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Dr. Keller is interested in modeling biological systems to gain insight. While studying the regulation of gene transcription, he modeled the behavior of synthetic genetic circuits to identify those with multiple stable states of gene expression. A current aim is to apply similar models to biological networks inferred from diverse types of data. Dr. Keller has also been involved in developing algorithms to facilitate high throughput shotgun proteomics. He employed machine learning and statistical methods to identify confident peptide assignments to tandem mass spectra, and from those assignments, derive likely sample proteins.
Trans-Proteomic Pipeline, including PeptideProphet and ProteinProphet software
Seattle Proteome Center