Literature DB >> 35305179

Identifiable Paths and Cycles in Linear Compartmental Models.

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.
© 2022. The Author(s), under exclusive licence to Society for Mathematical Biology.

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


  3 in total

1.  Identifiability Results for Several Classes of Linear Compartment Models.

Authors:  Nicolette Meshkat; Seth Sullivant; Marisa Eisenberg
Journal:  Bull Math Biol       Date:  2015-09-03       Impact factor: 1.758

2.  Similarity transformation approach to identifiability analysis of nonlinear compartmental models.

Authors:  S Vajda; K R Godfrey; H Rabitz
Journal:  Math Biosci       Date:  1989-04       Impact factor: 2.144

3.  DAISY: a new software tool to test global identifiability of biological and physiological systems.

Authors:  Giuseppina Bellu; Maria Pia Saccomani; Stefania Audoly; Leontina D'Angiò
Journal:  Comput Methods Programs Biomed       Date:  2007-08-20       Impact factor: 5.428

  3 in total

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