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Item Development of a protein-based sensor assay for rapid classification of complex biological samples(Montana State University - Bozeman, College of Letters & Science, 2016) Hamerly, Timothy Kyle; Chairperson, Graduate Committee: Brian Bothner; Joshua Heinemann, Monika Tokmina-Lukaszewska, Elizabeth R. Lusczek, Kristine E. Mulier, Greg J. Beilman and Brian Bothner were co-authors of the article, 'Bovine serum albumin as a molecular sensor for the discrimmination of complex metabolite samples' in the journal 'Analytica chemica acta' which is contained within this dissertation.; Brian Bothner was a co-author of the article, 'Adding metrics to the aging of whiskey using a protein sensor assay' which is contained within this dissertation.; Brian Bothner was a co-author of the article, 'Analysis of wine using the protein sensor assay' which is contained within this dissertation.; Brian Bothner was a co-author of the article, 'Investigations into the use of a protein sensor assay for metabolite analysis' in the journal 'Applied biochemistry and biotechnology' which is contained within this dissertation.Metabolomics, one of the core 'omics' fields within the umbrella of systems biology, is the study of the small molecules which can be used to characterize the state of an organism. Metabolites are constantly being transformed inside a cell in direct response to stimuli around them. This makes the metabolome the most dynamic of all the omics fields and is considered to be a direct readout of the cells state at any given time. Although highly informative, the metabolome is inherently difficult to study, with thousands of known metabolites, any of which could be important for classifying a cell into a healthy or diseased state. Techniques such as mass spectrometry are well suited to study the metabolome and have been used to successfully classify cells by identify markers for a given disease state. However, current methods require lengthy analysis times due in part to the complexity of the metabolome. The research presented in this dissertation highlights a new and promising methodology which improves classification and speeds marker discovery. Making use of a protein found in animals which has evolved to selectively bind metabolites, an assay was developed which better classified samples compared to current methods used in the field of metabolomics. This improved classification was achieved with an overall decrease in analysis time. The implementation of this method in the study of complex biological systems would have an immediate impact in academic and medical research.