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