  We present a new experimental-computational technology of inferring network
models that predict the response of cells to perturbations and that may be
useful in the design of combinatorial therapy against cancer. The experiments
are systematic series of perturbations of cancer cell lines by targeted drugs,
singly or in combination. The response to perturbation is measured in terms of
levels of proteins and phospho-proteins and of cellular phenotype such as
viability. Computational network models are derived de novo, i.e., without
prior knowledge of signaling pathways, and are based on simple non-linear
differential equations. The prohibitively large solution space of all possible
network models is explored efficiently using a probabilistic algorithm, belief
propagation, which is three orders of magnitude more efficient than Monte Carlo
methods. Explicit executable models are derived for a set of perturbation
experiments in Skmel-133 melanoma cell lines, which are resistant to the
therapeutically important inhibition of Raf kinase. The resulting network
models reproduce and extend known pathway biology. They can be applied to
discover new molecular interactions and to predict the effect of novel drug
perturbations, one of which is verified experimentally. The technology is
suitable for application to larger systems in diverse areas of molecular
biology.
