Browsing by Author "Heinemann, Joshua Vance"

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Fossil viruses, redox paradigms and predictive metabolism from a systems biology perspective
(Montana State University - Bozeman, College of Letters & Science, 2014) Heinemann, Joshua Vance; Chairperson, Graduate Committee: Brian Bothner; Walid S. Maaty, George Gauss, Narahari Akkaladevi, Susan K. Brumfield, Vamseedhar Rayaprolu, Mark Young, C. Martin Lawrence and Brian Bothner were co-authors of the article, 'Fossil record of an HK-97-like provirus' in the journal 'Virology' which is contained within this thesis.; Timothy Hamerly, Walid S. Maaty, Navid Movahed, Joseph D. Steffens, Benjamin D. Reeves, Jonathan K. Hilmer, Jesse Therien, Paul A. Grieco, John W. Peters and Brian Bothner were co-authors of the article, 'Expanding the paradigm of thiol redox in the thermophilic root of life' in the journal 'Biochimica et biophysica acta' which is contained within this thesis.; Aurélien Mazurie, Monika Tokmina-Lukaszewska, Greg J. Beilman and Brian Bothner were co-authors of the article, 'Application of support vector machines to metabolomics experiments with limited replicates' in the journal 'Metabolomics' which is contained within this thesis.; Brigit Noon, Mohammad J. Mohigmi, Aurélien Mazurie, David L. Dickensheets and Brian Bothner were co-authors of the article, 'Real-time digitization of metabolomic patterns from a living system using mass spectrometry' submitted to the journal 'Journal of the American Chemical Society' which is contained within this thesis.
One of the goals of systems biology is to develop a model which encapsulates the molecular, structural and temporal complexity of a living organism. While modern omics experiments can deliver a high resolution view of an organism's molecular complexity, methods for correlating the information from multiple biomolecular systems (i.e. genes, proteins and metabolites) and their changes over time remain greatly underdeveloped. Presented in this research are: (1) methods for understanding the inter-relation of multiple biomolecular systems correlating genomics, proteomics and metabolomics experiments; (2) techniques for machine learning based metabolic biomarker selection; (3) robotics technology for real-time measurement of changes in metabolism. The methods for correlating information from multiple biomolecular systems have provided a new perspective of biomolecular adaptation and evolutionary relationships in the thermophilic archaea. The techniques for biomarker selection have provided a method to assess the reliability of biomarkers in experiments where limited samples are available. The new technology has provided an engineered system for automated analysis of metabolic patterns and how they change over time. Together, these results have created a framework for future improvement of our understanding of biology through the use of molecular biology, machine learning and robotics.