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Volumn 10, Issue 2, 2009, Pages 122-133

Stochastic modelling for quantitative description of heterogeneous biological systems

Author keywords

[No Author keywords available]

Indexed keywords

PROTEIN MDM2; PROTEIN P53;

EID: 58549110252     PISSN: 14710056     EISSN: 14710064     Source Type: Journal    
DOI: 10.1038/nrg2509     Document Type: Review
Times cited : (454)

References (117)
  • 1
    • 0037079054 scopus 로고    scopus 로고
    • Computational systems biology
    • Kitano, H. Computational systems biology. Nature 420, 206-210 (2002).
    • (2002) Nature , vol.420 , pp. 206-210
    • Kitano, H.1
  • 2
    • 0033083733 scopus 로고    scopus 로고
    • It's a noisy business: Genetic regulation at the nan omolecular scale
    • McAdams, H. H. and Arkin, A. It's a noisy business: genetic regulation at the nan omolecular scale. Trends Genet. 15, 65-69 (1999).
    • (1999) Trends Genet , vol.15 , pp. 65-69
    • McAdams, H.H.1    Arkin, A.2
  • 4
    • 0021616838 scopus 로고
    • UV irradiation stimulates levels of p53 cellular tumor antigen in nontransformed mouse cells
    • Maltzman, W. & Czyzyk, L. UV irradiation stimulates levels of p53 cellular tumor antigen in nontransformed mouse cells. Mol. Cell. Biol. 4, 1689-1694 (1984).
    • (1984) Mol. Cell. Biol , vol.4 , pp. 1689-1694
    • Maltzman, W.1    Czyzyk, L.2
  • 5
    • 0034633702 scopus 로고    scopus 로고
    • Lev Bar-Or, R. et al. Generation of oscillations by the p53-mdm2 feedback loop: A theoretical and experimental study. Proc. Natl Acad. Sci. USA 97, 11250-11255 (2000).
    • Lev Bar-Or, R. et al. Generation of oscillations by the p53-mdm2 feedback loop: A theoretical and experimental study. Proc. Natl Acad. Sci. USA 97, 11250-11255 (2000).
  • 6
    • 0842332406 scopus 로고    scopus 로고
    • Dynamics of the p53-mdm2 feedback loop in individual cells
    • Lahav, G. et al. Dynamics of the p53-mdm2 feedback loop in individual cells. Nature Genet. 36, 147-150 (2004).
    • (2004) Nature Genet , vol.36 , pp. 147-150
    • Lahav, G.1
  • 7
    • 33745451921 scopus 로고    scopus 로고
    • Geva-Zatorsky, N. et al. Oscillations and variability in the p53 system. Mol. Syst. Biol. 2, 2006.0033 (2006).
    • Geva-Zatorsky, N. et al. Oscillations and variability in the p53 system. Mol. Syst. Biol. 2, 2006.0033 (2006).
  • 8
    • 0030905284 scopus 로고    scopus 로고
    • Mdm2 promotes the rapid degradation of p53
    • Haupt, Y., Maya, R., Kazaz, A. & Oren, M. Mdm2 promotes the rapid degradation of p53. Nature 387, 296-299 (1997).
    • (1997) Nature , vol.387 , pp. 296-299
    • Haupt, Y.1    Maya, R.2    Kazaz, A.3    Oren, M.4
  • 9
    • 40549099702 scopus 로고    scopus 로고
    • Unlocking the mdm2-p53 loop: Ubiquitin is the key
    • Clegg, H. V., Itahana, K. & Zhang, Y. Unlocking the mdm2-p53 loop: ubiquitin is the key. Cell Cycle 7, 287-292 (2008).
    • (2008) Cell Cycle , vol.7 , pp. 287-292
    • Clegg, H.V.1    Itahana, K.2    Zhang, Y.3
  • 10
    • 0037342537 scopus 로고    scopus 로고
    • The systems biology markup language (SBML): A medium for representation and exchange of biochemical network models
    • 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).
    • (2003) Bioinformatics , vol.19 , pp. 524-531
    • Hucka, M.1
  • 12
    • 26444583906 scopus 로고    scopus 로고
    • A plausible model for the digital response of p53 to DNA damage
    • Ma, L. et al. A plausible model for the digital response of p53 to DNA damage. Proc. Natl Acad. Sci. USA 102, 14266-14271 (2005).
    • (2005) Proc. Natl Acad. Sci. USA , vol.102 , pp. 14266-14271
    • Ma, L.1
  • 13
    • 34248146626 scopus 로고    scopus 로고
    • A dynamical model of DNA-damage derived p53-mdm2 interaction
    • Zhang, L. J., Yan, S. W. & Zhuo, Y. Z. A dynamical model of DNA-damage derived p53-mdm2 interaction. Acta Physica Sinica 56, 2442-2447 (2007).
    • (2007) Acta Physica Sinica , vol.56 , pp. 2442-2447
    • Zhang, L.J.1    Yan, S.W.2    Zhuo, Y.Z.3
  • 14
    • 53149144701 scopus 로고    scopus 로고
    • Proctor, C. J. & Gray, D. A. Explaining oscillations and variability in the p53-mdm2 system. BMC Syst. Biol. 2, 75 (2008).
    • Proctor, C. J. & Gray, D. A. Explaining oscillations and variability in the p53-mdm2 system. BMC Syst. Biol. 2, 75 (2008).
  • 16
    • 33745375673 scopus 로고    scopus 로고
    • Bahcall, O. G. Single cell resolution in regulation of gene expression. Mol. Syst. Biol. 1, 2005.0015 (2005).
