Parameter identifiability and model selection for partial differential equation models of cell invasion
When employing mechanistic models to study biological phenomena, practical parameter identifiability is important for making accurate predictions across wide range of unseen scenarios, as well as for understanding the underlying mechanisms. In this work we use a profile likelihood approach to invest...
| Main Authors: | , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Royal Society
2024
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| _version_ | 1826312641636728832 |
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| author | Liu, Y Suh, K Maini, P Cohen, D Baker, R |
| author_facet | Liu, Y Suh, K Maini, P Cohen, D Baker, R |
| author_sort | Liu, Y |
| collection | OXFORD |
| description | When employing mechanistic models to study biological phenomena, practical parameter identifiability is important for making accurate predictions across wide range
of unseen scenarios, as well as for understanding the underlying mechanisms. In this
work we use a profile likelihood approach to investigate parameter identifiability for
four extensions of the Fisher–KPP model, given experimental data from a cell invasion
assay. We show that more complicated models tend to be less identifiable, with parameter estimates being more sensitive to subtle differences in experimental procedures,
and that they require more data to be practically identifiable. As a result, we suggest
that parameter identifiability should be considered alongside goodness-of-fit and model
complexity as criteria for model selection. |
| first_indexed | 2024-03-07T08:22:58Z |
| format | Journal article |
| id | oxford-uuid:4001eb0d-55b2-4fe6-ba2c-1935a08a1842 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-04-09T03:58:55Z |
| publishDate | 2024 |
| publisher | Royal Society |
| record_format | dspace |
| spelling | oxford-uuid:4001eb0d-55b2-4fe6-ba2c-1935a08a18422024-04-05T11:09:15ZParameter identifiability and model selection for partial differential equation models of cell invasionJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:4001eb0d-55b2-4fe6-ba2c-1935a08a1842EnglishSymplectic ElementsRoyal Society2024Liu, YSuh, KMaini, PCohen, DBaker, RWhen employing mechanistic models to study biological phenomena, practical parameter identifiability is important for making accurate predictions across wide range of unseen scenarios, as well as for understanding the underlying mechanisms. In this work we use a profile likelihood approach to investigate parameter identifiability for four extensions of the Fisher–KPP model, given experimental data from a cell invasion assay. We show that more complicated models tend to be less identifiable, with parameter estimates being more sensitive to subtle differences in experimental procedures, and that they require more data to be practically identifiable. As a result, we suggest that parameter identifiability should be considered alongside goodness-of-fit and model complexity as criteria for model selection. |
| spellingShingle | Liu, Y Suh, K Maini, P Cohen, D Baker, R Parameter identifiability and model selection for partial differential equation models of cell invasion |
| title | Parameter identifiability and model selection for partial differential equation models of cell invasion |
| title_full | Parameter identifiability and model selection for partial differential equation models of cell invasion |
| title_fullStr | Parameter identifiability and model selection for partial differential equation models of cell invasion |
| title_full_unstemmed | Parameter identifiability and model selection for partial differential equation models of cell invasion |
| title_short | Parameter identifiability and model selection for partial differential equation models of cell invasion |
| title_sort | parameter identifiability and model selection for partial differential equation models of cell invasion |
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