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Genome-Scale Reconstruction and Analysis of Eukaryotic Metabolic Networks

  • Author(s): Hurlen, Natalie Christine
  • Advisor(s): Palsson, Bernhard O
  • et al.
Abstract

Cells are comprised of complex, highly integrated networks of genes, proteins, and chemical compounds that interact with one another to achieve biological functions. A goal of systems biology is to develop comprehensive reconstructions of these networks in order to study their emergent properties. With the growing availability of whole genome sequences, cellular 'part lists' can now be defined for many organisms. The procedure for assembling genome-scale microbial networks is well established. However, such efforts have been limited for eukaryotes, especially in multicellular species. Thus, the overall goal of this Dissertation was to advance the reconstruction and analysis of eukaryotic systems by developing genome-scale metabolic models of Saccharomyces cerevisiae and a generic human cell.

We first describe the reconstruction of S. cerevisiae iND750, a fully compartmentalized metabolic network that includes systemic gene-protein relationships, pH-specific metabolite formula and charge, and elementally and charge-balanced reactions. iND750 was manually assembled with component-by-component (i.e., bottom-up) approach and then functionally validated by comparing its predictions of 4,200 gene deletion phenotypes to in vitro data.

Next we discuss the human reconstruction project, which required a combination of top-down and bottom-up approaches to construct a comprehensive, high quality network within a reasonable time frame. This entailed automated extraction of a candidate component list from the genome annotation and parallelized, manual curation by a team of researchers. The resultant network, named Homo sapiens Recon 1, collectively represents 1,497 genes, 2,005 proteins, and 3,311 reactions found in a variety of human cell types, and is the largest genome-scale reconstruction to date.

Finally, we demonstrate the applications of these networks as mathematical models and as a context for high-throughput data analysis. In silico and in vitro growth experiments revealed that yeast exhibits few optimal phenotypes over a range of glucose and oxygen uptake rates, and that there are distinct combinations of these rates that yield maximal biomass and ethanol production. Qualitative assessment of gene expression levels in obese skeletal muscle highlighted consistencies between metabolic states post-gastric bypass and under caloric restriction. Pathway analysis of gene expression data also provided to initial steps towards generating tissue-specific metabolic reconstructions.

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