    • Bahcall, O. G. Single cell resolution in regulation of gene expression. Mol. Syst. Biol. 1, 2005.0015 (2005).
  • 17
    • 34347260600 scopus 로고    scopus 로고
    • Living with noisy genes: How cells function reliably with inherent variability in gene expression
    • Maheshri, N. & O'Shea, E. K. Living with noisy genes: how cells function reliably with inherent variability in gene expression. Annu. Rev. Biophys. Biomol. Struct. 36, 413-434 (2007).
    • (2007) Annu. Rev. Biophys. Biomol. Struct , vol.36 , pp. 413-434
    • Maheshri, N.1    O'Shea, E.K.2
  • 18
    • 42949131308 scopus 로고    scopus 로고
    • Selection to minimise noise in living systems and its implications for the evolution of gene expression
    • Lehner, B. Selection to minimise noise in living systems and its implications for the evolution of gene expression. Mol. Syst. Biol. 4, 170 (2008).
    • (2008) Mol. Syst. Biol , vol.4 , pp. 170
    • Lehner, B.1
  • 19
    • 43249103636 scopus 로고    scopus 로고
    • Ansel, J. Cell-to-cell stochastic variation in gene expression is a complex genetic trait. PLoS Genet. 4, e1000049 (2008).
    • Ansel, J. Cell-to-cell stochastic variation in gene expression is a complex genetic trait. PLoS Genet. 4, e1000049 (2008).
  • 20
    • 25444468618 scopus 로고    scopus 로고
    • Noise in gene expression: Origins, consequences, and control
    • Raser, J. M. & O'Shea, E. K. Noise in gene expression: origins, consequences, and control. Science 309, 2010-2013 (2005).
    • (2005) Science , vol.309 , pp. 2010-2013
    • Raser, J.M.1    O'Shea, E.K.2
  • 21
    • 47649125318 scopus 로고    scopus 로고
    • Tuning gene expression to changing environments: From rapid responses to evolutionary adaptation
    • Lopez-Maury, L., Marguerat, S. & Bahler, J. Tuning gene expression to changing environments: from rapid responses to evolutionary adaptation. Nature Rev. Genet. 9, 583-593 (2008).
    • (2008) Nature Rev. Genet , vol.9 , pp. 583-593
    • Lopez-Maury, L.1    Marguerat, S.2    Bahler, J.3
  • 26
    • 33645429016 scopus 로고
    • Exact stochastic simulation of coupled chemical reactions
    • The original description of the stochastic simulation algorithm for discrete event simulation of biochemical reaction networks
    • Gillespie, D. T. Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81, 2340-2361 (1977). The original description of the stochastic simulation algorithm for discrete event simulation of biochemical reaction networks.
    • (1977) J. Phys. Chem , vol.81 , pp. 2340-2361
    • Gillespie, D.T.1
  • 27
    • 0031029852 scopus 로고    scopus 로고
    • Stochastic mechanisms in gene expression
    • McAdams, H. H. & Arkin, A. Stochastic mechanisms in gene expression. Proc. Natl Acad. Sci. USA 94, 814-819 (1997).
    • (1997) Proc. Natl Acad. Sci. USA , vol.94 , pp. 814-819
    • McAdams, H.H.1    Arkin, A.2
  • 28
    • 0032472195 scopus 로고    scopus 로고
    • Quantitation of transcription and clonal selection of single living cells with beta-lactamase as reporter
    • Zlokarnik, G. et al. Quantitation of transcription and clonal selection of single living cells with beta-lactamase as reporter. Science 279, 84-88 (1998).
    • (1998) Science , vol.279 , pp. 84-88
    • Zlokarnik, G.1
  • 30
    • 38949164415 scopus 로고    scopus 로고
    • Algorithms and software for stochastic simulation of biochemical reacting systems
    • Li, H., Cao, Y., Petzold, L. R. & Gillespie, D. T. Algorithms and software for stochastic simulation of biochemical reacting systems. Biotechnol. Prog. 24, 56-61 (2007).
    • (2007) Biotechnol. Prog , vol.24 , pp. 56-61
    • Li, H.1    Cao, Y.2    Petzold, L.R.3    Gillespie, D.T.4
  • 31
    • 44949114109 scopus 로고    scopus 로고
    • Modeling and simulating chemical reactions
    • Higham, D. J. Modeling and simulating chemical reactions. SIAM Rev. 50, 347-368 (2008).
    • (2008) SIAM Rev , vol.50 , pp. 347-368
    • Higham, D.J.1
  • 32
    • 0034691214 scopus 로고    scopus 로고
    • Stochastic focusing: Fluctuation-enhanced sensitivity of intracellular regulation
    • Paulsson, J., Berg, O. & Ehrenberg, M. Stochastic focusing: fluctuation-enhanced sensitivity of intracellular regulation. Proc. Natl. Acad. Sci. USA 97, 7148-7153 (2000).
    • (2000) Proc. Natl. Acad. Sci. USA , vol.97 , pp. 7148-7153
    • Paulsson, J.1    Berg, O.2    Ehrenberg, M.3
  • 34
    • 33645027986 scopus 로고    scopus 로고
    • Stochastic protein expression in individual cells at the single molecule level
    • Cai, L., Friedman, N. & Xie, X. S. Stochastic protein expression in individual cells at the single molecule level. Nature 440, 358-362 (2006).
