Details

Autor(en) / Beteiligte

• Joubert, D
• Stigter, J D
• Molenaar, J
• Srinivasan, Manoj

Titel

Determining minimal output sets that ensure structural identifiability

Ist Teil von

• PloS one, 2018-11, Vol.13 (11), p.e0207334-e0207334

Ort / Verlag

United States: Public Library of Science

Erscheinungsjahr

2018

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• 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.