AI Article Synopsis

  • Introduces identifiable path/cycle models, which ensure all monomial parameter functions linked to directed cycles and paths in compartmental models are identifiable.
  • Provides conditions to create identifiable path/cycle models and demonstrates how to derive locally identifiable models by eliminating leaks.
  • Offers necessary and sufficient conditions for model identifiability based on graph properties and outlines methods to test models against these criteria using graph structure analysis.

Article Abstract

We introduce a class of linear compartmental models called identifiable path/cycle models which have the property that all of the monomial functions of parameters associated to the directed cycles and paths from input compartments to output compartments are identifiable and give sufficient conditions to obtain an identifiable path/cycle model. Removing leaks, we then show how one can obtain a locally identifiable model from an identifiable path/cycle model. These identifiable path/cycle models yield the only identifiable models with certain conditions on their graph structure and thus we provide necessary and sufficient conditions for identifiable models with certain graph properties. A sufficient condition based on the graph structure of the model is also provided so that one can test if a model is an identifiable path/cycle model by examining the graph itself. We also provide some necessary conditions for identifiability based on graph structure. Our proofs use algebraic and combinatorial techniques.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8934029PMC
http://dx.doi.org/10.1007/s11538-022-01007-5DOI Listing

Publication Analysis

Top Keywords

identifiable path/cycle
20
path/cycle model
12
model identifiable
12
graph structure
12
identifiable
10
linear compartmental
8
compartmental models
8
path/cycle models
8
sufficient conditions
8
conditions identifiable
8

Similar Publications

Article Synopsis
  • Introduces identifiable path/cycle models, which ensure all monomial parameter functions linked to directed cycles and paths in compartmental models are identifiable.
  • Provides conditions to create identifiable path/cycle models and demonstrates how to derive locally identifiable models by eliminating leaks.
  • Offers necessary and sufficient conditions for model identifiability based on graph properties and outlines methods to test models against these criteria using graph structure analysis.
View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!