Determination of parameter identifiability in nonlinear biophysical models: A Bayesian approach
暂无分享,去创建一个
[1] Kenneth Levenberg. A METHOD FOR THE SOLUTION OF CERTAIN NON – LINEAR PROBLEMS IN LEAST SQUARES , 1944 .
[2] T Porumb. Determination of calcium-binding constants by flow dialysis. , 1994, Analytical biochemistry.
[3] Kenneth A. Johnson,et al. Global kinetic explorer: a new computer program for dynamic simulation and fitting of kinetic data. , 2009, Analytical biochemistry.
[4] James W. T. Yates,et al. The design and analysis of parallel experiments to produce structurally identifiable models , 2013, Journal of Pharmacokinetics and Pharmacodynamics.
[5] M B Jackson,et al. Single‐Channel Recording , 1998, Current protocols in neuroscience.
[6] J. Falke,et al. Intermolecular tuning of calmodulin by target peptides and proteins: Differential effects on Ca2+ binding and implications for kinase activation , 1997, Protein science : a publication of the Protein Society.
[7] Arild Thowsen,et al. Structural identifiability , 1977, 1977 IEEE Conference on Decision and Control including the 16th Symposium on Adaptive Processes and A Special Symposium on Fuzzy Set Theory and Applications.
[8] K. Glover,et al. Identifiability of linear and nonlinear dynamical systems , 1976 .
[9] M L Johnson,et al. Parameter estimation by least-squares methods. , 1992, Methods in enzymology.
[10] Matthias Rief,et al. Calcium-dependent folding of single calmodulin molecules , 2012, Proceedings of the National Academy of Sciences.
[11] T. Rothenberg. Identification in Parametric Models , 1971 .
[12] Andrew Gelman,et al. Handbook of Markov Chain Monte Carlo , 2011 .
[13] D.G. Dudley,et al. Dynamic system identification experiment design and data analysis , 1979, Proceedings of the IEEE.
[14] T. Lohman,et al. Review of Wyman and Gill, Binding and Linkage: Functional Chemistry of Biological Macromolecules , 1993 .
[15] Jorma Rissanen. Optimal Estimation , 2011, ALT.
[16] D. Marquardt. An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .
[17] M Ikura,et al. Molecular and structural basis of target recognition by calmodulin. , 1995, Annual review of biophysics and biomolecular structure.
[18] M. Straume,et al. Monte Carlo method for determining complete confidence probability distributions of estimated model parameters. , 1992, Methods in enzymology.
[19] Fiona E Müllner,et al. Improved hidden Markov models for molecular motors, part 1: basic theory. , 2010, Biophysical journal.
[20] A. Hill. The Combinations of Haemoglobin with Oxygen and with Carbon Monoxide. I. , 1913, The Biochemical journal.
[21] R. Keynes,et al. Kinetics and steady‐state properties of the charged system controlling sodium conductance in the squid giant axon , 1974, The Journal of physiology.
[22] M. Girolami,et al. Riemann manifold Langevin and Hamiltonian Monte Carlo methods , 2011, Journal of the Royal Statistical Society: Series B (Statistical Methodology).
[23] B Sakmann,et al. Fast events in single‐channel currents activated by acetylcholine and its analogues at the frog muscle end‐plate. , 1985, The Journal of physiology.
[24] R W Wallace,et al. An endogenous Ca2+-dependent activator protein of brain adenylate cyclase and cyclic neucleotide phosphodiesterase. , 1978, Advances in cyclic nucleotide research.
[25] J. J. Celentano,et al. Use of the covariance matrix in directly fitting kinetic parameters: application to GABAA receptors. , 2004, Biophysical journal.
[26] Lawrence R. Rabiner,et al. A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.
[27] J Haiech,et al. Effects of cations on affinity of calmodulin for calcium: ordered binding of calcium ions allows the specific activation of calmodulin-stimulated enzymes. , 1981, Biochemistry.
[28] J. Pearson,et al. Using independent open-to-closed transitions to simplify aggregated Markov models of ion channel gating kinetics. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[29] Hoon Kim,et al. Monte Carlo Statistical Methods , 2000, Technometrics.
[30] A G Hawkes,et al. The quality of maximum likelihood estimates of ion channel rate constants , 2003, The Journal of physiology.
[31] Andrej Pázman,et al. Nonlinear Regression , 2019, Handbook of Regression Analysis With Applications in R.
[32] J. Arnold,et al. An ensemble method for identifying regulatory circuits with special reference to the qa gene cluster of Neurospora crassa , 2002, Proceedings of the National Academy of Sciences of the United States of America.
[33] David J. Spiegelhalter,et al. Introducing Markov chain Monte Carlo , 1995 .
[34] K R Godfrey,et al. Structural identifiability of the parameters of a nonlinear batch reactor model. , 1992, Mathematical biosciences.
[35] Lennart Ljung,et al. System Identification: Theory for the User , 1987 .
[36] W. Y. Cheung,et al. Calmodulin plays a pivotal role in cellular regulation. , 1980, Science.
[37] Jeremy L. Muhlich,et al. Properties of cell death models calibrated and compared using Bayesian approaches , 2013, Molecular systems biology.
[38] C. Cobelli,et al. Parameter and structural identifiability concepts and ambiguities: a critical review and analysis. , 1980, The American journal of physiology.
