Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli
Figure 9
Trained cFL models linking ligand cues, phospho-protein signals, and cytokine release phenotypic responses.
A dataset describing release of five cytokines after three hours under conditions identical to those under which protein phosphorylation was measured was combined with the phospho-protein dataset. PKN2D was further extended to include links from protein signals that occupied unique principle component space (Text S1) to nodes of cytokine release after three hours. Training this network to the data indicated that the growth and survival pathways were not needed to describe cytokine release. Thus, the PKN was revised to link only Stat3, NFκB, c-Jun, and Hsp27 to the cytokine release nodes, and this PKN was trained to the experimental dataset of both cytokine release and protein phosphorylation. In contrast to the cFL models describing only signaling activation, we found that the family of 141 cFL models fit the cytokine response data with a wider distribution of MSE. The resultant sub-family of seven filtered cFL models that fit the data with a MSE less than the average plus one standard deviation of the family MSE is shown. Nodes represent proteins that were either ligand stimulations (green), inhibited (orange), phosphorylation states measured (blue), cytokine secretion measured (yellow) or could not be removed without introducing potential logical inconsistency (white). The grey/black intensity scale of the gates corresponds to the proportion of individual models within the family that include that gate. The graph of the cFL models was generated by a CellNOpt routine using the graphviz visualization engine (www.graphviz.org) followed by manual annotation in Adobe Illustrator.