|Title||Determining minimal output sets that ensure structural identifiability|
|Author(s)||Joubert, D.; Stigter, J.D.; Molenaar, J.|
|Source||PLoS One 13 (2018)11. - ISSN 1932-6203|
Biometris (WU MAT)
|Publication type||Refereed Article in a scientific journal|
The process of inferring parameter values from experimental data can be a cumbersome task. In addition, the collection of experimental data can be time consuming and costly. This paper covers both these issues by addressing the following question: "Which experimental outputs should be measured to ensure that unique model parameters can be calculated?". Stated formally, we examine the topic of minimal output sets that guarantee a model's structural identifiability. To that end, we introduce an algorithm that guides a researcher as to which model outputs to measure. Our algorithm consists of an iterative structural identifiability analysis and can determine multiple minimal output sets of a model. This choice in different output sets offers researchers flexibility during experimental design. Our method can determine minimal output sets of large differential equation models within short computational times.