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A major theme in constraint-based modeling is unifying experimental data,such as biochemical information about the reactions that can occur in a systemor the composition and localization of enzyme complexes, with highthroughputdata including expression data, metabolomics, or DNA sequencing. The desiredresult is to increase predictive capability resulting in improved understandingof metabolism. The approach typically employed when only gene (or protein)intensities are available is the creation of tissue-specific models, whichreduces the available reactions in an organism model, and does not provide anobjective function for the estimation of fluxes, which is an importantlimitation in many modeling applications. We develop a method, flux assignmentwith LAD (least absolute deviation) convex objectives and normalization(FALCON), that employs metabolic network reconstructions along with expressiondata to estimate fluxes. In order to use such a method, accurate measures ofenzyme complex abundance are needed, so we first present a new algorithm thataddresses quantification of complex abundance. Our extensions to priortechniques include the capability to work with large models and significantlyimproved run-time performance even for smaller models, an improved analysis ofenzyme complex formation logic, the ability to handle very large enzyme complexrules that may incorporate multiple isoforms, and depending on the modelconstraints, either maintained or significantly improved correlation withexperimentally measured fluxes. FALCON has been implemented in MATLAB and ATS,and can be downloaded from: https://github.com/bbarker/FALCON. ATS is notrequired to compile the software, as intermediate C source code is available,and binaries are provided for Linux x86-64 systems. FALCON requires use of theCOBRA Toolbox, also implemented in MATLAB.

A robust and efficient method for estimating enzyme complex abundance and metabolic flux from expression data