  Motivation: An essential step in developing computational tools for the
inference, optimization, and simulation of biochemical reaction networks is
gauging tool performance against earlier efforts using an appropriate set of
benchmarks. General strategies for the assembly of benchmark models include
collection from the literature, creation via subnetwork extraction and de novo
generation. However, with respect to biochemical reaction networks, these
approaches and their associated tools are either poorly suited to generate
models that reflect the wide range of properties found in natural biochemical
networks or to do so in numbers that enable rigorous statistical analysis.
Results: In this work we present SBbadger, a python-based software tool for the
generation of synthetic biochemical reaction or metabolic networks with
user-defined degree distributions, multiple available kinetic formalisms, and a
host of other definable properties. SBbadger thus enables the creation of
benchmark model sets that reflect properties of biological systems and generate
the kinetics and model structures typically targeted by computational analysis
and inference software. Here we detail the computational and algorithmic
workflow of SBbadger, demonstrate its performance under various settings,
provide samples outputs, and compare it to currently available biochemical
reaction network generation software.
