pySubnetSB 0.0.6

Python Subnet Discovery for Systems Biology

SUBNET DISCOVERY FOR SBML MODELS

Motivation

Many advances in biomedical research are driven by structural analysis, a study of the interconnections between elements in biological systems (e.g., identifying drug target and phylogenetic analyses). Structural analysis appeals because structural information is much easier to obtain than dynamical data such as species concentrations and reaction fluxes. Our focus is on subnet discovery in chemical reaction networks (CRNs); that is, discovering a subset of a target CRN that is structurally identical to a reference CRN. Applications of subnet discovery include the discovery of conserved chemical pathways and the elucidation of the structure of complex CRNs. Although there are theoretical results for finding subgraphs, we are unaware of tools for CRN subnet discovery. This is in part due to the special characteristics of CRN graphs, that they are directed, bipartite, hypergraphs.

Results

We introduces pySubnetSB, an open source python package for discovering subnets represented in the systems biology markup language (SBML) community standard. pySubnetSB uses a constraint-based approach to discover subgraphs using techniques that work well for CRNs, and provides considerable speed-up through vectorization and process-based parallelism. We provide a methodology for evaluating the statistical significance of subnet discovery and apply pySubnetSB to discovering subnets in more than 100,000 model pairs in the BioModels repository of curated models.

Availability

pySubnetSB is installed using

pip install pySubnetSB

https://github.com/ModelEngineering/pySubnetSB/blob/main/ examples/api_basics.ipynb is a Jupyter notebook that demonstrates pySubsetSB capabilities.

Version History

• 0.1.0 2/21/2025. First beta release.