Developing Bioinformatics Tools for Proteomics Research

High throughput proteomics and genomics methods have gained widespread acceptance as ways of monitoring global changes in gene transcription and translation in response to environmental changes. While quantification and identification of mRNA and protein is now routine, software is still required to relate expression patterns to broader biological phenomena. Specifically, given an mRNA or protein expression experiment, can the observed changes be used to make inferences about events upstream and downstream of the point in time that the experiment represents? We propose to develop software that that helps researchers predict what cellular and molecular events may have preceded a given expression profile, and what events may follow, including physiological changes, and pathological and clinical conditions. This will be done by integrating a set of highly-respected, human-curated data sources in biology and medicine, and developing a software inference engine that mines this data resource for associations. Predicted upstream and downstream events will be displayed graphically, in a browsable format wherein the user directs the line of inquiry through the data.