Interpretation and Integration of 13C-Fluxomics Data

Abstract

al. and Young et al. have achieved good results by using LC-MS or LC-MS/MS to detect the state of 13C labeling [11,12].Based on their work and the increasingly wide use of 13C metabolic flux methods in recent years, more and more metabolic flux data have been accumulated, with fluxomics itself emerging as a new way to describe cell systems. Thus, fluxomics has gradually matured into a new -omics technology that can be mentioned in the same breath with proteomics and metabolomics. What follows then is that the interpretation of metabolic fluxomics data is becoming more and more important. The interpretation of fluxomics data currently relies predominantly on Genome-Scale Metabolic Network Models (GSMR) [13]. Burgard et al., relying on GSMR and a Flux Balance Analysis framework, developed a method of predicting the biological significance of specific metabolic flux [14]. The method uses bi-level linear programming and can identify an objective function that leads to a specific metabolic flux distribution. Cell growth under either aerobic or anaerobic conditions has the same metabolic objective, but different metabolic fluxes due to the differences in input conditions. Schuetz et al. projected metabolic flux data from the Sauer laboratory onto a threedimensional flux space, and using the multi-objective optimization method, they found that these flux data were distributed on the Paretofront produced by several different objective functions, indicating that the evolution of the flux state was achieved by a shift among different objectives [15]. By combining metabolic flux data, GSMR and multiobjective optimization, we found that, in the case of oxidative stress, the central carbon metabolism of E. coli could transit from the normal state into a suboptimal state, resulting in more NADPH to fight against oxygen free radicals [16].Corresponding with the interpretation of fluxomics data is the integration of fluxomics data with other -omics data to provide greater biological knowledge. Progress in this area has been slow in recent years or to put it in another way, has not yet attracted enough attention from relevant researchers. However, the number of metabolic fluxomic data