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