Macrophages are abundant immune cells in mammary tumors and, with breast cancer epithelial cells, deposit cysteine cathepsins into tumor microenvironments. Cathepsins are components in a proteolytic network where cathepsin-mediated substrate degradation can be susceptible to hydrolysis by other cathepsins, a mechanism we have termed cathepsin cannibalism, or cleavage by other proteases. Inhibitor binding to proteases can perturb the network’s steady state and total substrate degradation by affecting inter-protease interactions and cellular feedback. We showed that incubation of MDA-MB-231 breast cancer cells with 0-50µM of E-64 or 0-1µM of cystatin C, both broad cathepsin inhibitors, increased the amount of active cathepsin S while suppressing active cathepsin L. In follow-up studies, we observed this differential inhibitor-induced feedback in THP1 macrophages and primary monocyte-derived macrophages, another cell type in mammary tumors. Using systems biology tools to understand cellular feedback responses and cathepsin activity in an extracellular proteolytic network will help predict effects of pharmacologic cathepsin inhibitors. Using MATLAB and COPASI software, we developed kinetic, computational models of cathepsins 1) being produced, secreted, activated, and 2) degrading matrix or other cathepsins, and binding to inhibitors (cystatins and synthetic). To fit parameters, we experimentally quantified MDA-MB-231- or macrophage-specific rates for cathepsins L and S and cystatin C, DQ-gelatin or DQ-elastin degradation. COPASI solved system of ordinary differential equations, fit parameters to experimental data, and allowed us to generate predicted outputs of protease concentrations and substrate degradation under variable conditions relevant for breast cancer tumors. Experimentally, catS accumulated faster than catL, which was reflected by high sensitivity of its rate parameters on total substrate degradation in simulations. Other non-intuitive insights from varying inhibitor and cathepsin parameters will be presented to demonstrate importance of variability in cell-type composition, multiple cathepsins, and inhibitors on breast tumor microenvironments.