Structural and practical identifiability analysis in bioengineering: a beginner’s guide
Abstract Advancements in digital technology have brought modelling to the forefront in many disciplines from healthcare to architecture. Mathematical models, often represented using parametrised sets of ordinary differential equations, can be used to characterise different processes. To infer possib...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-03-01
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| Series: | Journal of Biological Engineering |
| Online Access: | https://doi.org/10.1186/s13036-024-00410-x |
| _version_ | 1797274332733374464 |
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| author | Linda Wanika Joseph R. Egan Nivedhitha Swaminathan Carlos A. Duran-Villalobos Juergen Branke Stephen Goldrick Mike Chappell |
| author_facet | Linda Wanika Joseph R. Egan Nivedhitha Swaminathan Carlos A. Duran-Villalobos Juergen Branke Stephen Goldrick Mike Chappell |
| author_sort | Linda Wanika |
| collection | DOAJ |
| description | Abstract Advancements in digital technology have brought modelling to the forefront in many disciplines from healthcare to architecture. Mathematical models, often represented using parametrised sets of ordinary differential equations, can be used to characterise different processes. To infer possible estimates for the unknown parameters, these models are usually calibrated using associated experimental data. Structural and practical identifiability analyses are a key component that should be assessed prior to parameter estimation. This is because identifiability analyses can provide insights as to whether or not a parameter can take on single, multiple, or even infinitely or countably many values which will ultimately have an impact on the reliability of the parameter estimates. Also, identifiability analyses can help to determine whether the data collected are sufficient or of good enough quality to truly estimate the parameters or if more data or even reparameterization of the model is necessary to proceed with the parameter estimation process. Thus, such analyses also provide an important role in terms of model design (structural identifiability analysis) and the collection of experimental data (practical identifiability analysis). Despite the popularity of using data to estimate the values of unknown parameters, structural and practical identifiability analyses of these models are often overlooked. Possible reasons for non-consideration of application of such analyses may be lack of awareness, accessibility, and usability issues, especially for more complicated models and methods of analysis. The aim of this study is to introduce and perform both structural and practical identifiability analyses in an accessible and informative manner via application to well established and commonly accepted bioengineering models. This will help to improve awareness of the importance of this stage of the modelling process and provide bioengineering researchers with an understanding of how to utilise the insights gained from such analyses in future model development. |
| first_indexed | 2024-03-07T14:57:52Z |
| format | Article |
| id | doaj.art-da7a9208b906428496f0b411855e299e |
| institution | Directory Open Access Journal |
| issn | 1754-1611 |
| language | English |
| last_indexed | 2024-03-07T14:57:52Z |
| publishDate | 2024-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Biological Engineering |
| spelling | doaj.art-da7a9208b906428496f0b411855e299e2024-03-05T19:21:59ZengBMCJournal of Biological Engineering1754-16112024-03-0118111310.1186/s13036-024-00410-xStructural and practical identifiability analysis in bioengineering: a beginner’s guideLinda Wanika0Joseph R. Egan1Nivedhitha Swaminathan2Carlos A. Duran-Villalobos3Juergen Branke4Stephen Goldrick5Mike Chappell6School of Engineering, University of WarwickDepartment of Biochemical Engineering, University College LondonDepartment of Biochemical Engineering, University College LondonDepartment of Electrical and Electronic Engineering, University of ManchesterWarwick Business School, University of WarwickDepartment of Biochemical Engineering, University College LondonSchool of Engineering, University of WarwickAbstract Advancements in digital technology have brought modelling to the forefront in many disciplines from healthcare to architecture. Mathematical models, often represented using parametrised sets of ordinary differential equations, can be used to characterise different processes. To infer possible estimates for the unknown parameters, these models are usually calibrated using associated experimental data. Structural and practical identifiability analyses are a key component that should be assessed prior to parameter estimation. This is because identifiability analyses can provide insights as to whether or not a parameter can take on single, multiple, or even infinitely or countably many values which will ultimately have an impact on the reliability of the parameter estimates. Also, identifiability analyses can help to determine whether the data collected are sufficient or of good enough quality to truly estimate the parameters or if more data or even reparameterization of the model is necessary to proceed with the parameter estimation process. Thus, such analyses also provide an important role in terms of model design (structural identifiability analysis) and the collection of experimental data (practical identifiability analysis). Despite the popularity of using data to estimate the values of unknown parameters, structural and practical identifiability analyses of these models are often overlooked. Possible reasons for non-consideration of application of such analyses may be lack of awareness, accessibility, and usability issues, especially for more complicated models and methods of analysis. The aim of this study is to introduce and perform both structural and practical identifiability analyses in an accessible and informative manner via application to well established and commonly accepted bioengineering models. This will help to improve awareness of the importance of this stage of the modelling process and provide bioengineering researchers with an understanding of how to utilise the insights gained from such analyses in future model development.https://doi.org/10.1186/s13036-024-00410-x |
| spellingShingle | Linda Wanika Joseph R. Egan Nivedhitha Swaminathan Carlos A. Duran-Villalobos Juergen Branke Stephen Goldrick Mike Chappell Structural and practical identifiability analysis in bioengineering: a beginner’s guide Journal of Biological Engineering |
| title | Structural and practical identifiability analysis in bioengineering: a beginner’s guide |
| title_full | Structural and practical identifiability analysis in bioengineering: a beginner’s guide |
| title_fullStr | Structural and practical identifiability analysis in bioengineering: a beginner’s guide |
| title_full_unstemmed | Structural and practical identifiability analysis in bioengineering: a beginner’s guide |
| title_short | Structural and practical identifiability analysis in bioengineering: a beginner’s guide |
| title_sort | structural and practical identifiability analysis in bioengineering a beginner s guide |
| url | https://doi.org/10.1186/s13036-024-00410-x |
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