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Utilizing genome-scale models to enhance high-throughput data analysis : : from pathways to dynamics


Technological advances have revolutionized the life sciences. The cost of biological data generation has decreased exponentially in the past decade allowing simultaneous measurement of various biomolecules, which has enabled biologists to study cells as systems of interacting components. However, it is becoming clear that the true bottleneck to biological understanding is not data generation, but data analysis, leaving many data sets incompletely analyzed. To cope with this data deluge, diverse and sophisticated data analysis techniques have been developed and implemented. In this thesis, methodological advances for genome-scale metabolic models are developed and applied to interpret high-throughput data sets to better understand cellular processes at the pathway, network, and dynamic levels. First, an un-biased, algorithmic approach to enumerate biological pathways is presented. The computed pathways are more consistent with the biomolecular interactions of the associated proteins and genes than even classical, human-defined pathways. Further, the computed pathways are utilized to discover novel and non-intuitive transcriptional regulatory interactions in Escherichia coli. Second, mRNA and protein expression omic data are used to build genome-scale metabolic models of the human alveolar macrophage and the murine macrophage-like cell line, RAW 264.7. Model simulations and prospective high-throughput experiments determined metabolism's role in macrophages during infection and inflammation at the network level. Finally, personalized data-driven kinetic models of human erythrocyte metabolism are presented. Tailored to 24 individuals' plasma and intracellular metabolite levels, the kinetic models better represent the underlying genetic makeup of the individuals than the metabolite levels alone. Further, the personalized models were used to predict and interpret side effects on an individual basis

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