How evolutionary objectives and the intracellular environment shape metabolic fluxes
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Genome-scale flux balance models of metabolism provide testable predictions of all metabolic rates in an organism, by assuming that the cell is optimizing a metabolic goal known as the objective function. In the first chapter of this dissertation, we introduce an efficient inverse flux balance analysis (invFBA) approach, based on linear programming duality, to characterize the space of possible objective functions compatible with measured fluxes. After testing our algorithm on simulated E. coli data and time-dependent S. oneidensis fluxes inferred from gene expression, we apply our inverse approach to flux measurements in long-term evolved E. coli strains, revealing objective functions that provide insight into metabolic adaptation trajectories. For over a hundred years, enzymes, or the proteins that catalyze metabolic reactions, have been characterized in vitro, even though the aqueous solution of a test tube little resembles the crowded intracellular milieu. Since few metabolites show unique fluorescent signatures, metabolism is all but invisible, greatly complicating efforts to describe fluxes in vivo. In the second chapter of this dissertation, we introduce a new technique called EIFFL (Estimation of Intracellular Flux through Fluorescence Loss) for visualizing the flux through a reaction inside single E. coli cells, using a substrate that undergoes an enzyme-catalyzed loss of fluorescence. EIFFL would not only further our quantitative understanding of metabolism, but enable us to promptly detect enzymes that confer clinically meaningful states, such as antibiotic resistance. We present a particular instance of EIFFL that couples nfsA, the major nitroreductase of E. coli responsible for its antibiotic sensitivity to nitrofurantoin, to 2-NBDG, a glucose derivative that loses fluorescence upon being reduced by nfsA with NADPH. We correlate the flux through the reaction with the concentration of a fluorescently tagged nfsA and measure the “flux noise” across a population of E. coli cells. Given that nfsA abolishes 2-NBDG fluorescence by the same molecular mechanism that it activates nitrofurantoin, EIFFL could serve as a means to rapidly infer the antibiotic resistance of single pathogenic E. coli cells directly from clinical samples.