FLUX RATIO-BASED ENERGY ESTIMATION: AN INTEGRATIVE INVESTIGATION INTO CELLULAR METABOLISM

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.