    • (2006) Nature , vol.440 , pp. 358-362
    • Cai, L.1    Friedman, N.2    Xie, X.S.3
  • 35
    • 0031879114 scopus 로고    scopus 로고
    • Stochastic kinetic analysis of developmental pathway bifurcation in phage λ-infected Escherichia coli cells
    • An important early example illustrating that stochastic kinetic models can describe important biological phenomena that cannot easily be understood using continuous deterministic models
    • Arkin, A., Ross, J. & McAdams, H. H. Stochastic kinetic analysis of developmental pathway bifurcation in phage λ-infected Escherichia coli cells. Genetics 149, 1633-1648 (1998). An important early example illustrating that stochastic kinetic models can describe important biological phenomena that cannot easily be understood using continuous deterministic models.
    • (1998) Genetics , vol.149 , pp. 1633-1648
    • Arkin, A.1    Ross, J.2    McAdams, H.H.3
  • 36
    • 43249118966 scopus 로고    scopus 로고
    • Colored extrinsic fluctuations and stochastic gene expression
    • Shahrezaei, V., Ollivier, J. and Swain, P. Colored extrinsic fluctuations and stochastic gene expression. Mol. Syst. Biol. 4, 196 (2008).
    • (2008) Mol. Syst. Biol , vol.4 , pp. 196
    • Shahrezaei, V.1    Ollivier, J.2    Swain, P.3
  • 37
    • 0001144902 scopus 로고    scopus 로고
    • Efficient exact stochastic simulation of chemical systems with many species and many channels
    • Gibson, M. A. & Bruck, J. Efficient exact stochastic simulation of chemical systems with many species and many channels. J. Phys. Chem. A 104, 1876-1889 (2000).
    • (2000) J. Phys. Chem. A , vol.104 , pp. 1876-1889
    • Gibson, M.A.1    Bruck, J.2
  • 38
    • 0035933994 scopus 로고    scopus 로고
    • Approximate accelerated stochastic simulation of chemically reacting systems
    • Gillespie, D. T. Approximate accelerated stochastic simulation of chemically reacting systems. J. Chem. Phys. 115, 1716-1732 (2001).
    • (2001) J. Chem. Phys , vol.115 , pp. 1716-1732
    • Gillespie, D.T.1
  • 39
    • 0242425970 scopus 로고    scopus 로고
    • Improved leap-size selection for accelerated stochastic simulation
    • Gillespie, D. T. & Petzold, L. R. Improved leap-size selection for accelerated stochastic simulation. J. Chem. Phys. 119, 8229-8234 (2003).
    • (2003) J. Chem. Phys , vol.119 , pp. 8229-8234
    • Gillespie, D.T.1    Petzold, L.R.2
  • 41
    • 33749511375 scopus 로고    scopus 로고
    • Adaptive simulation of hybrid stochastic and deterministic models for biochemical systems
    • Alfonsi, A., Cances, E., Turinici, G., di Ventura, B. & Huisinga, W. Adaptive simulation of hybrid stochastic and deterministic models for biochemical systems. ESAIM: Proc. 14, 1-13 (2005).
    • (2005) ESAIM: Proc , vol.14 , pp. 1-13
    • Alfonsi, A.1    Cances, E.2    Turinici, G.3    di Ventura, B.4    Huisinga, W.5
  • 42
    • 1542345686 scopus 로고    scopus 로고
    • Bridging the gap between stochastic and deterministic regimes in the kinetic simulations of the biochemical reaction networks
    • Puchalka, J. & Kierzek, A. M. Bridging the gap between stochastic and deterministic regimes in the kinetic simulations of the biochemical reaction networks. Biophys. J. 86, 1357-1372 (2004).
    • (2004) Biophys. J , vol.86 , pp. 1357-1372
    • Puchalka, J.1    Kierzek, A.M.2
  • 43
    • 0037444724 scopus 로고    scopus 로고
    • Stochastic chemical kinetics and the quasi-steady-state assumption: Application to the Gillespie algorithm
    • Rao, C. V. & Arkin, A. P. Stochastic chemical kinetics and the quasi-steady-state assumption: application to the Gillespie algorithm. J. Chem. Phys. 118, 4999-5010 (2003).
    • (2003) J. Chem. Phys , vol.118 , pp. 4999-5010
    • Rao, C.V.1    Arkin, A.P.2
  • 44
    • 0037109565 scopus 로고    scopus 로고
    • Approximate simulation of coupled fast and slow reactions for stochastic chemical kinetics
    • Haseltine, E. L. & Rawlings, J. B. Approximate simulation of coupled fast and slow reactions for stochastic chemical kinetics. J. Chem. Phys.117, 6959-6969 (2002).
    • (2002) J. Chem. Phys , vol.117 , pp. 6959-6969
    • Haseltine, E.L.1    Rawlings, J.B.2
  • 45
    • 22944451159 scopus 로고    scopus 로고
    • Accurate hybrid stochastic simulation of a system of coupled chemical or biochemical reactions
    • Salis, H. & Kaznessis, Y. Accurate hybrid stochastic simulation of a system of coupled chemical or biochemical reactions. J. Chem. Phys. 122, 054103 (2005).
    • (2005) J. Chem. Phys , vol.122 , pp. 054103
    • Salis, H.1    Kaznessis, Y.2
  • 46
    • 18144386494 scopus 로고    scopus 로고
    • Multiscale stochastic simulation algorithm with stochastic partial equilibrium assumption for chemically reacting systems
    • Cao, Y., Gillespie, D. T. & Petzold, L. Multiscale stochastic simulation algorithm with stochastic partial equilibrium assumption for chemically reacting systems. J. Comput. Phys. 206, 395-411 (2005).
