Detecting and Removing Inconsistencies between Experimental Data and Signaling Network Topologies Using Integer Linear Programming on Interaction Graphs
Figure 1
A simple example network used for illustration purposes.
The interaction graph consists of 7 nodes and 7 edges. The green nodes and
can be perturbed externally; the grey nodes
,
and
are the readouts of the network whose activation state is measured in the experiments; the white nodes
and
are latent nodes which are neither perturbed nor measured (see scenarios in Table 1). (A) The initial topology of the interaction graph representing the prior knowledge. This graph produces a total fitting error of 5 over the three scenarios in Table 1. (B) The (unique) optimal subgraph of (A) minimizing the total fitting error on the experimental scenarios to 2 (see Table 1). (C) Two optimal graphs obtained from (A) by applying OPT_GRAPH: by adding edge
and either (left) removing
or (right) removing
and
, the fitting error is eradicated completely and becomes 0 (cf. Table 1).