SBbadger: biochemical reaction networks with definable degree distributions

Kochen, Michael A.; Wiley, H. Steven; Feng, Song; Sauro, Herbert M.; Valencia, ed., Alfonso

doi:10.1093/bioinformatics/btac630

SBbadger: biochemical reaction networks with definable degree distributions

Journal Article · 16 September 2022 · Bioinformatics

DOI:https://doi.org/10.1093/bioinformatics/btac630· OSTI ID:1898211

; Wiley, H. Steven; Feng, Song; ; Valencia, ed., Alfonso

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.

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Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: National Cancer Institute (NCI); USDOE

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 1898211

Alternate ID(s):: OSTI ID: 1903700

Report Number(s):: PNNL-SA-177488

Journal Information:: Bioinformatics, Journal Name: Bioinformatics Journal Issue: 22 Vol. 38; ISSN 1367-4803

Publisher:: Oxford University PressCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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