    • (2005) J. Comput. Phys , vol.206 , pp. 395-411
    • Cao, Y.1    Gillespie, D.T.2    Petzold, L.3
  • 47
    • 26944450876 scopus 로고    scopus 로고
    • Overcoming stiffness in stochastic simulation stemming from partial equilibrium: A multiscale Monte Carlo algorithm
    • Samant, A. & Vlachos, D. G. Overcoming stiffness in stochastic simulation stemming from partial equilibrium: a multiscale Monte Carlo algorithm. J. Chem. Phys. 123, 144114 (2005).
    • (2005) J. Chem. Phys , vol.123 , pp. 144114
    • Samant, A.1    Vlachos, D.G.2
  • 48
    • 27744516835 scopus 로고    scopus 로고
    • Nested stochastic simulation algorithm for chemical kinetic systems with disparate rates
    • Weinan, E., Liu, D. & Vanden-Eijnden, E. Nested stochastic simulation algorithm for chemical kinetic systems with disparate rates. J. Chem. Phys. 123, 194107 (2005).
    • (2005) J. Chem. Phys , vol.123 , pp. 194107
    • Weinan, E.1    Liu, D.2    Vanden-Eijnden, E.3
  • 49
    • 33846123314 scopus 로고    scopus 로고
    • Nested stochastic simulation algorithms for chemical kinetic systems with multiple time scales
    • Weinan, E., Liu, D. & Vanden-Eijnden, E. Nested stochastic simulation algorithms for chemical kinetic systems with multiple time scales. J. Comput. Phys. 221, 158-180 (2007).
    • (2007) J. Comput. Phys , vol.221 , pp. 158-180
    • Weinan, E.1    Liu, D.2    Vanden-Eijnden, E.3
  • 50
    • 0034225547 scopus 로고    scopus 로고
    • The chemical Langevin equation
    • A well presented and accessible introduction to the chemical Langevin equation
    • Gillespie, D. T. The chemical Langevin equation. J. Chem. Phys. 113, 297-306 (2000). A well presented and accessible introduction to the chemical Langevin equation.
    • (2000) J. Chem. Phys , vol.113 , pp. 297-306
    • Gillespie, D.T.1
  • 53
    • 0036790975 scopus 로고    scopus 로고
    • Intrinsic and extrinsic contributions to stochasticity in gene expression
    • Swain, P. S., Elowitz, M. B. & Siggia, E. D. Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc. Natl Acad. Sci. USA 99, 12795-12800 (2002).
    • (2002) Proc. Natl Acad. Sci. USA , vol.99 , pp. 12795-12800
    • Swain, P.S.1    Elowitz, M.B.2    Siggia, E.D.3
  • 54
    • 58549094860 scopus 로고    scopus 로고
    • A mathematical model of ageing in yeast
    • Gillespie, C. S. et al. A mathematical model of ageing in yeast. J. Theor. Biol. 44, 493-516 (2004).
    • (2004) J. Theor. Biol , vol.44 , pp. 493-516
    • Gillespie, C.S.1
  • 55
    • 50949119269 scopus 로고    scopus 로고
    • Tanase-Nicola, S. & ten Wolde, P. R. Regulatory control and the costs and benefits of biochemical noise. PLoS Comput. Biol. 4, e1000125 (2008).
    • Tanase-Nicola, S. & ten Wolde, P. R. Regulatory control and the costs and benefits of biochemical noise. PLoS Comput. Biol. 4, e1000125 (2008).
  • 60
    • 34447534987 scopus 로고    scopus 로고
    • Vanucci, M, Do, K.-A. & Muller, P, eds, Cambridge Univ. Press, New York
    • Vanucci, M., Do, K.-A. & Muller, P. (eds) Bayesian Inference for Gene Expression and Proteomics (Cambridge Univ. Press, New York 2006).
    • (2006) Bayesian Inference for Gene Expression and Proteomics
  • 61
    • 24644473482 scopus 로고    scopus 로고
    • Hein, A.-M. K., Richardson, S., Causton, H. C., Ambler, G. K. & Green, P. J. BGX: a fully Bayesian integrated approach to the analysis of Affymetrix Gene Chip data. Biostatistics 6, 349-373 (2005).
    • Hein, A.-M. K., Richardson, S., Causton, H. C., Ambler, G. K. & Green, P. J. BGX: a fully Bayesian integrated approach to the analysis of Affymetrix Gene Chip data. Biostatistics 6, 349-373 (2005).
  • 63
    • 0842288337 scopus 로고    scopus 로고
    • Inferring cellular networks using probabilistic graphical models
    • Friedman, N. Inferring cellular networks using probabilistic graphical models. Science 303, 799-805 (2004).
    • (2004) Science , vol.303 , pp. 799-805
    • Friedman, N.1
  • 64
    • 10244230983 scopus 로고    scopus 로고
    • Reconstruction of gene networks using Bayesian learning and manipulation experiments
    • Pournara, I. & Wernisch, L. Reconstruction of gene networks using Bayesian learning and manipulation experiments. Bioinformatics 20, 2934-2942 (2004).
    • (2004) Bioinformatics , vol.20 , pp. 2934-2942
    • Pournara, I.1    Wernisch, L.2
  • 65
    • 13844253637 scopus 로고    scopus 로고
    • A Bayesian approach to reconstructing genetic regulatory networks with hidden factors
    • Beal, M. J., Falciani, F., Ghahramani, Z., Rangel, C. & Wild, D. L. A Bayesian approach to reconstructing genetic regulatory networks with hidden factors. Bioinformatics 21, 349-356, (2005).
