Structural Identifiability Analysis via Extended Observability and Decomposition
Structural identifiability analysis of nonlinear dynamic models requires symbolic manipulations, whose computational cost rises very fast with problem size. This hampers the application of these techniques to the large models which are increasingly common in systems biology. Here we present a method...
| প্রধান লেখক: | , , |
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| বিন্যাস: | Journal article |
| প্রকাশিত: |
Elsevier
2017
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| _version_ | 1826263950766899200 |
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| author | Villaverde, A Barreiro, A Papachristodoulou, A |
| author_facet | Villaverde, A Barreiro, A Papachristodoulou, A |
| author_sort | Villaverde, A |
| collection | OXFORD |
| description | Structural identifiability analysis of nonlinear dynamic models requires symbolic manipulations, whose computational cost rises very fast with problem size. This hampers the application of these techniques to the large models which are increasingly common in systems biology. Here we present a method to assess parametric identifiability based on the framework of nonlinear observability. Essentially, our method considers model parameters as particular cases of state variables with zero dynamics, and evaluates structural identifiability by calculating the rank of a generalized observability-identifiability matrix. If a model is unidentifiable as a whole, the method determines the identifiability of its individual parameters. For models whose size or complexity prevents the direct application of this procedure, an optimization approach is used to decompose them into tractable subsystems. We demonstrate the feasibility of this approach by applying it to three well-known case studies. |
| first_indexed | 2024-03-06T20:00:02Z |
| format | Journal article |
| id | oxford-uuid:26f7fb9c-405a-4272-b254-1e4e48b10f39 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T20:00:02Z |
| publishDate | 2017 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:26f7fb9c-405a-4272-b254-1e4e48b10f392022-03-26T12:04:08ZStructural Identifiability Analysis via Extended Observability and DecompositionJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:26f7fb9c-405a-4272-b254-1e4e48b10f39Symplectic Elements at OxfordElsevier2017Villaverde, ABarreiro, APapachristodoulou, AStructural identifiability analysis of nonlinear dynamic models requires symbolic manipulations, whose computational cost rises very fast with problem size. This hampers the application of these techniques to the large models which are increasingly common in systems biology. Here we present a method to assess parametric identifiability based on the framework of nonlinear observability. Essentially, our method considers model parameters as particular cases of state variables with zero dynamics, and evaluates structural identifiability by calculating the rank of a generalized observability-identifiability matrix. If a model is unidentifiable as a whole, the method determines the identifiability of its individual parameters. For models whose size or complexity prevents the direct application of this procedure, an optimization approach is used to decompose them into tractable subsystems. We demonstrate the feasibility of this approach by applying it to three well-known case studies. |
| spellingShingle | Villaverde, A Barreiro, A Papachristodoulou, A Structural Identifiability Analysis via Extended Observability and Decomposition |
| title | Structural Identifiability Analysis via Extended Observability and Decomposition |
| title_full | Structural Identifiability Analysis via Extended Observability and Decomposition |
| title_fullStr | Structural Identifiability Analysis via Extended Observability and Decomposition |
| title_full_unstemmed | Structural Identifiability Analysis via Extended Observability and Decomposition |
| title_short | Structural Identifiability Analysis via Extended Observability and Decomposition |
| title_sort | structural identifiability analysis via extended observability and decomposition |
| work_keys_str_mv | AT villaverdea structuralidentifiabilityanalysisviaextendedobservabilityanddecomposition AT barreiroa structuralidentifiabilityanalysisviaextendedobservabilityanddecomposition AT papachristodouloua structuralidentifiabilityanalysisviaextendedobservabilityanddecomposition |