January 13, 2023
Journal Article

SBbadger: Biochemical Reaction Networks with Definable Degree Distributions


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 predicted parameters 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. Availability and Implementation SBbadger is implemented in Python and is freely available at https://github.com/sys-bio/SBbadger and via PyPi at https://pypi.org/project/SBbadger/. Documentation can be found at https://SBbadger.readthedocs.io.

Published: January 13, 2023


Kochen M.A., H. Wiley, S. Feng, and H.M. Sauro. 2022. SBbadger: Biochemical Reaction Networks with Definable Degree Distributions. Bioinformatics 38, no. 22:5064–5072. PNNL-SA-177488. doi:10.1093/bioinformatics/btac630