Open AccessDetermining minimal output sets that ensure structural identifiability
Author(s) -
Dominique Joubert,
J.D. Stigter,
Jaap Molenaar
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0207334
Subject(s) - identifiability , computer science , flexibility (engineering) , measure (data warehouse) , process (computing) , task (project management) , algorithm , set (abstract data type) , experimental data , data mining , mathematical optimization , mathematics , machine learning , statistics , management , economics , programming language , operating system
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.