Research on plant metabolism is currently experiencing the common use of various omics methods creating valuable information on the concentrations of the cell's constituents. However, little is known about in vivo reaction rates, which can be determined by Metabolic Flux Analysis (MFA), a combination of isotope labeling experiments and computer modeling of the metabolic network. Large-scale applications of this method so far have been hampered by tedious procedures of tissue culture, analytics, modeling and simulation. By streamlining the workflow of MFA, the throughput of the method could be significantly increased. We propose strategies for these improvements on various sub-steps which will move flux analysis to the medium-throughput range and closer to established methods such as metabolite profiling. Furthermore, this may enable novel applications of MFA, for example screening plant populations for traits related to the flux phenotype.