Identification of optimal drug combinations targeting cellular networks: integrating phospho-proteomics and computational network analysis.

ABSTRACT: Targeted therapeutics hold tremendous promise in inhibiting cancer cell proliferation. However, targeting proteins individually can be compensated for by bypass mechanisms and activation of regulatory loops. Designing optimal therapeutic combinations must therefore take into consideration the complex dynamic networks in the cell. In this study, we analyzed the insulin-like growth factor (IGF-1) signaling network in the MDA-MB231 breast cancer cell line. We used reverse-phase protein array to measure the transient changes in the phosphorylation of proteins after IGF-1 stimulation. We developed a computational procedure that integrated mass action modeling with particle swarm optimization to train the model against the experimental data and infer the unknown model parameters. The trained mod

SUBMITTER: Iadevaia S 

PROVIDER: S-EPMC2932856 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature