The two original models for the TCR and TLR5 signaling pathways were merged, and the logical rules of shared nodes were updated to take into account the additional regulatory inputs (Fig. 5, table S4, and data file S5). We computed the stable states of the two original and merged models under wild-type and mutant conditions. We further generated a reduced version of the merged model (fig. S4) to explore its dynamical behavior. All of the analyses were performed with GINsim [as previously described (8184)], which supports model reduction by hiding selected (intermediate) nodes. Provided that no functional regulatory circuit is eliminated in the process, this reduction preserves all attractors (81). The dynamical behavior of a logical model is represented by a state transition graph (STG). In this graph, each node represents a state of the model, which is defined by a vector encompassing the levels of all components, and the arcs represent transitions between states. One core function of GINsim is the automatic construction of this graph (82). When the number of components in a model is large, the resulting STG becomes difficult to compute and to visualize. In this respect, GINsim enables the generation of a hierarchical transition graph (HTG), which is computed by clustering the nodes of an STG into groups of states (hyper-nodes) sharing the same set of successors (84). Last, computing the HTG for different initial conditions enabled us to identify all of the attractors of our merged model for wild-type and mutant scenarios (see Results).

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