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In Silico Methods for Discovery in Proteomics and Genomics

Images courtesy Peter DiMaggio

The top-half of the figure illustrates the study of proteomics using tandem mass spectrometry. Presented in the blue box are the experimental approaches used to separate (HPLC), ionize (ESI), and fragment (MS/MS) the peptides derived from a protein mixture. The corresponding primary sequence of each peptide can be determined from an experimental tandem mass spectrum (MS/MS) using the de novo or hybrid peptide identification algorithms, PILOT and PILOT_SEQUEL, which are developed within the Floudas research group using integer linear optimization (ILP).

The bottom-half of the figure illustrates several of the applications for optimal methods that we have developed for clustering based on mixed-integer linear optimization (MILP). The bottom-left figure presents the separation of stress conditions and subsequent biclustering of metabolite concentration data, where metabolites of similar known function are shown to group together. The bottom-middle figure illustrates the clustering of protein sequences for de novo protein design in order to assess important homology trends. The bottom-right figure demonstrates the utility of optimal clustering for analyzing drug inhibition data, where desirable target molecules of high percent inhibition were found to cluster in the upper right-hand corner of the data matrix.

For more infomation about Professor Floudas' research please see the Computer-Aided Systems Laboratory site.