| Literature DB >> 35305179 |
Cashous Bortner1, Nicolette Meshkat2.
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.Entities:
Keywords: Identifiable combinations; Identifiable functions of parameters; Linear compartmental model; Structural identifiability
Mesh:
Year: 2022 PMID: 35305179 PMCID: PMC8934029 DOI: 10.1007/s11538-022-01007-5
Source DB: PubMed Journal: Bull Math Biol ISSN: 0092-8240 Impact factor: 3.871