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Sensitivity, robustness, and identifiability in stochastic chemical kinetics models
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| Zeitschriftentitel: | Proceedings of the National Academy of Sciences |
|---|---|
| Personen und Körperschaften: | , , , |
| In: | Proceedings of the National Academy of Sciences, 108, 2011, 21, S. 8645-8650 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Proceedings of the National Academy of Sciences
|
| Schlagwörter: |
| author_facet |
Komorowski, Michał Costa, Maria J. Rand, David A. Stumpf, Michael P. H. Komorowski, Michał Costa, Maria J. Rand, David A. Stumpf, Michael P. H. |
|---|---|
| author |
Komorowski, Michał Costa, Maria J. Rand, David A. Stumpf, Michael P. H. |
| spellingShingle |
Komorowski, Michał Costa, Maria J. Rand, David A. Stumpf, Michael P. H. Proceedings of the National Academy of Sciences Sensitivity, robustness, and identifiability in stochastic chemical kinetics models Multidisciplinary |
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komorowski, michał |
| spelling |
Komorowski, Michał Costa, Maria J. Rand, David A. Stumpf, Michael P. H. 0027-8424 1091-6490 Proceedings of the National Academy of Sciences Multidisciplinary http://dx.doi.org/10.1073/pnas.1015814108 <jats:p>We present a novel and simple method to numerically calculate Fisher information matrices for stochastic chemical kinetics models. The linear noise approximation is used to derive model equations and a likelihood function that leads to an efficient computational algorithm. Our approach reduces the problem of calculating the Fisher information matrix to solving a set of ordinary differential equations. This is the first method to compute Fisher information for stochastic chemical kinetics models without the need for Monte Carlo simulations. This methodology is then used to study sensitivity, robustness, and parameter identifiability in stochastic chemical kinetics models. We show that significant differences exist between stochastic and deterministic models as well as between stochastic models with time-series and time-point measurements. We demonstrate that these discrepancies arise from the variability in molecule numbers, correlations between species, and temporal correlations and show how this approach can be used in the analysis and design of experiments probing stochastic processes at the cellular level. The algorithm has been implemented as a Matlab package and is available from the authors upon request.</jats:p> Sensitivity, robustness, and identifiability in stochastic chemical kinetics models Proceedings of the National Academy of Sciences |
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| title |
Sensitivity, robustness, and identifiability in stochastic chemical kinetics models |
| title_unstemmed |
Sensitivity, robustness, and identifiability in stochastic chemical kinetics models |
| title_full |
Sensitivity, robustness, and identifiability in stochastic chemical kinetics models |
| title_fullStr |
Sensitivity, robustness, and identifiability in stochastic chemical kinetics models |
| title_full_unstemmed |
Sensitivity, robustness, and identifiability in stochastic chemical kinetics models |
| title_short |
Sensitivity, robustness, and identifiability in stochastic chemical kinetics models |
| title_sort |
sensitivity, robustness, and identifiability in stochastic chemical kinetics models |
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Multidisciplinary |
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http://dx.doi.org/10.1073/pnas.1015814108 |
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2011 |
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8645-8650 |
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<jats:p>We present a novel and simple method to numerically calculate Fisher information matrices for stochastic chemical kinetics models. The linear noise approximation is used to derive model equations and a likelihood function that leads to an efficient computational algorithm. Our approach reduces the problem of calculating the Fisher information matrix to solving a set of ordinary differential equations. This is the first method to compute Fisher information for stochastic chemical kinetics models without the need for Monte Carlo simulations. This methodology is then used to study sensitivity, robustness, and parameter identifiability in stochastic chemical kinetics models. We show that significant differences exist between stochastic and deterministic models as well as between stochastic models with time-series and time-point measurements. We demonstrate that these discrepancies arise from the variability in molecule numbers, correlations between species, and temporal correlations and show how this approach can be used in the analysis and design of experiments probing stochastic processes at the cellular level. The algorithm has been implemented as a Matlab package and is available from the authors upon request.</jats:p> |
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| author | Komorowski, Michał, Costa, Maria J., Rand, David A., Stumpf, Michael P. H. |
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| description | <jats:p>We present a novel and simple method to numerically calculate Fisher information matrices for stochastic chemical kinetics models. The linear noise approximation is used to derive model equations and a likelihood function that leads to an efficient computational algorithm. Our approach reduces the problem of calculating the Fisher information matrix to solving a set of ordinary differential equations. This is the first method to compute Fisher information for stochastic chemical kinetics models without the need for Monte Carlo simulations. This methodology is then used to study sensitivity, robustness, and parameter identifiability in stochastic chemical kinetics models. We show that significant differences exist between stochastic and deterministic models as well as between stochastic models with time-series and time-point measurements. We demonstrate that these discrepancies arise from the variability in molecule numbers, correlations between species, and temporal correlations and show how this approach can be used in the analysis and design of experiments probing stochastic processes at the cellular level. The algorithm has been implemented as a Matlab package and is available from the authors upon request.</jats:p> |
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| spelling | Komorowski, Michał Costa, Maria J. Rand, David A. Stumpf, Michael P. H. 0027-8424 1091-6490 Proceedings of the National Academy of Sciences Multidisciplinary http://dx.doi.org/10.1073/pnas.1015814108 <jats:p>We present a novel and simple method to numerically calculate Fisher information matrices for stochastic chemical kinetics models. The linear noise approximation is used to derive model equations and a likelihood function that leads to an efficient computational algorithm. Our approach reduces the problem of calculating the Fisher information matrix to solving a set of ordinary differential equations. This is the first method to compute Fisher information for stochastic chemical kinetics models without the need for Monte Carlo simulations. This methodology is then used to study sensitivity, robustness, and parameter identifiability in stochastic chemical kinetics models. We show that significant differences exist between stochastic and deterministic models as well as between stochastic models with time-series and time-point measurements. We demonstrate that these discrepancies arise from the variability in molecule numbers, correlations between species, and temporal correlations and show how this approach can be used in the analysis and design of experiments probing stochastic processes at the cellular level. The algorithm has been implemented as a Matlab package and is available from the authors upon request.</jats:p> Sensitivity, robustness, and identifiability in stochastic chemical kinetics models Proceedings of the National Academy of Sciences |
| spellingShingle | Komorowski, Michał, Costa, Maria J., Rand, David A., Stumpf, Michael P. H., Proceedings of the National Academy of Sciences, Sensitivity, robustness, and identifiability in stochastic chemical kinetics models, Multidisciplinary |
| title | Sensitivity, robustness, and identifiability in stochastic chemical kinetics models |
| title_full | Sensitivity, robustness, and identifiability in stochastic chemical kinetics models |
| title_fullStr | Sensitivity, robustness, and identifiability in stochastic chemical kinetics models |
| title_full_unstemmed | Sensitivity, robustness, and identifiability in stochastic chemical kinetics models |
| title_short | Sensitivity, robustness, and identifiability in stochastic chemical kinetics models |
| title_sort | sensitivity, robustness, and identifiability in stochastic chemical kinetics models |
| title_unstemmed | Sensitivity, robustness, and identifiability in stochastic chemical kinetics models |
| topic | Multidisciplinary |
| url | http://dx.doi.org/10.1073/pnas.1015814108 |