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Dysregulation in signal transduction pathways can lead to a variety ofcomplex disorders, including cancer. Computational approaches such as networkanalysis are important tools to understand system dynamics as well as toidentify critical components that could be further explored as therapeutictargets. Here, we performed perturbation analysis of a large-scale signaltransduction model in extracellular environments that stimulate cell death,growth, motility, and quiescence. Each of the model's components was perturbedunder both loss-of-function and gain-of-function mutations. We identified themost and least influential components based on the magnitude of their influenceon the rest of the system. Based on the premise that the most influentialcomponents might serve as better drug targets, we characterized them forbiological functions, housekeeping genes, essential genes, and druggableproteins. Moreover, known cancer drug targets were also classified ininfluential components based on the affected components in the network.Additionally, the systemic perturbation analysis of the model revealed anetwork motif of most influential components which affect each other.Furthermore, our analysis predicted novel combinations of cancer drug targetswith various effects on other most influential components. We found that thecombinatorial perturbation consisting of PI3K inactivation and overactivationof IP3R1 can lead to increased activity levels of apoptosis-related componentsand tumor suppressor genes, suggesting that this combinatorial perturbation maylead to a better target for decreasing cell proliferation and inducingapoptosis. Lastly, our results suggest that systematic perturbation analyses oflarge-scale computational models may serve as an approach to prioritize andassess signal transduction components in order to identify novel drug targetsin complex disorders.

Systems Perturbation Analysis of a Large Scale Signal Transduction Model Reveals Potentially Influential Candidates for Cancer Therapeutics