    • (2005) Bioinformatics , vol.21 , pp. 349-356
    • Beal, M.J.1    Falciani, F.2    Ghahramani, Z.3    Rangel, C.4    Wild, D.L.5
  • 66
    • 15944364151 scopus 로고    scopus 로고
    • An empirical Bayes approach to inferring large-scale gene association networks
    • Schafer, J. & Strimmer, K. An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21, 754-764 (2005).
    • (2005) Bioinformatics , vol.21 , pp. 754-764
    • Schafer, J.1    Strimmer, K.2
  • 67
    • 33749825955 scopus 로고    scopus 로고
    • Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical Gaussian models and Bayesian networks
    • Werhli, A. V., Grzegorczyk, M. & Husmeier, D. Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical Gaussian models and Bayesian networks. Bioinformatics 22, 2523-2531 (2006).
    • (2006) Bioinformatics , vol.22 , pp. 2523-2531
    • Werhli, A.V.1    Grzegorczyk, M.2    Husmeier, D.3
  • 68
    • 15944399178 scopus 로고    scopus 로고
    • Sparse graphical models for exploring gene expression data
    • Dobra, A. et al. Sparse graphical models for exploring gene expression data. J. Multivar. Anal. 90, 196-212 (2004).
    • (2004) J. Multivar. Anal , vol.90 , pp. 196-212
    • Dobra, A.1
  • 69
    • 20144364427 scopus 로고    scopus 로고
    • Experiments in stochastic computation for high-dimensional graphical models
    • Jones, B. et al. Experiments in stochastic computation for high-dimensional graphical models. Stat. Sci. 20, 388-400 (2005).
    • (2005) Stat. Sci , vol.20 , pp. 388-400
    • Jones, B.1
  • 70
    • 0344464762 scopus 로고    scopus 로고
    • Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks
    • Husmeier, D. Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics 19, 2271-2282 (2003).
    • (2003) Bioinformatics , vol.19 , pp. 2271-2282
    • Husmeier, D.1
  • 71
    • 12344259602 scopus 로고    scopus 로고
    • Advances to Bayesian network inference for generating causal networks from observational data
    • Yu, J., Smith, V. A., Wang, P. P., Hartemink, A. J. & Jarvis, E. D. Advances to Bayesian network inference for generating causal networks from observational data. Bioinformatics 20, 3594-3603 (2004).
    • (2004) Bioinformatics , vol.20 , pp. 3594-3603
    • Yu, J.1    Smith, V.A.2    Wang, P.P.3    Hartemink, A.J.4    Jarvis, E.D.5
  • 72
    • 34249862287 scopus 로고    scopus 로고
    • Learning causal networks from systems biology time course data: An effective model selection procedure for the vector autoregressive process
    • The first paper to explore the use of sparse vector autoregressive models for inferring causal genetic regulatory relationships
    • Opgen-Rhein, R. & Strimmer, K. Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregressive process. BMC Bioinformatics 8 (Suppl. 2), S3 (2007). The first paper to explore the use of sparse vector autoregressive models for inferring causal genetic regulatory relationships.
    • (2007) BMC Bioinformatics , vol.8 , Issue.SUPPL. 2
    • Opgen-Rhein, R.1    Strimmer, K.2
  • 73
    • 36148996468 scopus 로고    scopus 로고
    • Bayesian stochastic search for VAR model restrictions
    • George, E., Sun, D. & Ni, S. Bayesian stochastic search for VAR model restrictions. J. Econom. 142, 553-580 (2008).
    • (2008) J. Econom , vol.142 , pp. 553-580
    • George, E.1    Sun, D.2    Ni, S.3
  • 74
    • 33747624926 scopus 로고    scopus 로고
    • High-throughput fluorescence microscopy for systems biology
    • Pepperkok, R. & Ellenberg, J. High-throughput fluorescence microscopy for systems biology. Nature Rev. Mol. Cell Biol. 7, 690-696 (2006).
    • (2006) Nature Rev. Mol. Cell Biol , vol.7 , pp. 690-696
    • Pepperkok, R.1    Ellenberg, J.2
  • 75
    • 33845446475 scopus 로고    scopus 로고
    • Automated tracking of gene expression profiles in individual cells and cell compartments
    • Shen, H. et al. Automated tracking of gene expression profiles in individual cells and cell compartments. J. R. Soc. Interface 3, 787 (2006).
    • (2006) J. R. Soc. Interface , vol.3 , pp. 787
    • Shen, H.1
  • 76
    • 33750374139 scopus 로고    scopus 로고
    • Linking data to models: Data regression
    • Jaqaman, K. & Danuser, G. Linking data to models: data regression. Nature Rev. Mol. Cell Biol. 7, 813-819 (2006).
    • (2006) Nature Rev. Mol. Cell Biol , vol.7 , pp. 813-819
    • Jaqaman, K.1    Danuser, G.2
  • 77
    • 0242574982 scopus 로고    scopus 로고
    • Parameter estimation in biochemical pathways: A comparison of global optimization methods
    • 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).
    • (2003) Genome Res , vol.13 , pp. 2467-2474
    • Moles, C.G.1    Mendes, P.2    Banga, J.R.3
  • 78
    • 42749109054 scopus 로고    scopus 로고
    • Statistical mechanical approaches to models with many poorly known parameters
    • An early example of applying MCMC methods for inferring parameters of continuous deterministic models
    • Brown, K. S. & Sethna, J. P. Statistical mechanical approaches to models with many poorly known parameters. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 68, 021904 (2003). An early example of applying MCMC methods for inferring parameters of continuous deterministic models.
