SBbadger: Biochemical Reaction Networks with Definable Degree Distributions

TL;DR

SBbadger is a Python tool that generates synthetic biochemical networks with customizable properties, aiding in benchmarking and testing computational tools for biological network analysis.

Contribution

It introduces SBbadger, enabling the creation of diverse, property-specific biochemical networks for benchmarking, which was difficult with previous methods.

Findings

SBbadger can generate networks with user-defined degree distributions.

The tool supports multiple kinetic formalisms.

Performance comparisons show SBbadger's advantages over existing software.

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 samples outputs, and compare it to currently available biochemical reaction network generation software.