Summary

The role that mechanistic mathematical modeling and systems biology will play in molecular medicine and clinicaldevelopment remains uncertain. In this study, mathematical modeling and sensitivity analysis were used to explorethe working hypothesis that mechanistic models of human cascades, despite model uncertainty, can be computationallyscreened for points of fragility, and that these sensitive mechanisms could serve as therapeutic targets. Wetested our working hypothesis by screening a model of the well-studied coagulation cascade, developed and validatedfrom literature. The predicted sensitive mechanisms were then compared with the treatment literature. The model,composed of 92 proteins and 148 protein–protein interactions, was validated using 21 published datasets generatedfrom two different quiescent in vitro coagulation models. Simulated platelet activation and thrombin generationprofiles in the presence and absence of natural anticoagulants were consistent with measured values, with a meancorrelation of 0.87 across all trials. Overall state sensitivity coefficients, which measure the robustness or fragility of agiven mechanism, were calculated using a Monte Carlo strategy. In the absence of anticoagulants, fluid and surfacephase factor X/activated factor X (fX/FXa) activity and thrombin-mediated platelet activation were found to be fragile,while fIX/FIXa and fVIII/FVIIIa activation and activity were robust. Both anti-fX/FXa and direct thrombin inhibitors areimportant classes of anticoagulants; for example, anti-fX/FXa inhibitors have FDA approval for the prevention ofvenous thromboembolism following surgical intervention and as an initial treatment for deep venous thrombosis andpulmonary embolism. Both in vitro and in vivo experimental evidence is reviewed supporting the prediction that fIX/FIXa activity is robust. When taken together, these results support our working hypothesis that computationallyderived points of fragility of human relevant cascades could be used as a rational basis for target selection despitemodel uncertainty.