Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01hh63sw108
 Title: FLUX RATIO-BASED ENERGY ESTIMATION: AN INTEGRATIVE INVESTIGATION INTO CELLULAR METABOLISM Authors: Rubin, Sara Ann Advisors: Rabinowitz, Joshua D. Department: Chemistry Class Year: 2014 Abstract: Cellular metabolism is a complex network of thousands of biochemical reactions, mostly enzyme catalyzed. Enzyme usage depends on the concentration of the small molecule intermediates: metabolites, acting not only as substrates and products but also as regulators. Current technological limitations, however, hinder the direct measurement of certain core metabolites, including centrally located glyceraldehyde-3-phosphate and erythrose-4-phosphate. Here, I introduce an alternative approach, Flux Ratio-based Energy Estimation (FREE), which takes advantage of a previously underappreciated thermodynamic relationship to remedy such shortcomings. In contrast to the canonical way of calculating in vivo Gibbs free energy of reaction ( $$\Delta$$ $$_{r}$$G’) from metabolite concentrations and standard Gibbs free energy of reaction ( $$\Delta$$ $$_{r}$$G’$$^{o}$$ ), FREE directly determines ( $$\Delta$$ $$_{r}$$G’) from the ratio of forward to reverse reaction rates (fluxes). With ( $$\Delta$$ $$_{r}$$G’) in hand, unknown metabolite concentrations can be computed from ( $$\Delta$$ $$_{r}$$G’$$^{o}$$ ) and measureable metabolite concentrations. To demonstrate the utility of this approach, FREE was applied in a comparative metabolic study of three organisms: E. coli, S. cerevisiae (baker’s yeast), and immortalized baby mouse kidney cells. Through a joint experimental and computational approach, involving liquid chromatography-mass spectrometry and metabolic flux analysis, in vivo ( $$\Delta$$ $$_{r}$$G’) values were classified into three categories: precisely determinable, highly reversible, and close to irreversible. Combining these results with extensive metabolite quantitation, I was able to populate genome-scale metabolic networks with global in vivo metabolite concentrations and ( $$\Delta$$ $$_{r}$$G’) values. FREE is broadly applicable and paves the way for future integrative analyses which could lead to breakthroughs in biochemical engineering as well as the study of metabolic diseases. Extent: 182 pages URI: http://arks.princeton.edu/ark:/88435/dsp01hh63sw108 Type of Material: Princeton University Senior Theses Language: en_US Appears in Collections: Chemistry, 1926-2016