Determining minimal output sets that ensure structural identifiability.

ABSTRACT: 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.

SUBMITTER: Joubert D 

PROVIDER: S-EPMC6231658 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature