spectra were identified, aligned, and quantified by “targeted profiling” algorithms within the software Chenomix NMR Suite 4.5. The list of metabolites discovered in the 2D spectra was used to guide quantification in one dimension. Standards In NMR spectra, MedChemExpress Relebactam absolute concentrations can be obtained from peak integrals if the sample contains an added internal standard of known concentration, or if the concentration of a substance is known by independent means . Scaling factors obtained previously were used to determine absolute concentration of the 10 metabolites included in the model. Normalization and scaling Individual samples within groups were normalized by the sum of all metabolite concentrations in the sample, and then re-scaled by the group average of these concentration totals. Normalization between groups was performed using Bradford assays of the soluble protein content. To minimize the effect of high variability in the Bradford assays, metabolite concentrations for each group of 5 samples were divided by their median protein content. Selected metabolites were scaled empirically using standards in order to account for small variations in the scaling relationships between peak area in the spectrum and metabolite concentration. Principal Component Analysis For the Principal Component Analysis, all metabolites with at least one measurement above 0.01 mM were included in the dataset. Data from all samples were combined into one matrix and principal components were computed using the princomp function in Matlab. Principal component scores for the samples were plotted and visualized within Matlab. The weights of each PC were calculated as the percent each eigenvalue contributes to the sum of all eigenvalues. Flux-balance analysis Metabolic fluxes in control and adapted flies were modeled using flux-balance analysis within a genome-wide reconstruction, as described previously. Metabolite concentrations for the short term hypoxic conditions were converted into sets of fluxes by dividing the differences in mean concentrations by the time period, resulting in units of nmolmg prot-1min-1. Standard errors of the metabolite fluxes were calculated from SE of the concentrations and converted to the same units. A list of 10 “NMR fluxes” was chosen for constraining the flux-balance simulations, based on magnitude of the fluxes and presence of a feasible pathway within the metabolic reconstruction. Virtual “sinks” with unlimited capacity were created for each of these compounds in order to represent metabolite pools, allowing intracellular accumulation and depletion in case substrates and end products did not perfectly balance. The rates of exchange from these “sinks” into/out of the 
system of reactions were forced to the flux rates calculated from the data. The models with flux constraints are provided. Flux-balance analysis was used to simulate system flux distributions during acute hypoxia for each group. The objective function for the system in all simulations was the reaction representing utilization of ATP via hydrolysis. Statistics Flux constraints from the NMR data were applied to the model with their respective error distributions. Pseudorandom sets of fluxes were created by sampling from normal distributions with mean and standard errors equal to those of each NMR flux applied to the model. A set of 100 different pseudo-random flux constraints were generated and PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/19797474 simulated for each experiment. Simulating each of the 100 constraint condit