    • (2003) Phys. Rev. E Stat. Nonlin. Soft Matter Phys , vol.68 , pp. 021904
    • Brown, K.S.1    Sethna, J.P.2
  • 79
    • 33745038921 scopus 로고    scopus 로고
    • Ranked prediction of p53 targets using hidden variable dynamic modeling
    • Barenco, M. et al. Ranked prediction of p53 targets using hidden variable dynamic modeling. Genome Biol. 7, R25 (2006).
    • (2006) Genome Biol , vol.7
    • Barenco, M.1
  • 80
    • 40749094910 scopus 로고    scopus 로고
    • Bayesian ranking of biochemical system models
    • Describes the use of MCMC for parameter inference and model selection using deterministic models
    • Vyshemirsky, V. & Girolami, M. Bayesian ranking of biochemical system models. Bioinformatics 24, 833 (2008). Describes the use of MCMC for parameter inference and model selection using deterministic models.
    • (2008) Bioinformatics , vol.24 , pp. 833
    • Vyshemirsky, V.1    Girolami, M.2
  • 81
    • 27244440301 scopus 로고    scopus 로고
    • Liebermeister, W. & Klipp, E. Biochemical networks with uncertain parameters. IEE Syst. Biol. 152, 97-107 (2005).
    • Liebermeister, W. & Klipp, E. Biochemical networks with uncertain parameters. IEE Syst. Biol. 152, 97-107 (2005).
  • 83
    • 77956890234 scopus 로고
    • Carlo sampling methods using Markov chains and their applications
    • Hastings, W. K. Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57, 97-109 (1970).
    • (1970) Biometrika , vol.57 , pp. 97-109
    • Hastings, W.1    Monte, K.2
  • 85
    • 50549100597 scopus 로고    scopus 로고
    • BioBayes: A software package for Bayesian inference in systems biology
    • Vyshemirsky, V. & Girolami, M. BioBayes: a software package for Bayesian inference in systems biology. Bioinformatics 24, 1933-1934 (2008).
    • (2008) Bioinformatics , vol.24 , pp. 1933-1934
    • Vyshemirsky, V.1    Girolami, M.2
  • 86
    • 33746582040 scopus 로고    scopus 로고
    • Reinker, S., Altman, R. M. & Timmer, J. Parameter estimation in stochastic biochemical reactions. IEE Syst. Biol. 153, 168-178 (2006).
    • Reinker, S., Altman, R. M. & Timmer, J. Parameter estimation in stochastic biochemical reactions. IEE Syst. Biol. 153, 168-178 (2006).
  • 87
    • 33845875759 scopus 로고    scopus 로고
    • Simulated maximum likelihood method for estimating kinetic rates in gene expression
    • Tian, T., Xu, S., Gao, J. & Burrage, K. Simulated maximum likelihood method for estimating kinetic rates in gene expression. Bioinformatics 23, 84-91 (2007).
    • (2007) Bioinformatics , vol.23 , pp. 84-91
    • Tian, T.1    Xu, S.2    Gao, J.3    Burrage, K.4
  • 88
    • 41549140160 scopus 로고    scopus 로고
    • Bayesian inference for a discretely observed stochastic kinetic model
    • The first paper to demonstrate the possibility of conducting fully Bayesian inference for the parameters of stochastic kinetic models
    • Boys, R. J., Wilkinson, D. J. & Kirkwood, T. B. L. Bayesian inference for a discretely observed stochastic kinetic model. Stat. Comput. 18, 125-135 (2008). The first paper to demonstrate the possibility of conducting fully Bayesian inference for the parameters of stochastic kinetic models.
    • (2008) Stat. Comput , vol.18 , pp. 125-135
    • Boys, R.J.1    Wilkinson, D.J.2    Kirkwood, T.B.L.3
  • 89
    • 33746907501 scopus 로고    scopus 로고
    • A stochastic model of gene transcription: An application to L1 retrotransposition events
    • Rempala, G. A., Ramos, K. S. & Kalbfleisch, T. A stochastic model of gene transcription: an application to L1 retrotransposition events. J. Theor. Biol. 242, 101-116 (2006).
    • (2006) J. Theor. Biol , vol.242 , pp. 101-116
    • Rempala, G.A.1    Ramos, K.S.2    Kalbfleisch, T.3
  • 91
    • 27744503232 scopus 로고    scopus 로고
    • Bayesian inference for stochastic kinetic models using a diffusion approximation
    • Golightly, A. & Wilkinson, D. J. Bayesian inference for stochastic kinetic models using a diffusion approximation. Biometrics 61, 781-788 (2005).
    • (2005) Biometrics , vol.61 , pp. 781-788
    • Golightly, A.1    Wilkinson, D.J.2
  • 92
    • 35748969565 scopus 로고    scopus 로고
    • Bayesian inference for dynamic transcriptional regulation; the Hes1 system as a case study
    • Heron, E. A., Finkenstadt, B. & Rand, D. A. Bayesian inference for dynamic transcriptional regulation; the Hes1 system as a case study. Bioinformatics 23, 2596-2603 (2007).
    • (2007) Bioinformatics , vol.23 , pp. 2596-2603
    • Heron, E.A.1    Finkenstadt, B.2    Rand, D.A.3
  • 93
    • 33744475347 scopus 로고    scopus 로고
    • Bayesian sequential inference for stochastic kinetic biochemical network models
    • Describes using Bayesian inference for stochastic kinetic models using multiple, partial and noisy experimental data sets
    • Golightly, A. & Wilkinson, D. J. Bayesian sequential inference for stochastic kinetic biochemical network models. J. Comput. Biol. 13, 838-851 (2006). Describes using Bayesian inference for stochastic kinetic models using multiple, partial and noisy experimental data sets.
