New insights into the intrarenal renin-angiotensin (Ang) system have modified our traditional view of the system. However, many finer details of this network of peptides and associated peptidases remain unclear. We hypothesized that a computational systems biology approach, applied to peptidomic data, could help to unravel the network of enzymatic conversions. We built and refined a Bayesian network model and a dynamic systems model starting from a skeleton created with established elements of the renin-Ang system and further developed it with archived matrix-assisted laser desorption ionization-time of flight mass spectra from experiments conducted in mouse podocytes exposed to exogenous Ang substrates. The model-building process suggested previously unrecognized steps, 3 of which were confirmed in vitro, including the conversion of Ang(2-10) to Ang(2-7) by neprilysin, Ang(1-9) to Ang(2-9), and Ang(1-7) to Ang(2-7) by aminopeptidase A. These data suggest a wider role of neprilysin and aminopeptidase A in glomerular formation of bioactive Ang peptides and shunting their formation. Other steps were also suggested by the model, and supporting evidence for those steps was evaluated using model-comparison methods. Our results demonstrate that systems biology methods applied to peptidomic data are effective in identifying novel steps in the Ang peptide processing network, and these findings improve our understanding of the glomerular renin-Ang system.