Abstract: This talk will present a simple but meaningful example of the importance and application of identifiability analysis of compartmental models in a real-world context. Carbon stable isotope breath tests provide a dose of non-radioactive 13C-labeled substrate, which is digested, absorbed, and metabolized, appearing on the breath as 13CO2. These tests offer new opportunities to better understand gastrointestinal function in health and disease. However, it is often not clear how to isolate information about a gastrointestinal or metabolic process of interest from a breath test curve, and it is generally unknown how well summary statistics from empirical curve fitting correlate with underlying biological rates. We developed a framework that can be used to make mechanistic inference about the metabolic rates underlying a 13C breath test curve, and we applied it to a pilot study of 13C-sucrose breath test in 20 healthy adults. Starting from a standard conceptual model of sucrose metabolism, we determined the structural and practical identifiability of the model, using algebraic methods and profile likelihoods, respectively. We used these results to develop a reduced, identifiable model as a function of a gamma-distributed process; an exponential process; and a scaling term related to the fraction of the substrate that is exhaled as opposed to sequestered or excreted through urine. Our work develops a better understanding of how the underlying biological processes impact different aspects of 13C breath test curves, enhancing the clinical and research potential of these 13C breath tests.