“Reverse engineering” Biological Networks. To understand nonlinear and highly interconnected signaling pathways in cancer cells, I am applying computational approaches that directly "reverse engineer" networks from large scale quantitative experimental data that I am collecting. Biological data are naturally noisy and sparse and therefore particularly suited to a probabilistic modeling framework, of which Bayesian network analysis is one of the best developed.When inputs and outputs are known, Bayesian modeling can be used to infer the functional interactions (Inference). When the inputs and the system are known, the model can be used to predict a response (Prediction). When the system and the desired output are known, the model can serve to design optimal modes of control (Control).