Abstract：
:Identifiability concerns finding which unknown parameters of a model can be estimated, uniquely or otherwise, from given input-output data. If some subset of the parameters of a model cannot be determined given input-output data, then we say the model is unidentifiable. In this work, we study linear compartment models, which are a class of biological models commonly used in pharmacokinetics, physiology, and ecology. In past work, we used commutative algebra and graph theory to identify a class of linear compartment models that we call identifiable cycle models, which are unidentifiable but have the simplest possible identifiable functions (so-called monomial cycles). Here we show how to modify identifiable cycle models by adding inputs, adding outputs, or removing leaks, in such a way that we obtain an identifiable model. We also prove a constructive result on how to combine identifiable models, each corresponding to strongly connected graphs, into a larger identifiable model. We apply these theoretical results to several real-world biological models from physiology, cell biology, and ecology.
journal_name
Bull Math Bioljournal_title
Bulletin of mathematical biologyauthors
Meshkat N,Sullivant S,Eisenberg Mdoi
10.1007/s11538-015-0098-0subject
Has Abstractpub_date
2015-08-01 00:00:00pages
1620-51issue
8eissn
0092-8240issn
1522-9602pii
10.1007/s11538-015-0098-0journal_volume
77pub_type
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