%0 Journal Article
%T Systematic identifiability testing for unambiguous mechanistic modeling ¨C application to JAK-STAT, MAP kinase, and NF-¦ÊB signaling pathway models
%A Tom Quaiser
%A Martin M£¿nnigmann
%J BMC Systems Biology
%D 2009
%I BioMed Central
%R 10.1186/1752-0509-3-50
%X We propose an eigenvalue based method for efficiently testing identifiability of large ordinary differential models and compare this approach to three existing ones. The methods are benchmarked by applying them to models of the signaling pathways mentioned above. In all cases the eigenvalue method proposed here and the orthogonal method find the largest set of identifiable parameters, thus clearly outperforming the other approaches. The identifiability analysis shows that the pathway models are not identifiable, even under the strong assumption that all system state variables are measurable. We demonstrate how the results of the identifiability analysis can be used for model simplification.While it has undoubtedly contributed to recent advances in systems biology, mechanistic modeling by itself does not guarantee unambiguous descriptions of biological processes. We show that some recent signal transduction pathway models have reached a level of detail that is not warranted. Rigorous identifiability tests reveal that even if highly idealized experiments could be carried out to measure all state variables of these signaling pathways, some unknown parameters could still not be estimated. The identifiability tests therefore show that the level of detail of the investigated models is too high in principle, not just because too little experimental information is available. We demonstrate how the proposed method can be combined with biological insight, however, to simplify these models.Several large and detailed mathematical models for signal transduction pathways exist in the literature. Lipniacki et al. [1] model the NF-¦ÊB pathway using 15 state variables and 29 parameters. Yamada et al. [2] introduce a system of ordinary differential equations that describe the JAK-STAT pathway with 31 state variables and 52 parameters, and Schoeberl et al. [3] describe the EGF pathway with a model that comprises 103 variables and 98 parameters. Models of this kind can provide a concise
%U http://www.biomedcentral.com/1752-0509/3/50