Abstract
Sensitivity Analysis (SA) provides techniques which can be used to identify the parameters which have the greatest influence on the results obtained from a model. Classical SA methods apply to deterministic simulations of ODE models. We extend these to stochastic simulations and consider the analysis of models with bifurcation points and bistable behaviour. We consider local, global and screening SA methods applied to multiple runs of Gillespie’s Stochastic Simulation Algorithm (SSA) . We present an example of stochastic sensitivity analysis of a real pathway, the MAPK signalling pathway.
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References
Gillespie, D.T.: Exact stochastic simulation of coupled chemical reactions. Journal of Physical Chemistry 81(25), 2340–2361 (1977)
Cao, Y., Petzold, L.: Accuracy limitations and the measurement of errors in the stochastic simulation of chemically reacting systems. J. Comput. Phys. 212(1), 6–24 (2006)
Saltelli, A., Chan, K., Scott, E.M. (eds.): Sensitivity Analysis. Wiley, Chichester (2000)
Saltelli, A., Ratto, M., Stefano, T., Francesca, C.: Sensitivity analysis for chemical models. Chem Rev 105(7), 2811–2828 (2005)
Gunawan, R., Cao, Y., Petzold, L., Doyle, F.J.: Sensitivity analysis of discrete stochastic systems. Biophys J 88(4), 2530–2540 (2005)
Chan, K., Saltelli, A., Tarantola, S.: Sensitivity analysis of model output: Variance-based methods make the difference. In: Proceedings of the 1997 Winter Simulation Conference, pp. 261–268 (1997)
Ramsey, S., Orrell, D., Bolouri, H.: Dizzy: stochastic simulation of large-scale genetic regulatory networks. J Bioinform Comput Biol 3(2), 415–436 (2005)
Markevich, N.I., Hoek, J.B., Kholodenko, B.N.: Signaling switches and bistability arising from multisite phosphorylation in protein kinase cascades. The Journal of Cell Biology 164, 353–359 (2004)
Klipp, E., Herwig, R., Kowald, A., Wierling, C., Lehrach, H. (eds.): Systems Biology in practice. Wiley-Vch, Chichester (2005)
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Degasperi, A., Gilmore, S. (2008). Sensitivity Analysis of Stochastic Models of Bistable Biochemical Reactions. In: Bernardo, M., Degano, P., Zavattaro, G. (eds) Formal Methods for Computational Systems Biology. SFM 2008. Lecture Notes in Computer Science, vol 5016. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68894-5_1
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DOI: https://doi.org/10.1007/978-3-540-68894-5_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68892-1
Online ISBN: 978-3-540-68894-5
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