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dc.contributor.advisorGregory Stephanopoulos.en_US
dc.contributor.authorKeibler, Mark Andrewen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Chemical Engineering.en_US
dc.date.accessioned2018-04-27T18:10:09Z
dc.date.available2018-04-27T18:10:09Z
dc.date.copyright2017en_US
dc.date.issued2017en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/115016
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2017.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references.en_US
dc.description.abstractCancer remains a leading worldwide health problem, and in the U.S., 1 in 3 women and 1 in 2 men are expected to develop it during their lifetimes. While the last several decades have provided tremendous understanding of the genetic mutations and signaling pathways that give rise to oncogenic transformation. However, the field of oncology has only relatively recently begun to appreciate the extent of metabolic rewiring required to sustain the uncontrolled growth of tumors. Cancer metabolism is now an active area of research, and efforts are underway to expand the range of oncology diagnostics and therapeutics through targeting metabolism. Despite this progress, there remains much unknown about the extent of metabolic remodeling in cancer cells, including the influence of proliferation, oncogene activation, tumor suppressor loss, tissue of origin, metastatic potential, environmental cues, and many other factors. Further, metabolism comprises a large and complex network of interconnected reactions and transport processes that interface with many other components of cell physiology. In the field of metabolic engineering, there is precedence for using systems-wide approaches to studying metabolism, and these can be applied to cancer cells to unravel the contribution of such factors to the metabolic phenotype. In this thesis, we describe an integrated approach, spanning multiple computational and empirical techniques derived from metabolic engineering, to understand contributors to cancer metabolism. We specifically focused on the influence of proliferation (i.e. cell growth and division) for our analysis. We first review how some of the techniques of metabolic engineering, most notably stable isotope tracers, can be used to study metabolism in cancer cells. Next, using a published account of hybridoma composition, we analyzed the metabolic requirements of proliferation through reducing mammalian cell biomass needs to a small number of precursors and cofactors. We then incorporated this information into a stoichiometric network to understand how metabolic flux became redistributed following either shifts in metabolic objective or the introduction of constraints to simulate respiratory impairment. These investigations revealed the high biosynthetic burdens of amino acids, ATP, and NADPH, but also demonstrated the flexibility through which metabolism can adapt to fulfill these needs. Finally, we established a cell line system to study the differential metabolic effects of proliferation and expression of an oncogenic mutant of KRAS that drives growth factor-independent division. We found that, in the growth-titratable control cell line, proliferation was accompanied by a dramatic switch of branched-chain amino acid catabolism for TCA cycle flux to proteinogenesis. Additionally, we saw that cells undergoing oncogenic KRAS-driven growth possessed lower consumption rates of the major carbon metabolic substrates, glucose and glutamine, than the growth factor-sensitive control line growing at comparable rates. Our multifaceted approach toward understanding the influence of proliferation on metabolism in cancer cells clarifies the biosynthetic requirements for growth and range of metabolic strategies through which they can be satisfied. As well, our comparison of growth vs. oncogenic KRAS-driven metabolism represents, to our knowledge, the first rigorous attempt to deconvolute the proliferation- and oncogene-specific effects on metabolism in a cell line model.en_US
dc.description.statementofresponsibilityby Mark Andrew Keibler.en_US
dc.format.extent260 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectChemical Engineering.en_US
dc.titleAn integrated approach to understanding the metabolic rewiring of cancer cellsen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Chemical Engineering
dc.identifier.oclc1030146367en_US


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