Direct Lyaponov exponent analysis enables parametric study of transient signalling governing cell behaviour. IEE Proc. Syst. Biol. 153, (2006).

    Google Scholar 

  33. Bentele, M. et al. Mathematical modeling reveals threshold mechanism in CD95-induced apoptosis. J. Cell Biol. 166, 839–851 (2004).

    CAS  Article  Google Scholar 

  34. Frey, D. & Li, X. in Engineering Systems 2004 Symposium (MIT Engineering Systems Division, Cambridge, 2004).

    Google Scholar 

  35. Wiggins, S. in Introduction to Applied Nonlinear Dynamical Systems and Chaos (eds. Marsden, J. E., Sirovich, L. & Antman, S. S.) 356–xxx (Springer-Verlag, New York, 2003).

    Google Scholar 

  36. Hoppenstaedt, F. C. Analysis and Simulation of Chaotic Systems (Springer-Verlag, New York, 2000).

    Google Scholar 

  37. Hucka, M. et al. The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19, 524–531 (2003).

    CAS  Article  Google Scholar 

  38. Merks, R. M. H. & Glazier, J. A. A cell-centered approach to developmental biology. Physica A 352, 113–130 (2005).

    CAS  Article  Google Scholar 

  39. Dyson, F. A meeting with Enrico Fermi. Nature 427, 297 (2004).

    CAS  Article  Google Scholar 

  40. Kitano, H., Funahashi, A., Matsuoka, Y. & Oda, K. Using process diagrams for the graphical representation of biological networks. Nature Biotechnol. 23, 961–966 (2005).

    CAS  Article  Google Scholar 

  41. Moles, C. G., Mendes, P. & Banga, J. R. Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Res. 13, 2467–2474 (2003).

    CAS  Article  Google Scholar 

  42. Alves, R., Antunes, F. & Salvador, A. Tools for kinetic modeling of biochemical networks. Nature Biotechnol. 24, 667–672 (2006).

    CAS  Article  Google Scholar 

  43. Le Novere, N. et al. Minimum information requested in the annotation of biochemical models (MIRIAM). Nature Biotechnol 23, 1509–1515 (2005).

    CAS  Article  Google Scholar 

  44. Gillespie, D. T. A Rigorous Derivation of the Chemical Master Equation. Physica A 188, 404–425 (1992).

    CAS  Article  Google Scholar 

  45. Roussel, M. R. & Zhu, R. Reducing a chemical master equation by invariant manifold methods. J. Chem. Phys. 121, 8716–8730 (2004).

    CAS  Article  Google Scholar 

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Acknowledgements

We thank G. Danuser, J. Gunawardena, B. Schoeberl and W. Fontana for critical reading of this manuscript. This work was funded by a National Institutes of Health (NIH) grant P50-GM68762 and a Deparment of Energy (DOE) Computational Science Graduate Fellowship to B.B.A. (DE-FG02-97ER25308).

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Aldridge, B., Burke, J., Lauffenburger, D. et al. Physicochemical modelling of cell signalling pathways. Nat Cell Biol 8, 1195–1203 (2006). https://doi.org/10.1038/ncb1497

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