Identifiability analysis for stochastic differential equation models in systems biology
Mathematical models are routinely calibrated to experimental data, with goals ranging from building predictive models to quantifying parameters that cannot be measured. Whether or not reliable parameter estimates are obtainable from the available data can easily be overlooked. Such issues of paramet...
| Main Authors: | , , , , |
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| 格式: | Journal article |
| 語言: | English |
| 出版: |
Royal Society
2020
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| _version_ | 1826279818786766848 |
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| author | Browning, AP Warne, DJ Burrage, K Baker, RE Simpson, MJ |
| author_facet | Browning, AP Warne, DJ Burrage, K Baker, RE Simpson, MJ |
| author_sort | Browning, AP |
| collection | OXFORD |
| description | Mathematical models are routinely calibrated to experimental data, with goals ranging from building predictive models to quantifying parameters that cannot be measured. Whether or not reliable parameter estimates are obtainable from the available data can easily be overlooked. Such issues of parameter identifiability have important ramifications for both the predictive power of a model, and the mechanistic insight that can be obtained. Identifiability analysis is well-established for deterministic, ordinary differential equation (ODE) models, but there are no commonly adopted methods for analysing identifiability in stochastic models. We provide an accessible introduction to identifiability analysis and demonstrate how existing ideas for analysis of ODE models can be applied to stochastic differential equation (SDE) models through four practical case studies. To assess structural identifiability, we study ODEs that describe the statistical moments of the stochastic process using open-source software tools. Using practically motivated synthetic data and Markov chain Monte Carlo methods, we assess parameter identifiability in the context of available data. Our analysis shows that SDE models can often extract more information about parameters than deterministic descriptions. All code used to perform the analysis is available on Github. |
| first_indexed | 2024-03-07T00:04:29Z |
| format | Journal article |
| id | oxford-uuid:7713eb4d-01ec-4b85-adf7-abd0a7fd3878 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T00:04:29Z |
| publishDate | 2020 |
| publisher | Royal Society |
| record_format | dspace |
| spelling | oxford-uuid:7713eb4d-01ec-4b85-adf7-abd0a7fd38782022-03-26T20:20:53ZIdentifiability analysis for stochastic differential equation models in systems biologyJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:7713eb4d-01ec-4b85-adf7-abd0a7fd3878EnglishSymplectic ElementsRoyal Society2020Browning, APWarne, DJBurrage, KBaker, RESimpson, MJMathematical models are routinely calibrated to experimental data, with goals ranging from building predictive models to quantifying parameters that cannot be measured. Whether or not reliable parameter estimates are obtainable from the available data can easily be overlooked. Such issues of parameter identifiability have important ramifications for both the predictive power of a model, and the mechanistic insight that can be obtained. Identifiability analysis is well-established for deterministic, ordinary differential equation (ODE) models, but there are no commonly adopted methods for analysing identifiability in stochastic models. We provide an accessible introduction to identifiability analysis and demonstrate how existing ideas for analysis of ODE models can be applied to stochastic differential equation (SDE) models through four practical case studies. To assess structural identifiability, we study ODEs that describe the statistical moments of the stochastic process using open-source software tools. Using practically motivated synthetic data and Markov chain Monte Carlo methods, we assess parameter identifiability in the context of available data. Our analysis shows that SDE models can often extract more information about parameters than deterministic descriptions. All code used to perform the analysis is available on Github. |
| spellingShingle | Browning, AP Warne, DJ Burrage, K Baker, RE Simpson, MJ Identifiability analysis for stochastic differential equation models in systems biology |
| title | Identifiability analysis for stochastic differential equation models in systems biology |
| title_full | Identifiability analysis for stochastic differential equation models in systems biology |
| title_fullStr | Identifiability analysis for stochastic differential equation models in systems biology |
| title_full_unstemmed | Identifiability analysis for stochastic differential equation models in systems biology |
| title_short | Identifiability analysis for stochastic differential equation models in systems biology |
| title_sort | identifiability analysis for stochastic differential equation models in systems biology |
| work_keys_str_mv | AT browningap identifiabilityanalysisforstochasticdifferentialequationmodelsinsystemsbiology AT warnedj identifiabilityanalysisforstochasticdifferentialequationmodelsinsystemsbiology AT burragek identifiabilityanalysisforstochasticdifferentialequationmodelsinsystemsbiology AT bakerre identifiabilityanalysisforstochasticdifferentialequationmodelsinsystemsbiology AT simpsonmj identifiabilityanalysisforstochasticdifferentialequationmodelsinsystemsbiology |