The computational approach. (A) Modeling the current knowledge. Nodes represent the variables of the model and arcs are known regulatory relations. Here, the state of variable C depends on the states of its regulators A and B according to a specific logic. In the combinatorial logic of C (left), the state of C is 1 if, and only if, at least one of its regulators has state 1. In the probabilistic modeling (right), each possible state of C is assigned a probability depending on our confidence in the current biological knowledge (here, 90% confidence). (B) Improving the model. The model refinement and expansion procedures look for model changes that improve the model significantly. The improvement score compares between the fit of a possible modified model and that of the null (original) model. The plots are a schematic representation of these two models in cases of refinement (top) and expansion (bottom). In expansion, when adding a new gene, the null model assumes that the gene expression can be explained sufficiently by the environmental stimulation. The alternative hypothesis is a model-dependent gene, i.e., the gene is regulated by our signaling network. We expand the model only if the improvement score is significant, i.e., the signaling network explains the expression much better than the environmental stimulation only.