Off-campus UNL users: To download campus access dissertations, please use the following link to log into our proxy server with your NU ID and password. When you are done browsing please remember to return to this page and log out.
Non-UNL users: Please talk to your librarian about requesting this dissertation through interlibrary loan.
Chemometric and Bioinformatic Analyses of Cellular Biochemistry
The amount of information collected and analyzed in biochemical and bioanalytical research has exploded over the last few decades, due in large part to the increasing availability of analytical instrumentation that yields information-rich spectra. Datasets from Nuclear Magnetic Resonance (NMR), Mass Spectrometry (MS), infrared (IR) or Raman spectroscopy may easily carry tens to hundreds of thousands of potentially correlated variables observed from only a few samples, making the application of classical statistical methods inappropriate, if not impossible. Drawing useful biochemical conclusions from these unique sources of data requires the use of specialized multivariate data handling techniques. Unfortunately, proper implementation of many new multivariate algorithms requires domain knowledge in mathematics, statistics, digital signal processing, and software engineering in addition to analytical chemical and biochemical expertise. As a consequence, analysts using multivariate statistical methods were routinely required to chain together multiple commercial software packages and fashion small ad hoc software solutions to interpret a single dataset. This has been especially true in the field of NMR metabolomics, where no single software package, free or otherwise, was capable of completing all operations required to transform raw instrumental data into a set of validated, informative multivariate models. Therefore, while many powerful methods exist in published literature to statistically treat and model multivariate spectral data, few are readily available for immediate use by the community as a whole. This dissertation describes the development of an end-to-end software solution for the handling and multivariate statistical modeling of spectroscopic data, called MVAPACK, and a set of novel spectral data acquisition, processing and treatment algorithms whose creation was expedited by MVAPACK. A final foray into the potential existence of n-pi* interactions in proteins is also presented.
Applied Mathematics|Computer science
Worley, Bradley, "Chemometric and Bioinformatic Analyses of Cellular Biochemistry" (2015). ETD collection for University of Nebraska - Lincoln. AAI3732884.