[39] Edmund J Crampin,et al. MCMC can detect nonidentifiable models. , 2012, Biophysical journal.
[40] Radford M. Neal. MCMC Using Hamiltonian Dynamics , 2011, 1206.1901.
[41] Ursula Klingmüller,et al. Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood , 2009, Bioinform..
[42] Michael B. Miller. Linear Regression Analysis , 2013 .
[43] J Barcroft,et al. The Combinations of Haemoglobin with Oxygen and with Carbon Monoxide. II. , 1913, The Biochemical journal.
[44] Walerian Kipiniak,et al. Optimal Estimation, Identification, and Control , 1964 .
[45] Jens Timmer,et al. Data-based identifiability analysis of non-linear dynamical models , 2007, Bioinform..
[46] Peter Green,et al. Markov chain Monte Carlo in Practice , 1996 .
[47] J. Wallach. Thermodynamic theory of site‐specific binding processes in biological macromolecules by E Di Cera. pp 296. Cambridge University Press. 1995 ISBN 0‐521‐41659‐0 , 1996 .
[48] D. Colquhoun,et al. Binding, gating, affinity and efficacy: The interpretation of structure‐activity relationships for agonists and of the effects of mutating receptors , 1998, British journal of pharmacology.
[49] A. Auerbach,et al. Maximum likelihood estimation of aggregated Markov processes , 1997, Proceedings of the Royal Society of London. Series B: Biological Sciences.
[50] I Gustavsson,et al. Survey of applications of identification in chemical and physical processes , 1975, Autom..
[51] R I Jennrich,et al. Fitting nonlinear models to data. , 1979, Annual review of biophysics and bioengineering.
[52] Zachary B. Simpson,et al. FitSpace explorer: an algorithm to evaluate multidimensional parameter space in fitting kinetic data. , 2009, Analytical biochemistry.
[53] Sylvia Richardson,et al. Markov Chain Monte Carlo in Practice , 1997 .
[54] J. Banga,et al. Structural Identifiability of Systems Biology Models: A Critical Comparison of Methods , 2011, PloS one.
[55] Mitsuhiko Ikura,et al. Calmodulin in Action Diversity in Target Recognition and Activation Mechanisms , 2002, Cell.
[56] J. Cox,et al. Sequential conformational changes in calmodulin upon binding of calcium , 1984 .
[57] S. Linse,et al. Calcium binding to calmodulin and its globular domains. , 1991, The Journal of biological chemistry.
[58] N. Metropolis,et al. Equation of State Calculations by Fast Computing Machines , 1953, Resonance.
[59] Tjalling C. Koopmans,et al. Identification problems in economic model construction , 1949 .
[60] Bernd Weissmuller,et al. Ligand Receptor Energetics A Guide For The Perplexed , 2016 .
[61] Keegan E. Hines. Inferring subunit stoichiometry from single molecule photobleaching , 2013, The Journal of general physiology.
[62] G. Adair. THE HEMOGLOBIN SYSTEM VI. THE OXYGEN DISSOCIATION CURVE OF HEMOGLOBIN , 1925 .
[63] D. Rubin,et al. Inference from Iterative Simulation Using Multiple Sequences , 1992 .
[64] Ursula Klingmüller,et al. Simulation Methods for Optimal Experimental Design in Systems Biology , 2003, Simul..
[65] Claudia Biermann,et al. Mathematical Methods Of Statistics , 2016 .
[66] P. Eykhoff. System Identification Parameter and State Estimation , 1974 .
[67] K. R. GODFREY,et al. Factors affecting the identifiability of compartmental models , 1982, Autom..
[68] E. Cera,et al. Thermodynamic Theory of Site-Specific Binding Processes in Biological Macromolecules , 1996 .
[69] David J. Klinke,et al. An empirical Bayesian approach for model-based inference of cellular signaling networks , 2009, BMC Bioinformatics.
[70] C. Klee,et al. Positive cooperative binding of calcium to bovine brain calmodulin. , 1980, Biochemistry.
[71] Eric Walter,et al. QUALITATIVE AND QUANTITATIVE IDENTIFIABILITY ANALYSIS OF NONLINEAR CHEMICAL KINETIC MODELS , 1989 .
[72] Michael L. Johnson. Essential numerical computer methods , 2010 .
[73] F. Bezanilla,et al. Currents Related to Movement of the Gating Particles of the Sodium Channels , 1973, Nature.
[74] S. Linse,et al. Cooperativity: over the Hill. , 1995, Trends in biochemical sciences.
[75] Marc S. Sherman,et al. Calmodulin Target Database , 2004, Journal of Structural and Functional Genomics.
[76] Melanie I. Stefan,et al. BMC Systems Biology , 2022 .
[77] Klaus Benndorf,et al. How subunits cooperate in cAMP-induced activation of homotetrameric HCN2 channels. , 2012, Nature chemical biology.
[78] P. Kienker. Equivalence of aggregated Markov models of ion-channel gating , 1989, Proceedings of the Royal Society of London. B. Biological Sciences.
[79] L. Tierney. Markov Chains for Exploring Posterior Distributions , 1994 .
[80] Claudio Cobelli,et al. Global identifiability of nonlinear models of biological systems , 2001, IEEE Transactions on Biomedical Engineering.
[81] J. Jacquez,et al. Numerical parameter identifiability and estimability: Integrating identifiability, estimability, and optimal sampling design , 1985 .