SBbadger: biochemical reaction networks with definable degree distributions

Abstract

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 sample 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. Supplementary information Supplementary data are available at Bioinformatics online.

Generating synthetic signaling networks for in silico modeling studies

“…There are two existing synthetic random reaction network generating tools, i.e., SMGen [32] and SBbadger [33]. They focus on different perspectives, however, none of the two tools considered the phosphorylation and dephosphorylation cycles which are the critical units in signaling networks.…”

“…As a comparison with the other two existing tools, SM-Gen [32] and SBbadger [33], our method considered the critical units of phosphorylation and dephosphorylation cycles which are critical for signaling networks. Additionally, users could define both species and reaction counts within our Julia script.…”

“…In addition, our artificial random signaling networks could generate comparable steady states and oscillations, which were also commonly observed in the signaling networks from the BioModels Database. As a comparison with the other two existing tools, SMGen [21] and SBbadger [22], our method considered the critical units of phosphorylation and dephosphorylation cycles for signaling networks. Additionally, users could define both species and reaction counts within our Julia script.…”