eprintid: 10189130 rev_number: 7 eprint_status: archive userid: 699 dir: disk0/10/18/91/30 datestamp: 2024-03-15 11:31:26 lastmod: 2024-03-15 11:31:26 status_changed: 2024-03-15 11:31:26 type: article metadata_visibility: show sword_depositor: 699 creators_name: Wanika, Linda creators_name: Egan, Joseph R creators_name: Swaminathan, Nivedhitha creators_name: Duran-Villalobos, Carlos A creators_name: Branke, Juergen creators_name: Goldrick, Stephen creators_name: Chappell, Mike title: Structural and practical identifiability analysis in bioengineering: a beginner’s guide ispublished: pub divisions: UCL divisions: B04 divisions: C05 divisions: F47 note: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. 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. date: 2024-03-04 date_type: published publisher: Springer Science and Business Media LLC official_url: http://dx.doi.org/10.1186/s13036-024-00410-x oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 2257071 doi: 10.1186/s13036-024-00410-x lyricists_name: Swaminathan, Nivedhitha lyricists_id: NSWAM01 actors_name: Flynn, Bernadette actors_id: BFFLY94 actors_role: owner full_text_status: public publication: Journal of Biological Engineering volume: 18 article_number: 20 issn: 1754-1611 citation: Wanika, Linda; Egan, Joseph R; Swaminathan, Nivedhitha; Duran-Villalobos, Carlos A; Branke, Juergen; Goldrick, Stephen; Chappell, Mike; (2024) Structural and practical identifiability analysis in bioengineering: a beginner’s guide. Journal of Biological Engineering , 18 , Article 20. 10.1186/s13036-024-00410-x <https://doi.org/10.1186/s13036-024-00410-x>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10189130/1/s13036-024-00410-x.pdf