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Systems Biology and Chemometric Analyses of Cellular Chemistry
At the turn of the 20th century started the scientific chase to explain cell function, processes, controls, communication, and their regulation. The parallel improvements in available technologies made tools like nuclear magnetic resonance (NMR), mass spectrometry (MS), and high throughput sample handling available methods to characterize cell composition, communication, and metabolism. Genomics ushered us into an era driven by vast amount of data modeled to explain why cells and thus organism behave the way they do. All OMICs techniques function by extrapolating the central dogma of biology, where genetic material acts upon proteins that then regulate an array of small molecules – metabolites. In concept chemometrics can be used to examine any complex mixture, and when this mixture is of biological origin, we call it metabolomics. This dissertation focuses on three key details that enable better systems biology through chemometrics – (i) expanding the detectable metabolome, (ii) robust and potentially automated metabolite assignment (iii) implementation of intricate metabolomics experiments to explain cellular chemistry. In pursuit of the expansion of the detectable metabolome this dissertation evaluates other NMR active nuclei, namely, 15N and 31P for stable isotope labeled monitoring (SIRM) approaches. Multi-SIRM are evaluated in bacterial cells using a 15N isotope tracer and the 100% naturally abundant 31P isotope. We establish that concurrently acquiring GC-MS and NMR data for a metabolomics experiment leads to confident metabolite annotation. As a consequence, the analyst is rewarded with an increase in observed metabolites and thus improved understanding of the cellular processes. Automated metabolite assignment tool is an unmet need in metabolomics. Inclusion of 2D 1 H13C Heteronuclear MultiBond Coherence (HMBC) experiment with 1D 1 H NMR and a 1 H13C Heteronuclear Single Quantum Coherence experiment (HSQC) will invariably improve metabolite assignments and assist in implementation of automated metabolite assignments by weighted-graph matching. Using some of the existing methods and newer ones discussed this dissertation we explain chemistries in various biological systems, i.e. bacterial infections of Staphylococcus aureus, drug resistant cancers using gemcitabine resistance in pancreatic cancer, and two-cell communication platform using tumor and stromal cells. These examples give a larger picture of the utility of chemometrics and metabolomics in understanding cell chemistry while barely impressing upon the obvious entanglement of metabolic pathways.
Bhinderwala, Fatema, "Systems Biology and Chemometric Analyses of Cellular Chemistry" (2020). ETD collection for University of Nebraska - Lincoln. AAI27830095.