    • (2006) J. Comput. Biol , vol.13 , pp. 838-851
    • Golightly, A.1    Wilkinson, D.J.2
  • 94
    • 35549009345 scopus 로고    scopus 로고
    • Bayesian inference for nonlinear multivariate diffusion models observed with error
    • Golightly, A. & Wilkinson, D. J. Bayesian inference for nonlinear multivariate diffusion models observed with error. Comput. Stat. Data Anal. 52, 1674-1693 (2008).
    • (2008) Comput. Stat. Data Anal , vol.52 , pp. 1674-1693
    • Golightly, A.1    Wilkinson, D.J.2
  • 95
    • 0035648165 scopus 로고    scopus 로고
    • Bayesian calibration of computer models
    • Kennedy, M. C. & O'Hagan, A. Bayesian calibration of computer models. J. R. Stat. Soc. Ser. B 63, 425-464 (2001).
    • (2001) J. R. Stat. Soc. Ser. B , vol.63 , pp. 425-464
    • Kennedy, M.C.1    O'Hagan, A.2
  • 96
    • 33748848850 scopus 로고    scopus 로고
    • Bayes linear calibrated prediction for complex systems
    • Goldstein, M. & Rougier, J. Bayes linear calibrated prediction for complex systems. J. Am. Stat. Assoc. 101, 1132-114 (2006).
    • (2006) J. Am. Stat. Assoc , vol.101 , pp. 1132-2114
    • Goldstein, M.1    Rougier, J.2
  • 97
    • 33846929503 scopus 로고    scopus 로고
    • Schellnhuber, H. J, Cramer, W, Nakicenovic, N, Wigley, T. & Yohe, G. eds, Cambridge Univ. Press
    • Challenor, P. G., Hankin, R. K. S. & Marsh, R. in Avoiding Dangerous Climate Change (Schellnhuber, H. J., Cramer, W., Nakicenovic, N., Wigley, T. & Yohe, G. eds) 53-63 (Cambridge Univ. Press, 2006).
    • (2006) Avoiding Dangerous Climate Change , pp. 53-63
    • Challenor, P.G.1    Hankin, R.K.S.2    Marsh, R.3
  • 98
    • 58549087688 scopus 로고    scopus 로고
    • Henderson, D. A., Boys, R. J., Krishnan, K. J., Lawless, C. & Wilkinson, D. J. Bayesian emulation and calibration of a stochastic computer model of mitochondrial DNA deletions in substantia nigra neurons. J. Am. Stat. Assoc. (in the press). The first example of using inference for a single-cell model based on cell population data and a statistical emulator of a stochastic cell population model.
    • Henderson, D. A., Boys, R. J., Krishnan, K. J., Lawless, C. & Wilkinson, D. J. Bayesian emulation and calibration of a stochastic computer model of mitochondrial DNA deletions in substantia nigra neurons. J. Am. Stat. Assoc. (in the press). The first example of using inference for a single-cell model based on cell population data and a statistical emulator of a stochastic cell population model.
  • 99
    • 36149005118 scopus 로고
    • On the theory of Brownian motion
    • Uhlenbeck, G. E. & Ornstein, L. S. On the theory of Brownian motion. Phys. Rev. 36, 823-841 (1930).
    • (1930) Phys. Rev , vol.36 , pp. 823-841
    • Uhlenbeck, G.E.1    Ornstein, L.S.2
  • 100
    • 33847770912 scopus 로고    scopus 로고
    • A probabilistic model for cell cycle distributions in synchrony experiments
    • Orlando, D. et al. A probabilistic model for cell cycle distributions in synchrony experiments. Cell Cycle 6, 478-488 (2007).
    • (2007) Cell Cycle , vol.6 , pp. 478-488
    • Orlando, D.1
  • 101
    • 45149097650 scopus 로고    scopus 로고
    • Global control of cell-cycle transcription by coupled CDK and network oscillators
    • Orlando, D. et al. Global control of cell-cycle transcription by coupled CDK and network oscillators. Nature 453, 944-947 (2008).
    • (2008) Nature , vol.453 , pp. 944-947
    • Orlando, D.1
  • 102
    • 1842532350 scopus 로고    scopus 로고
    • The Bayesian revolution in genetics
    • Beaumont, M. A. & Rannala, B. The Bayesian revolution in genetics. Nature Rev. Genet. 5, 251-261 (2004).
    • (2004) Nature Rev. Genet , vol.5 , pp. 251-261
    • Beaumont, M.A.1    Rannala, B.2
  • 103
    • 34250658998 scopus 로고    scopus 로고
    • Bayesian methods in bioinformatics and computational systems biology
    • Wilkinson, D. J. Bayesian methods in bioinformatics and computational systems biology. Brief. Bioinformatics 8, 109-116 (2007).
    • (2007) Brief. Bioinformatics , vol.8 , pp. 109-116
    • Wilkinson, D.J.1
  • 104
    • 36749100036 scopus 로고    scopus 로고
    • Molecular level stochastic model for competence cycles in Bacillus subtilis
    • Schultz, D., Jacob, E. B., Onuchic, J. N. & Wolynes, P. G. Molecular level stochastic model for competence cycles in Bacillus subtilis. Proc. Natl Acad. Sci. USA 104, 17582-17587 (2007).
