2014/10/30 by Nicolette Meshkat, Meshkat, Nicolette, Seth Sullivant +3 · 2 citations
Biochemistry, Genetics and Molecular Biology · #Algebraic Geometry (math.AG) #Bioinformatics and Genomic Networks #Dynamical Systems (math.DS) #FOS: Mathematics #Gene Regulatory Network Analysis #Microbial Metabolic Engineering and Bioproduction
paper · pdf · doi:10.48550/arxiv.1410.8587
openalex publication_date 2014/10/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Identifiability concerns finding which unknown parameters of a model can be estimated 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 past work we identified a class of models, that we call identifiable cycle models, which are not identifiable 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.