Pii: S0025-5564(00)00035-3

The identi®ability problem is addressed for n-compartment linear mammillary and catenary models, for the common case of input and output in the ®rst compartment and prior information about one or more model rate constants. We ®rst de®ne the concept of independent constraints and show that n-compartment linear mammillary or catenary models are uniquely identi®able under nÿ 1 independent constraints. Closed-form algorithms for bounding the constrained parameter space are then developed algebraically, and their validity is con®rmed using an independent approach, namely joint estimation of the parameters of all uniquely identi®able submodels of the original multicompartmental model. For the noise-free (deterministic) case, the major e€ects of additional parameter knowledge are to narrow the bounds of rate constants that remain unidenti®able, as well as to possibly render others identi®able. When noisy data are considered, the means of the bounds of rate constants that remain unidenti®able are also narrowed, but the variances of some of these bound estimates increase. This unexpected result was veri®ed by Monte Carlo simulation of several di€erent models, using both normally and lognormally distributed data assumptions. Extensions and some consequences of this analysis useful for model discrimination and experiment design applications are also noted. Ó 2000 Elsevier Science Inc. All rights reserved.