Metabolic engineering endeavors seek to develop microorganisms as feedstocks for biofuels and commodity chemicals. Towards this, quantifying metabolic fluxes is an important step for characterizing an organism’s metabolism and designing effective engineering strategies. Metabolic fluxes are quantified using sophisticated techniques, namely flux balance analysis (FBA), an in silico technique, and isotope-assisted metabolic flux analysis (MFA), a hybrid experimental and computational technique. FBA uses a network’s stoichiometry with linear programming techniques to generate in silico flux predictions for genome-scale networks. MFA uses measurements from stable isotope (typically 13C) tracer experiments to estimate fluxes of central carbon metabolism. In MFA, fluxes are parameters to a model developed from the network’s carbon atom rearrangements, which is fit to isotope labeling data, typically acquired using mass spectrometry.We developed novel mathematical and computational techniques for quantifying and analyzing flux predictions obtained using MFA and FBA. FBA applications typically generate flux predictions for networks with on the order of 1000 [O(1000)] reactions and metabolites. We developed a network reduction algorithm that uses matrix algebra to reduce a large network and flux prediction to a smaller representation. From this reduced representation, a researcher may quickly gain holistic insights from the FBA model. In isotopically nonstationary MFA, time-series labeling measurements are acquired on the approach to steady state. A model consisting of a large system of typically O(1000) ordinary differential equations is fit to the measurements to estimate fluxes and pool sizes. For detailed networks, the number of parameters may be large. We developed a computationally effective framework for solving this problem having robust convergence and efficient scalability to large networks. In this approach, we formulate the problem as an equality-constrained nonlinear program (NLP), solved efficiently using a solver implemented on an algebraic modeling language. Finally, we apply this approach to a detailed model of Phaeodactylum tricornutum photoautotrophic and mixotrophic (on acetate) metabolism. Using the flux estimates, we characterized this organism’s metabolism under disparate growth conditions, which may inform future endeavors to engineer P. tricornutum as a chemical feedstock.