Identifiability investigation of within-host models of acute virus infection
Uncertainty in parameter estimates from fitting within-host models to empirical data limits the model's ability to uncover mechanisms of infection, disease progression, and to guide pharmaceutical interventions. Understanding the effect of model structure and data availability on model predicti...
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| Format: | Article |
| Language: | English |
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AIMS Press
2024-10-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2024325 |
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| author | Yuganthi R. Liyanage Nora Heitzman-Breen Necibe Tuncer Stanca M. Ciupe |
| author_facet | Yuganthi R. Liyanage Nora Heitzman-Breen Necibe Tuncer Stanca M. Ciupe |
| author_sort | Yuganthi R. Liyanage |
| collection | DOAJ |
| description | Uncertainty in parameter estimates from fitting within-host models to empirical data limits the model's ability to uncover mechanisms of infection, disease progression, and to guide pharmaceutical interventions. Understanding the effect of model structure and data availability on model predictions is important for informing model development and experimental design. To address sources of uncertainty in parameter estimation, we used four mathematical models of influenza A infection with increased degrees of biological realism. We tested the ability of each model to reveal its parameters in the presence of unlimited data by performing structural identifiability analyses. We then refined the results by predicting practical identifiability of parameters under daily influenza A virus titers alone or together with daily adaptive immune cell data. Using these approaches, we presented insight into the sources of uncertainty in parameter estimation and provided guidelines for the types of model assumptions, optimal experimental design, and biological information needed for improved predictions. |
| format | Article |
| id | doaj-art-dd17b767b6b34b359e8eb7c84a0d6ef6 |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-dd17b767b6b34b359e8eb7c84a0d6ef62025-01-23T07:48:00ZengAIMS PressMathematical Biosciences and Engineering1551-00182024-10-0121107394742010.3934/mbe.2024325Identifiability investigation of within-host models of acute virus infectionYuganthi R. Liyanage0Nora Heitzman-Breen1Necibe Tuncer2Stanca M. Ciupe3Department of Mathematics and Statistics, Florida Atlantic University, Boca Raton, FL, USADepartment of Mathematics, Virginia Polytechnic Institute and State University, Blacksburg, VA, USADepartment of Mathematics and Statistics, Florida Atlantic University, Boca Raton, FL, USADepartment of Mathematics, Virginia Polytechnic Institute and State University, Blacksburg, VA, USAUncertainty in parameter estimates from fitting within-host models to empirical data limits the model's ability to uncover mechanisms of infection, disease progression, and to guide pharmaceutical interventions. Understanding the effect of model structure and data availability on model predictions is important for informing model development and experimental design. To address sources of uncertainty in parameter estimation, we used four mathematical models of influenza A infection with increased degrees of biological realism. We tested the ability of each model to reveal its parameters in the presence of unlimited data by performing structural identifiability analyses. We then refined the results by predicting practical identifiability of parameters under daily influenza A virus titers alone or together with daily adaptive immune cell data. Using these approaches, we presented insight into the sources of uncertainty in parameter estimation and provided guidelines for the types of model assumptions, optimal experimental design, and biological information needed for improved predictions.https://www.aimspress.com/article/doi/10.3934/mbe.2024325influenza virusmathematical modelingstructural identifiabilityprofile likelihoodoptimal experimental design |
| spellingShingle | Yuganthi R. Liyanage Nora Heitzman-Breen Necibe Tuncer Stanca M. Ciupe Identifiability investigation of within-host models of acute virus infection Mathematical Biosciences and Engineering influenza virus mathematical modeling structural identifiability profile likelihood optimal experimental design |
| title | Identifiability investigation of within-host models of acute virus infection |
| title_full | Identifiability investigation of within-host models of acute virus infection |
| title_fullStr | Identifiability investigation of within-host models of acute virus infection |
| title_full_unstemmed | Identifiability investigation of within-host models of acute virus infection |
| title_short | Identifiability investigation of within-host models of acute virus infection |
| title_sort | identifiability investigation of within host models of acute virus infection |
| topic | influenza virus mathematical modeling structural identifiability profile likelihood optimal experimental design |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2024325 |
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