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Identifiability of Linear Compartmental Models: The Impact of Removing Leaks and Edges

2021/02/08 by Chan, Patrick, Johnston, Katherine, Shiu, Anne +2 · 1 citation
#37N25 #92C45 #93B30 #Combinatorics (math.CO) #Dynamical Systems (math.DS) #FOS: Mathematics

paper · doi:10.48550/arxiv.2102.04417

Abstract

A mathematical model is identifiable if its parameters can be recovered from data. Here, we focus on a particular class of model, linear compartmental models, which are used to represent the transfer of substances in a system. We analyze what happens to identifiability when operations are performed on a model, specifically, adding or deleting a leak or an edge. We first consider the conjecture of Gross et al. that states that removing a leak from an identifiable model yields a model that is again identifiable. We prove a special case of this conjecture, and also show that the conjecture is equivalent to asserting that leak terms do not divide the so-called singular-locus equation. As for edge terms that do divide this equation, we conjecture that removing any one of these edges makes the model become unidentifiable,and then prove a case of this somewhat surprising conjecture.

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