A Boolean Model of Microvascular Rarefaction to Predict Treatment Outcomes in Renal Disease

Despite advances in renovascular disease (RVD) research, gaps remain between experimental and clinical outcomes, translation of results, and the understanding of pathophysiological mechanisms. A predictive tool to indicate support (or lack of) for biological findings may aid clinical translation of therapies. We created a Boolean model of RVD and hypothesized that it would predict outcomes observed in our previous studies using a translational swine model of RVD. Our studies have focused on developing treatments to halt renal microvascular (MV) rarefaction in RVD, a major feature of renal injury. A network topology of 20 factors involved in renal MV rarefaction that allowed simulation of 5 previously tested treatments was created. Each factor was assigned a function based upon its interactions with other variables and assumed to be "on" or "off". Simulations of interventions were performed until outcomes reached a steady state and analyzed to determine pathological processes that were activated, inactivated, or unchanged vs. RVD with no intervention. Boolean simulations mimicked the results of our previous studies, confirming the importance of MV integrity on treatment outcomes in RVD. Furthermore, our study supports the potential application of a mathematical tool to predict therapeutic feasibility, which may guide the design of future studies for RVD.