Abstract
The instantaneous relative entropy (IRE) and the corresponding instantaneous Fisher information matrix (IFIM) for transient stochastic processes are presented in this paper. These novel tools for sensitivity analysis of stochastic models serve as an extension of the well known relative entropy rate (RER) and the corresponding Fisher information matrix (FIM) that apply to stationary processes. Three cases are studied here, discrete-time Markov chains, continuous-time Markov chains and stochastic differential equations. A biological reaction network is presented as a demonstration numerical example.
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References
Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M, Tarantola S (2008) Global sensitivity analysis. The primer. Wiley, New York
DiStefano III J (2013) Dynamic systems biology modeling and simulation. Elsevier, New York
Glynn PW (1990) Likelihood ratio gradient estimation for stochastic systems. Commun ACM 33(10):75–84
Nakayama M, Goyal A, Glynn PW (1994) Likelihood ratio sensitivity analysis for Markovian models of highly dependable systems. Stoch Models 10:701–717
Plyasunov S, Arkin AP (2007) Efficient stochastic sensitivity analysis of discrete event systems. J Comput Phys 221:724–738
Kim D, Debusschere BJ, Najm HN (2007) Spectral methods for parametric sensitivity in stochastic dynamical systems. Biophys J 92:379–393
Rathinam M, Sheppard PW, Khammash M (2010) Efficient computation of parameter sensitivities of discrete stochastic chemical reaction networks. J Chem Phys 132(1–13):034103
Anderson David F (2012) An efficient finite difference method for parameter sensitivities of continuous-time Markov chains. SIAM J Numer Anal 50(5):2237–2258
Sheppard PW, Rathinam M, Khammash M (2012) A pathwise derivative approach to the computation of parameter sensitivities in discrete stochastic chemical systems. J Chem Phys 136(3):034115
Meskine H, Matera S, Scheffler M, Reuter K, Metiu H (2009) Examination of the concept of degree of rate control by first-principles kinetic Monte Carlo simulations. Surf Sci 603(10–12):1724–1730
Baiesi M, Maes C, Wynants B (2009) Nonequilibrium linear response for Markov dynamics I: jump processes and overdamped diffusions. J Stat Phys 137:1094
Baiesi M, Maes C, Boksenbojm E, Wynants B (2010) Nonequilibrium linear response for markov dynamics, II: Inertial dynamics. J Stat Phys 139:492
Pantazis Y, Katsoulakis M (2013) A relative entropy rate method for path space sensitivity analysis of stationary complex stochastic dynamics. J Chem Phys 138(5):054115
Dupuis P, Katsoulakis MA, Pantazis Y, Plecháč P Sesnitivity bounds and error estimates for stochastic models (in Preparation)
Arampatzis G, Pantazis Y, Katsoulakis MA Accelerated sensitivity analysis in high-dimensional stochastic reaction networks. Submitted to PLoS ONE
Kullback S (1959) Information theory and statistics. Wiley, New York
Cover T, Thomas J (1991) Elements of information theory. Wiley, New York
Kipnis C, Landim C (1999) Scaling limits of interacting particle systems. Springer, Berlin
Avellaneda M, Friedman CA, Holmes R, Samperi DJ (1997) Calibrating volatility surfaces via relative-entropy minimization. Soc Sci Res Netw
Liu HB, Chen W, Sudjianto A (2006) Relative entropy based method for probabilistic sensitivity analysis in engineering design. J Mech Des 128:326–336
Limnios N, Oprisan G (2001) Semi-Markov processes and reliability. Springer, Berlin
Abramov RV, Grote MJ, Majda AJ (2005) Information theory and stochastics for multiscale nonlinear systems., CRM monograph series. American Mathematical Society, Providence
Liptser RS, Shiryaev AN (1977) Statistics of random processes: I & II. Springer, New York
Oksendal B (2000) Stochastic differential equations: an introduction with applications. Springer, New York
Tsourtis A, Pantazis Y, Harmandaris V, Katsoulakis MA Parametric sensitivity analysis for stochastic molecular systems using information theoretic metrics. Submitted to J Chem Phys
Kholodenko BN, Demin OV, Moehren G, Hoek J (1999) Quantification of short term signaling by the epidermal growth factor receptor. J Biol Chem 274(42):30169–30181
Moghal N, Sternberg PW (1999) Multiple positive and negative regulators of signaling by the EGF receptor. Curr Opin Cell Biol 11:190–196
Hackel PO, Zwick E, Prenzel N, Ullrich A (1999) Epidermal growth factor receptors: critical mediators of multiple receptor pathways. Curr Opin Cell Biol 11:184–189
Schoeberl B, Eichler-Jonsson C, Gilles ED, Muller G (2002) Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors. Nat Biotechnol 20:370–375
Casella G, Berger RL (2002) Statistical inference. Duxbury advanced series in statistics and decision sciencesThomson Learning, London
Kay SM (1993) Fundamentals of statistical signal processing: estimation theory. Prentice-Hall, Englewood Cliffs
Acknowledgments
The work of the authors was supported by the Office of Advanced Scientific Computing Research, U.S. Department of Energy, under Contract No. DE-SC0010723 and by the European Union (European Social Fund) and Greece (National Strategic Reference Framework), under the THALES Program, grant AMOSICSS.
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Arampatzis, G., Katsoulakis, M.A., Pantazis, Y. (2015). Pathwise Sensitivity Analysis in Transient Regimes. In: Heinz, S., Bessaih, H. (eds) Stochastic Equations for Complex Systems. Mathematical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-18206-3_5
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DOI: https://doi.org/10.1007/978-3-319-18206-3_5
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