Nagrath, Deepak2017-07-312018-12-012016-122016-12-02December 2Achreja, Abhinav. "Mapping tumor metabolism: from personalized multiobjective metabolic flux analysis to intercellular metabolite transport via exosomes." (2016) Diss., Rice University. <a href="https://hdl.handle.net/1911/95542">https://hdl.handle.net/1911/95542</a>.https://hdl.handle.net/1911/95542Departure from healthy and tightly-regulated metabolism is an emerging hallmark of cancer that facilitates tumorigenic characteristics of uncontrolled proliferation, metastasis, and resistance to chemotherapy. Our aim is to elucidate underlying mechanisms of metabolic shifts that satisfy enhanced energetic and biochemical demands of cancer cells. This is essential to develop therapies that sensitize resistant tumors to front-line treatments and alleviate side-effects or attack novel targets that are robust to acquired resistance. Study of metabolic reprogramming involves a marriage of empirical techniques that measure biophysical parameters and computational algorithms to estimate useful but unmeasurable parameters of metabolic activity, i.e. fluxes. This thesis describes novel computational algorithms designed to quantify metabolic fluxes vis a vis metabolic reprogramming in cancer cells and metabolic interaction within tumor microenvironment (TME). The first part describes reconstruction of metabolic models using transcriptomic and proteomic data from cancer cell-lines to address the need for personalized medicine. The second part discusses the implementation of currently used 13-carbon metabolic flux analysis (13C-MFA) to demonstrate how cancer-associated fibroblasts (CAFs) in TME, reprogram metabolism to utilize nutrients atypically, to synthesize glutamine for glutamine deprived cancer cells. In the third part, the 13C multiobjective MFA (13C-MOMFA) algorithm is presented that removes the restrictions of 13C-MFA to simple models and abundant empirical data. The empirical data utilized is same as conventional techniques, but with a multiobjective optimization approach to capture competing metabolic objectives facilitating various tumorigenic functions in larger “personalized” models. 13C-MOMFA is applied in ovarian cancer cell-lines subjected to glutamine catabolism inhibition, to uncover mechanisms linking invasiveness to glutamine-dependence. Finally, exosome-mediated MFA (Exo-MFA) technique is designed to elucidate tumor growth-supporting transport of metabolites from tumor stroma to nutrient-deprived cancer cells via secreted exosomes. Exo-MFA is the first to capture multicellular metabolic interaction between TME components. Results demonstrate packaging of nutrients into exosomes by CAFs, and sufficient supply of metabolites to central carbon metabolism of nutrient-deprived cancer cells. This thesis addresses two burgeoning fields of cancer biology focused on improving therapeutic outcomes – (i) precision medicine that recognizes patient-heterogeneity and complex metabolic programs that support tumorigenic phenotypes, and (ii) systems approach to understanding metabolism of whole tumors.application/pdfengCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.Cancer metabolism13C Metabolic Flux AnalysisMultiobjective Metabolic Flux AnalysisMulticellular Flux AnalysisTumor MetabolismExosomesThesis2017-07-31