Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli

Topologies of the family of filtered cFL models trained to the HepG2 dataset. Unprocessed cFL models can be found in and fit of the filtered models to the data in . Nodes represent proteins that were either ligand stimulations (green), inhibited (orange), measured by a phospho-specific bead-based antibody assay (blue), 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. Thus, links colored black were present in all models whereas links colored grey were present in a fraction of the models. Where visually feasible, cFL gates are labeled with a numerical value that corresponds to a quantitative sensitivity of the input-output relationship. Sensitivity is calculated as described in the . The larger this value, the lower the level of the input nodes' activity required for generating significant output node activity (i.e. a gate with a high sensitivity indicates that the output node is sensitive to a low value of its input node). The uncertainties in these values arise from the various best-fit EC50 for each individual model. 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.

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Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli. (2011) Melody K. Morris, et al. PLoS Comput Biol. 2011 Mar;7(3):e1001099. Figure: F5. All organisms Mus musculus Rattus norvegicus Homo sapiens Drosophila melanogaster

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