Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
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Phosphorylation is a ubiquitous and fundamental regulatory mechanism that controls signal transduction in living cells. It acts as switches in cell communications. One of the most important factors contributing to the dynamics is the binding competition, which refers to the existence of more than one protein that physically binds to the same or an overlapping residue on a protein. By integrating data from different sources on the HeLa cancer cells and all available Homo sapiens (human) cells and iteratively examining the phosphorylation interfaces, we found a number of conflicting interaction pairs. We extended the search into indirect conflicts over direct upstream cascades and further into the whole network and calculated a min-max conflict-sensitive decomposition of phosphorylation network by graph-theoretical methods. Further we used EGF-stimulation phosphoproteome data and obtained activation patterns of phosphorated proteins by soft clustering. By combining these two groupings, we calculated an optimal conflict-free activation patterns using maximum bipartite matching. Sorting the average peak time of the activation patterns brought forth an activation order of min-max conflict-sensitive decomposition subnetworks. We evaluated conflict-sensitive phosophorylation dynamics by analyzing the importance of the conflicting interactions in the whole networks, the distribution of serine/threonine/tyrosine phosphorylation, and the direct or indirect activation order of phosphorylated proteins. Compared with a previously published approach [15], our solution discovered conflict-sensitive dynamics that resolved conflicts and it inferred more practical causal effects consistent with EGFR signaling pathways.