Improving genome-scale metabolic model simulations by measuring exchange fluxes during exponential growth phase

ABSTRACT: With the state-of-the-art techniques currently available, measuring all metabolic reactions in a cell remains impossible. Therefore, systems biology approaches such as genome-scale metabolic models (GEMs) should be used to get a holistic view of metabolism. To reduce the solution space in GEM simulations, models are often constrained with exchange fluxes. However, previous literature that used exchange flux constraints violate the steady-state assumption necessary for GEMs, by inferring the flux from a single metabolomic sample. Here, we present the regression during exponential growth phase (REGP) method, which calculates exchange fluxes only during the log phase, thereby assuring measurements are conducted during metabolic steady-state. The REGP method was tested this method using MCF10A breast epithelial cells. We report that exchange fluxes are generally higher using the REGP method, highlighting the importance of measuring exchange fluxes during steady-state. Even changes in flux direction were observed, which indicates a metabolic shift between the lag phase and the exponential growth phase. While models constrained with previously reported exchange fluxes were infeasible or failed to predict the experimental growth rate, the REGP-constrained GEM accurately predicted the correct growth rate. Flux variability analysis revealed that even with constraints on the exchange of metabolites in central carbon metabolism and amino acid metabolism, these pathways still have intermediate variability in reaction flux. The results of this study suggest that future research should use the REGP method to determine exchange fluxes when these are used as constraints for GEMs, this will provide a better understanding of the complex mechanisms involved in metabolism.

ORGANISM(S): Homo sapiens

PROVIDER: GSE293588 | GEO | 2025/04/07

REPOSITORIES: GEO