Skip to main content

Python Subnet Discovery for Systems Biology

Project description

Build

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

  • 1.0.5 7/19/2025 Fix install issues with missing modules
  • 1.0.2 4/10/2025. ModelSpecification API accepts many kinds of model inputs, Antimony, SBML, roadrunner.
  • 1.0.1 4/09/2025. Improved generation of networks with subnets. Use "mapping_pair" in API. Bug fixes.
  • 1.0.0 2/27/2025. First beta release.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pysubnetsb-1.0.5.tar.gz (105.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

pysubnetsb-1.0.5-py3-none-any.whl (138.9 kB view details)

Uploaded Python 3

File details

Details for the file pysubnetsb-1.0.5.tar.gz.

File metadata

  • Download URL: pysubnetsb-1.0.5.tar.gz
  • Upload date:
  • Size: 105.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.2

File hashes

Hashes for pysubnetsb-1.0.5.tar.gz
Algorithm Hash digest
SHA256 cf687f30fbdf9d0cf05c3e0e3d735ff8d83d874bf4e16ffb1cca8252011f18c8
MD5 103cf8765400b3cf1bc374e77887b149
BLAKE2b-256 a2a9d99658411d3740640639a6208ba0c6b65c81b2d879f5b61c7a5528603c4f

See more details on using hashes here.

File details

Details for the file pysubnetsb-1.0.5-py3-none-any.whl.

File metadata

  • Download URL: pysubnetsb-1.0.5-py3-none-any.whl
  • Upload date:
  • Size: 138.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.2

File hashes

Hashes for pysubnetsb-1.0.5-py3-none-any.whl
Algorithm Hash digest
SHA256 f3fc2186ef18993327ea15228468a7d519c3b83289fcbf74c170e903dde5c8b1
MD5 85b6196415747672fe9d0a515ef5b98f
BLAKE2b-256 ad3eafc030a38cfd0a6a6f74c95cc007d31c29f715c756ac513f8fbcb4a85610

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page