    • (2007) Proc. Natl Acad. Sci. USA , vol.104 , pp. 17582-17587
    • Schultz, D.1    Jacob, E.B.2    Onuchic, J.N.3    Wolynes, P.G.4
  • 105
    • 17644414919 scopus 로고    scopus 로고
    • Stripping Bacillus: ComK auto-stimulation is responsible for the bistable response in competence development
    • Smits, W. K. et al. Stripping Bacillus: ComK auto-stimulation is responsible for the bistable response in competence development. Mol. Microbiol. 56, 604-614 (2005).
    • (2005) Mol. Microbiol , vol.56 , pp. 604-614
    • Smits, W.K.1
  • 106
    • 20344374152 scopus 로고    scopus 로고
    • Phosphatases modulate the bistable sporulation gene expression pattern in Bacillus subtilis
    • Veening, J.-W., Hamoen, L. W. & Kuipers, O. P. Phosphatases modulate the bistable sporulation gene expression pattern in Bacillus subtilis. Mol. Microbiol. 56, 1481-1494 (2005).
    • (2005) Mol. Microbiol , vol.56 , pp. 1481-1494
    • Veening, J.-W.1    Hamoen, L.W.2    Kuipers, O.P.3
  • 107
    • 42149139936 scopus 로고    scopus 로고
    • Transient heterogeneity in extracellular protease production by Bacillus subtilis
    • Veening, J.-W. et al. Transient heterogeneity in extracellular protease production by Bacillus subtilis. Mol. Syst. Biol. 4, 184 (2008).
    • (2008) Mol. Syst. Biol , vol.4 , pp. 184
    • Veening, J.-W.1
  • 108
    • 0038399793 scopus 로고    scopus 로고
    • A spatially extended stochastic model of the bacterial chemotaxis signalling pathway
    • Shimizu, T. S., Aksenov, S. V. & Bray, D. A spatially extended stochastic model of the bacterial chemotaxis signalling pathway. J. Mol. Biol. 329, 291-309 (2003).
    • (2003) J. Mol. Biol , vol.329 , pp. 291-309
    • Shimizu, T.S.1    Aksenov, S.V.2    Bray, D.3
  • 110
    • 33745287724 scopus 로고    scopus 로고
    • Noise in protein expression scales with natural protein abundance
    • Bar-Even, A. et al. Noise in protein expression scales with natural protein abundance. Nature Genet. 38, 636-643 (2006).
    • (2006) Nature Genet , vol.38 , pp. 636-643
    • Bar-Even, A.1
  • 111
    • 33745220278 scopus 로고    scopus 로고
    • Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise
    • Newman, J. et al. Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature 441, 840-846 (2006).
    • (2006) Nature , vol.441 , pp. 840-846
    • Newman, J.1
  • 112
    • 0037337990 scopus 로고    scopus 로고
    • Towards an e-biology of ageing: Integrating theory and data
    • Kirkwood, T. B. L. et al. Towards an e-biology of ageing: integrating theory and data. Nature Rev. Mol. Cell Biol. 4, 243-249 (2003).
    • (2003) Nature Rev. Mol. Cell Biol , vol.4 , pp. 243-249
    • Kirkwood, T.B.L.1
  • 113
    • 84873991116 scopus 로고    scopus 로고
    • 6th edn eds Masoro, E. J. & Austad, S. N, Academic, New York
    • Kirkwood, T. B. L. et al. in Handbook of the Biology of Aging 6th edn (eds Masoro, E. J. & Austad, S. N.) 334-357 (Academic, New York, 2005).
    • (2005) Handbook of the Biology of Aging , pp. 334-357
    • Kirkwood, T.B.L.1
  • 114
    • 33846851766 scopus 로고    scopus 로고
    • Modelling the checkpoint response to telomere uncapping in budding yeast
    • Proctor, C. J. et al. Modelling the checkpoint response to telomere uncapping in budding yeast. J. R. Soc. Interface 4, 73-90 (2007).
    • (2007) J. R. Soc. Interface , vol.4 , pp. 73-90
    • Proctor, C.J.1
  • 115
    • 10944269121 scopus 로고    scopus 로고
    • Modelling the action of chaperones and their role in ageing
    • Proctor, C. J. et al. Modelling the action of chaperones and their role in ageing. Mech. Ageing Dev. 126, 119-131 (2005).
    • (2005) Mech. Ageing Dev , vol.126 , pp. 119-131
    • Proctor, C.J.1
  • 116
    • 0028271046 scopus 로고
    • Towards a network theory of ageing: A model combining the free radical theory and the protein error theory
    • Kowald, A. & Kirkwood, T. B. Towards a network theory of ageing: a model combining the free radical theory and the protein error theory. J. Theor. Biol. 168, 75-94 (1994).
    • (1994) J. Theor. Biol , vol.168 , pp. 75-94
    • Kowald, A.1    Kirkwood, T.B.2
  • 117
    • 0035930836 scopus 로고    scopus 로고
    • A stochastic model of cell replicative senescence based on telomere shortening, oxidative stress, and somatic mutations in nuclear and mitochondrial DNA
    • de Sozou, P. & Kirkwood, T. B. L. A stochastic model of cell replicative senescence based on telomere shortening, oxidative stress, and somatic mutations in nuclear and mitochondrial DNA. J. Theor. Biol. 213, 573 (2001).
    • (2001) J. Theor. Biol , vol.213 , pp. 573
    • de Sozou, P.1    Kirkwood, T.B.L.2


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